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## **Meet the editor**

Dr Thomas Heinbockel, Ph.D., is currently Associate Professor, Director of Graduate Studies, and Interim Chairman in the Department of Anatomy, Howard University College of Medicine, Washington, DC, USA. Dr Heinbockel studied biology at the Philipps-University, Marburg, Germany. His studies of the brain started during his M.S. thesis work at the Max-Planck-Institute for Behavior-

al Physiology, Starnberg/Seewiesen, Germany. Subsequently, he completed a Ph.D. in Neuroscience at the University of Arizona, Tucson, Arizona, USA. After graduating, he held a Research Associate position at the Institute of Physiology, Otto-von-Guericke-University School of Medicine, Magdeburg, Germany. Prior to his arrival at Howard University, Dr Heinbockel held joint research faculty appointments in the Department of Anatomy & Neurobiology and the Department of Physiology at the University of Maryland School of Medicine, Baltimore, Maryland, USA.

Contents

**Preface VII**

**Section 1 Cellular and Computational Neuroscience 1**

Chapter 2 **Electrophysiological Recording and**

**Section 2 Cognitive and Social Neuroscience 49**

**Section 3 Clinical and Cognitive Neuroscience 67**

François Osiurak and Didier Le Gall

Chapter 5 **Neurocognitive Deficits in Patients with**

Chapter 6 **Cognitive Disturbances in Sneddon**

Jan Rostowski and Teresa Rostowska

Thomas Heinbockel

Chapter 4 **Apraxia: Clinical Types,**

Oh Dae Kwon

Chapter 1 **Is the Action Potential Waveform Constant? 3**  Besarion Partsvania and Tamaz Sulaberidze

**Imaging of Neuronal Signals in Brain Slices 19**

Chapter 3 **Neuropsychological Context of Marital Functioning 51**

**Theoretical Models, and Evaluation 69**

**and Antiphospholipid Syndromes 115**

Chapter 7 **Cognitive Features of Vascular Dementia 127**

**Obstructive Sleep Apnea Syndrome (OSAS) 93**

Georgia Andreou, Filippos Vlachos and Konstantinos Makanikas

M. Rahmani, M. El Alaoui Faris, M. Benabdeljlil and S. Aidi

### Contents

#### **Preface XI**

Preface

nervous system.

If one asks what neuroscience is, the answer can be found in this book. Neuroscience embraces not only anatomical and physiological studies but also cell biology, computer science, and biochemistry. Equally important for neuroscientific research are other disciplines, such as psychology, psychiatry, neurology and additional recent ones, such as neuroeconomics and social neuroscience. This book comprises chapters on diverse topics in neuroscience ranging from cellular, computational, cognitive, and clinical neuroscience. Individual chapters focus on recent advances in specific areas including social neuroscience, which is a relatively new field that studies the neural basis of social interactions. Other chapters focus on technological developments such as optical tools to study the function of the brain. All chapters represent recent contributions to the rapidly developing field of neuroscience and illustrate the range of research conducted under the umbrella of the truly interdisciplinary neurosciences. Therefore, this book epitomizes brain research broadly at this particular moment in the evolution of the field. By covering different interdisciplinary topics, this book aims at helping to overcome the department and discipline silos that hold back our research progress in favor of collaborative research teams that engage in successful collaborations to ask questions about the structure and function of the brain and

The book is organized into three somewhat overlapping sections: I. Cellular & Computational Neuroscience, II. Cognitive & Social Neuroscience, and III. Clinical & Cognitive Neuroscience. These sections reflect a diverse range of topics covered in the

The book begins with a chapter about basic mechanisms of information processing in the brain employed by its constituents, the neurons. Neurons are complex operating devices that use action potentials to encode information. Action potentials are not as static as previously thought and, therefore, Partsvania and Sulaberidze (chapter 1) ask

In chapter 2, 'Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices', Heinbockel describes experiments that test the effects of two pivotal neurotransmitter systems and their relevance for neural network activity: metabotropic glutamate receptors and cannabinoid receptors. Neurons release

book and at the same time represent major topics in modern neuroscience.

the question 'Is the action potential waveform constant?'

### Preface

If one asks what neuroscience is, the answer can be found in this book. Neuroscience embraces not only anatomical and physiological studies but also cell biology, computer science, and biochemistry. Equally important for neuroscientific research are other disciplines, such as psychology, psychiatry, neurology and additional recent ones, such as neuroeconomics and social neuroscience. This book comprises chapters on diverse topics in neuroscience ranging from cellular, computational, cognitive, and clinical neuroscience. Individual chapters focus on recent advances in specific areas including social neuroscience, which is a relatively new field that studies the neural basis of social interactions. Other chapters focus on technological developments such as optical tools to study the function of the brain. All chapters represent recent contributions to the rapidly developing field of neuroscience and illustrate the range of research conducted under the umbrella of the truly interdisciplinary neurosciences. Therefore, this book epitomizes brain research broadly at this particular moment in the evolution of the field. By covering different interdisciplinary topics, this book aims at helping to overcome the department and discipline silos that hold back our research progress in favor of collaborative research teams that engage in successful collaborations to ask questions about the structure and function of the brain and nervous system.

The book is organized into three somewhat overlapping sections: I. Cellular & Computational Neuroscience, II. Cognitive & Social Neuroscience, and III. Clinical & Cognitive Neuroscience. These sections reflect a diverse range of topics covered in the book and at the same time represent major topics in modern neuroscience.

The book begins with a chapter about basic mechanisms of information processing in the brain employed by its constituents, the neurons. Neurons are complex operating devices that use action potentials to encode information. Action potentials are not as static as previously thought and, therefore, Partsvania and Sulaberidze (chapter 1) ask the question 'Is the action potential waveform constant?'

In chapter 2, 'Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices', Heinbockel describes experiments that test the effects of two pivotal neurotransmitter systems and their relevance for neural network activity: metabotropic glutamate receptors and cannabinoid receptors. Neurons release

#### XII Preface

glutamate as a well-known neurotransmitter and they also release endogenously produced cannabinoids (endocannabinoids) as a retrograde signaling molecule. The effects of endocannabinoids on neurons are mimicked by their exogenous counterpart (tetrahydrocannabinol) found in marijuana (cannabis). Various experimental techniques come into play in this chapter ranging from electrophysiological techniques (patch-clamp recording in brain slices), gene knockout technology (transgenic mice), optical tools (laser flash photolysis, confocal microscopy), and neural staining techniques.

Preface XI

**Thomas Heinbockel, Ph.D.**

Department of Anatomy,

USA

Associate Professor and Interim Chairman,

Howard University College of Medicine, Washington, DC,

Having completed this book, I am grateful to InTech – Open Access Publisher for initiating this book project and for asking me to serve as its editor. Many thanks go to Ms Ivona Lovric at InTech for guiding me through the publication process and for moving the book ahead in a timely fashion. Thanks are due to all contributors of this book for taking the time to first write a chapter proposal, compose their chapter and, lastly, make requested revisions to it. Hopefully, all contributors will continue their neuroscience research with many intellectual challenges and exciting new directions. I would like to thank my wife Dr. Vonnie D.C. Shields, Professor, Towson University, Towson, MD and our son Torben Heinbockel for allowing me to spend time on this book project during the past year. Finally, I am grateful to my parents Erich and

Renate Heinbockel for their support over many years.

In chapter 3, 'Neuropsychological context of marital functioning', Rostowski and Rostowska address the relationship of brain function and social behavior, specifically marriage. The chapter provides a refreshing look at 'Interpersonal Neurobiology', emphasizing the relevance of the connection of interpersonal relationships, emotions and the brain. The authors elaborate on the system of mirror neurons that influence close social behaviors.

In chapter 4, 'Apraxia: clinical types, theoretical models, and evaluation', Osiurak and Le Gall provide a framework that allows the assessment of disorders which are generally viewed as apraxia. Traditionally, apraxia has been defined as a disorder of skilled movement without attributing it to elementary sensorimotor deficit, aphasia or severe mental deterioration. The authors take the reader to the discovery of apraxia and all the way back to recent proposals regarding its classification and evaluation.

In chapter 5, 'Neurocognitive deficits in patients with Obstructive Sleep Apnea Syndrome (OSAS)', Andreou, Vlachos and Makanikas address a problem in cognitive and clinical neuroscience, namely, Obstructive Sleep Apnea Syndrome (OSAS). The authors describe this sleep disorder and its relation to neurological and cognitive deficits. The authors also review the literature on possible treatment options as well as the neuroanatomical substrates and pathophysiological manifestations of OSAS.

In chapter 6, 'Cognitive disturbances in Sneddon and Antiphospholipid Syndromes',

Rahmani, El Alaoui Faris, Benabdeljlil, and Aidi report on another topic in cognitive and clinical neuroscience, namely, the relationship of a specific type of skin disease (generalised livedo racemosa of the skin) and recurrent stroke. The authors discuss the question whether Sneddon's syndrome overlaps with primary antiphospholipid antibody syndrome (APS) and systematic lupus erythromatosis (SLE).

In chapter 7, 'Cognitive features of vascular dementia', Kwon reviews the relationship of cerebrovascular disease and dementia. The author raises a critical issue, namely, the early accurate diagnosis which is so crucial to the effective management of patients with vascular dementia. Kwon states that recognition of a specific pattern of neuropsychological impairment could be a good diagnostic tool of vascular dementia. Different forms of vascular dementia exist, each with a specific time course of the clinical manifestation of dementia.

Having completed this book, I am grateful to InTech – Open Access Publisher for initiating this book project and for asking me to serve as its editor. Many thanks go to Ms Ivona Lovric at InTech for guiding me through the publication process and for moving the book ahead in a timely fashion. Thanks are due to all contributors of this book for taking the time to first write a chapter proposal, compose their chapter and, lastly, make requested revisions to it. Hopefully, all contributors will continue their neuroscience research with many intellectual challenges and exciting new directions. I would like to thank my wife Dr. Vonnie D.C. Shields, Professor, Towson University, Towson, MD and our son Torben Heinbockel for allowing me to spend time on this book project during the past year. Finally, I am grateful to my parents Erich and Renate Heinbockel for their support over many years.

X Preface

techniques.

close social behaviors.

glutamate as a well-known neurotransmitter and they also release endogenously produced cannabinoids (endocannabinoids) as a retrograde signaling molecule. The effects of endocannabinoids on neurons are mimicked by their exogenous counterpart (tetrahydrocannabinol) found in marijuana (cannabis). Various experimental techniques come into play in this chapter ranging from electrophysiological techniques (patch-clamp recording in brain slices), gene knockout technology (transgenic mice), optical tools (laser flash photolysis, confocal microscopy), and neural staining

In chapter 3, 'Neuropsychological context of marital functioning', Rostowski and Rostowska address the relationship of brain function and social behavior, specifically marriage. The chapter provides a refreshing look at 'Interpersonal Neurobiology', emphasizing the relevance of the connection of interpersonal relationships, emotions and the brain. The authors elaborate on the system of mirror neurons that influence

In chapter 4, 'Apraxia: clinical types, theoretical models, and evaluation', Osiurak and Le Gall provide a framework that allows the assessment of disorders which are generally viewed as apraxia. Traditionally, apraxia has been defined as a disorder of skilled movement without attributing it to elementary sensorimotor deficit, aphasia or severe mental deterioration. The authors take the reader to the discovery of apraxia and all the way back to recent proposals regarding its classification and evaluation.

In chapter 5, 'Neurocognitive deficits in patients with Obstructive Sleep Apnea Syndrome (OSAS)', Andreou, Vlachos and Makanikas address a problem in cognitive and clinical neuroscience, namely, Obstructive Sleep Apnea Syndrome (OSAS). The authors describe this sleep disorder and its relation to neurological and cognitive deficits. The authors also review the literature on possible treatment options as well as the neuroanatomical substrates and pathophysiological manifestations of OSAS.

In chapter 6, 'Cognitive disturbances in Sneddon and Antiphospholipid Syndromes',

Rahmani, El Alaoui Faris, Benabdeljlil, and Aidi report on another topic in cognitive and clinical neuroscience, namely, the relationship of a specific type of skin disease (generalised livedo racemosa of the skin) and recurrent stroke. The authors discuss the question whether Sneddon's syndrome overlaps with primary antiphospholipid

In chapter 7, 'Cognitive features of vascular dementia', Kwon reviews the relationship of cerebrovascular disease and dementia. The author raises a critical issue, namely, the early accurate diagnosis which is so crucial to the effective management of patients with vascular dementia. Kwon states that recognition of a specific pattern of neuropsychological impairment could be a good diagnostic tool of vascular dementia. Different forms of vascular dementia exist, each with a specific time course of the

antibody syndrome (APS) and systematic lupus erythromatosis (SLE).

clinical manifestation of dementia.

#### **Thomas Heinbockel, Ph.D.**

Associate Professor and Interim Chairman, Department of Anatomy, Howard University College of Medicine, Washington, DC, USA

**Section 1**

**Cellular and Computational Neuroscience**

## **Section 1**

## **Cellular and Computational Neuroscience**

**1**

*Georgia*

**Is the Action Potential Waveform Constant?**

*Vladimer Chavchanidze Institute of Cybernetics of the Georgian Technical University,*

Neurons perform multiple operations. These operations include the receiving of information, processing, coding and transmitting it to other neurons. These involve synapses, membrane ionic channels and changes in membrane potential. The operations are thought of as steps in an algorithm or as computations. The concept of a neuron as a simple integrator unit is antiquated. A neuron could be regarded as complex processor with mixed analogue-digital logic and highly adaptive synaptic elements. Individual nerve cells convert the incoming streams of binary pulses into analogue, spatially distributed variables, such as postsynaptic membrane potential and calcium distribution throughout the dendritic tree and cell body. A number of transformations are applied to these variables which might be understood as subtraction and addition, low- and band-pass filtering, normalization, etc.

"The mammalian brain contains more than 1010 densely packed neurons that are connected to an intricate network. In every small volume of the cortex, thousands of spikes are emitted each millisecond. For a long time it has been thought that most of the relevant information was contained in the mean firing rate of the neuron. The firing rate was defined by a temporal average. The concept of mean firing rates has been successfully applied during the last 80 years. It is clear, however, that an approach based on a temporal average neglects all of the information that might be contained in the exact timing of the spikes. From behavioural experiments, it is known that reaction times are often short. Temporal averaging can work well in cases where the stimulus is constant or slowly varying and does not require a fast reaction from the organism. Real inputs change on a fast time-scale. A fly can react to new stimuli and change the direction of flight within 30-40 ms; it has to respond after a postsynaptic neuron has received one or two spikes. Humans can recognize visual scenes in just a few hundred milliseconds. This does not leave enough time to perform temporal averages on each level. In fact, humans can detect images in a sequence of unrelated pictures even if each image is shown for only 14-100 milliseconds. In some cases, a neuron might be driven by an external stimulus which is suddenly switched. For example, when we look at a picture, our gaze jumps from one point to the next. After each saccade, the photo receptors in the retina receive a new visual input. Information about the onset of a saccade would easily be available in the brain and it could serve as an internal reference signal. In such a scenario, it is possible that there exists a code where, for each neuron, the timing of the *first* spike after the reference signal contains all of the information about the

**1. Introduction**

(Koch, 1999).

Besarion Partsvania and Tamaz Sulaberidze

## **Is the Action Potential Waveform Constant?**

Besarion Partsvania and Tamaz Sulaberidze

*Vladimer Chavchanidze Institute of Cybernetics of the Georgian Technical University, Georgia*

#### **1. Introduction**

Neurons perform multiple operations. These operations include the receiving of information, processing, coding and transmitting it to other neurons. These involve synapses, membrane ionic channels and changes in membrane potential. The operations are thought of as steps in an algorithm or as computations. The concept of a neuron as a simple integrator unit is antiquated. A neuron could be regarded as complex processor with mixed analogue-digital logic and highly adaptive synaptic elements. Individual nerve cells convert the incoming streams of binary pulses into analogue, spatially distributed variables, such as postsynaptic membrane potential and calcium distribution throughout the dendritic tree and cell body. A number of transformations are applied to these variables which might be understood as subtraction and addition, low- and band-pass filtering, normalization, etc. (Koch, 1999).

"The mammalian brain contains more than 1010 densely packed neurons that are connected to an intricate network. In every small volume of the cortex, thousands of spikes are emitted each millisecond. For a long time it has been thought that most of the relevant information was contained in the mean firing rate of the neuron. The firing rate was defined by a temporal average. The concept of mean firing rates has been successfully applied during the last 80 years. It is clear, however, that an approach based on a temporal average neglects all of the information that might be contained in the exact timing of the spikes. From behavioural experiments, it is known that reaction times are often short. Temporal averaging can work well in cases where the stimulus is constant or slowly varying and does not require a fast reaction from the organism. Real inputs change on a fast time-scale. A fly can react to new stimuli and change the direction of flight within 30-40 ms; it has to respond after a postsynaptic neuron has received one or two spikes. Humans can recognize visual scenes in just a few hundred milliseconds. This does not leave enough time to perform temporal averages on each level. In fact, humans can detect images in a sequence of unrelated pictures even if each image is shown for only 14-100 milliseconds. In some cases, a neuron might be driven by an external stimulus which is suddenly switched. For example, when we look at a picture, our gaze jumps from one point to the next. After each saccade, the photo receptors in the retina receive a new visual input. Information about the onset of a saccade would easily be available in the brain and it could serve as an internal reference signal. In such a scenario, it is possible that there exists a code where, for each neuron, the timing of the *first* spike after the reference signal contains all of the information about the

Is the Action Potential Waveform Constant? 5

previous conductance. The authors named this phenomenon the "AP waveform code". It is proposed that the code influences postsynaptic targets in different ways. One of these ways sees APs of different shapes interacting with incoming excitatory postsynaptic potentials (EPSPs) and influencing synaptic integration differently. Another way could be that the variability of AP waveforms might be saved in the synaptic terminal until a different calcium influx translates these variables into different EPSP sizes in the

Shu et al. (2006) have shown that the somatic AP waveforms survive axonal conduction. It was also shown that the differences in the somatic AP waveforms are further augmented by axonal conduction. This result was obtained using simultaneous somatic and axonal recordings. Furthermore, the EPSPs that they recorded were larger when the presynaptic

Häusser et al. (2001) have shown that because the cortical APs of different waveforms differentially shunt incoming synaptic events, they participate in the synaptic integration and the generation of succeeding APs. Therefore, at least 50 ms of the stimulus history can affect the state of the synaptic integration of the neuron and the generation of spikes, through the modulation of the waveform. This is a form of local encoding at the singleneuron level with a 50-ms-long memory, which affects neuronal communication by

In summary, for a long time it was thought that the waveform of the AP was constant. However, new evidence continues to show the opposite: it has become clear that the

The question arises: Is it possible that the AP waveform contains information about habituation when a single neuron is stimulated with intracellular current impulses and

The most desirable experiment for answering this question might consider conductance injection into the single neuron. However, there are major limitations to conductance injection. It has to be fast and temporally consistent. For this reason, we decided to stimulate a single neuron with the injection of short current impulses and investigate habituation to the stimulation. We investigated the possible relation of changes of the AP's parameters

The isolated nervous system of the mollusc *Helix pomatia* was used in the experiments. Each snail was anesthetized by the injection of isotonic MgCl2, described elsewhere (Patsvania et al., 2008). Then the nervous system was separated from the body. Ganglia were treated with 0.5% Pronaze solution (Protease from Streptomyces griseus - "Sigma-Aldrich") for 30 minutes at room temperature. After proteolytic treatment, the conjunctive tissue was carefully removed using fine micro scissors. Then, the ganglia were washed several times with Ringer solution. This solution consists of: NaCl - 80 mmol, KCl - 4 mmol, CaCl2 - 35 mmol, MgCl2 .6 H2O - 5 mmol, Tris -7 mmol at pH=7.5. The nervous system was placed in a Petri dish and positioned in a Faraday cage to filter out any environmental electromagnetic noise. The identified giant

(and consequently the AP waveform) with the process of habituation.

postsynaptic cell.

somatic and axonal spikes were broader.

influencing the production of future spikes.

waveform of an AP carries information.

habituates to the stimulation?

**2. Materials and methods**

new stimulus. In such cases, each neuron transmits exactly one spike per stimulus and it is clear that only the timing conveys information rather than the number of spikes. In the hippocampus, in the olfactory system, and also in other areas of the brain, oscillations of some global variable (e.g., the population activity) are quite common. These oscillations could serve as an internal reference signal. Neuronal spike trains could then encode information in the phase of a pulse with respect to the background oscillation. There is, for example, evidence that the phase of a spike during an oscillation in the hippocampus of rats conveys information on the spatial location of the animal which is not fully accounted for by the firing rate of the neuron" (Gerstner & Kistler, 2002).

The next step in information coding was "rate codes" (Tateno & Robinson, 2006). In fact, there are three different notions of rate, the three definitions of which refer to three different averaging procedures: either an average over time, an average over several repetitions of the experiment, or an average over a population of neurons.

In the view of the above mentioned codes, an action potential (AP) is normalized into a stereotypical form. However, recently we have witnessed another approach to information processing in the single neuron. An ongoing debate surrounds the question of the temporal resolution at which information is represented by individual APs. The longstanding view that an AP is an unchangeable thing has now been challenged.

There is a method of neuron stimulation called "conductance injection" (or "dynamic clamp") (Robinson, 1994). Conductance - representing the response to patterns of presynaptic firing - is applied to the neuron such that current is injected according to the instantaneous value of this conductance and the neuron's membrane potential. Conductance injection reproduces the current-limiting and shunting behaviour and the other dynamical aspects of real synaptic inputs. If experiments are done using conductance injection, the shapes of the resulting APs follow a characteristic width–height distribution. With this method it is possible to look up its immediate conductance history for each spike. The conductance histories leading to different AP waveforms are different: higher background conductance results in broader spikes.

In a study by de Polavieja et al. (2005), dynamic conductance stimulus patterns were used to investigate whether spike shapes reliably depend on the previous stimulus history. They show that in cortical neurons AP waveforms depend on the previous 50 ms of the conductance stimulus history. The authors concluded that this relationship is low in noise, carrying three to four times more information than spike times alone. Although spike frequency and spike shape are related, in general, it is only the spike shape which reliably depends on the different features of the stimulus history on a trial-by-trial basis.

It was shown by Koch & Segev (2000) that for a spike rate of 10 AP/sec, a code based on AP waveforms can transmit 200 bits/sec, compared to the 50 bits/sec of a code based on spike times. It becomes clear that the interaction between incoming postsynaptic potentials and a back propagating AP - as well as, therefore, the probability of producing the next spike depends upon the AP waveform.

In a recent work, Juusola et al. (2007) have shown that the different AP shapes produced by the same pyramidal cortical neuron are not random but correspond to the level of the

new stimulus. In such cases, each neuron transmits exactly one spike per stimulus and it is clear that only the timing conveys information rather than the number of spikes. In the hippocampus, in the olfactory system, and also in other areas of the brain, oscillations of some global variable (e.g., the population activity) are quite common. These oscillations could serve as an internal reference signal. Neuronal spike trains could then encode information in the phase of a pulse with respect to the background oscillation. There is, for example, evidence that the phase of a spike during an oscillation in the hippocampus of rats conveys information on the spatial location of the animal which is not fully accounted for by

The next step in information coding was "rate codes" (Tateno & Robinson, 2006). In fact, there are three different notions of rate, the three definitions of which refer to three different averaging procedures: either an average over time, an average over several repetitions of the

In the view of the above mentioned codes, an action potential (AP) is normalized into a stereotypical form. However, recently we have witnessed another approach to information processing in the single neuron. An ongoing debate surrounds the question of the temporal resolution at which information is represented by individual APs. The longstanding view

There is a method of neuron stimulation called "conductance injection" (or "dynamic clamp") (Robinson, 1994). Conductance - representing the response to patterns of presynaptic firing - is applied to the neuron such that current is injected according to the instantaneous value of this conductance and the neuron's membrane potential. Conductance injection reproduces the current-limiting and shunting behaviour and the other dynamical aspects of real synaptic inputs. If experiments are done using conductance injection, the shapes of the resulting APs follow a characteristic width–height distribution. With this method it is possible to look up its immediate conductance history for each spike. The conductance histories leading to different AP waveforms are different: higher background

In a study by de Polavieja et al. (2005), dynamic conductance stimulus patterns were used to investigate whether spike shapes reliably depend on the previous stimulus history. They show that in cortical neurons AP waveforms depend on the previous 50 ms of the conductance stimulus history. The authors concluded that this relationship is low in noise, carrying three to four times more information than spike times alone. Although spike frequency and spike shape are related, in general, it is only the spike shape which reliably

It was shown by Koch & Segev (2000) that for a spike rate of 10 AP/sec, a code based on AP waveforms can transmit 200 bits/sec, compared to the 50 bits/sec of a code based on spike times. It becomes clear that the interaction between incoming postsynaptic potentials and a back propagating AP - as well as, therefore, the probability of producing the next spike -

In a recent work, Juusola et al. (2007) have shown that the different AP shapes produced by the same pyramidal cortical neuron are not random but correspond to the level of the

depends on the different features of the stimulus history on a trial-by-trial basis.

the firing rate of the neuron" (Gerstner & Kistler, 2002).

experiment, or an average over a population of neurons.

that an AP is an unchangeable thing has now been challenged.

conductance results in broader spikes.

depends upon the AP waveform.

previous conductance. The authors named this phenomenon the "AP waveform code". It is proposed that the code influences postsynaptic targets in different ways. One of these ways sees APs of different shapes interacting with incoming excitatory postsynaptic potentials (EPSPs) and influencing synaptic integration differently. Another way could be that the variability of AP waveforms might be saved in the synaptic terminal until a different calcium influx translates these variables into different EPSP sizes in the postsynaptic cell.

Shu et al. (2006) have shown that the somatic AP waveforms survive axonal conduction. It was also shown that the differences in the somatic AP waveforms are further augmented by axonal conduction. This result was obtained using simultaneous somatic and axonal recordings. Furthermore, the EPSPs that they recorded were larger when the presynaptic somatic and axonal spikes were broader.

Häusser et al. (2001) have shown that because the cortical APs of different waveforms differentially shunt incoming synaptic events, they participate in the synaptic integration and the generation of succeeding APs. Therefore, at least 50 ms of the stimulus history can affect the state of the synaptic integration of the neuron and the generation of spikes, through the modulation of the waveform. This is a form of local encoding at the singleneuron level with a 50-ms-long memory, which affects neuronal communication by influencing the production of future spikes.

In summary, for a long time it was thought that the waveform of the AP was constant. However, new evidence continues to show the opposite: it has become clear that the waveform of an AP carries information.

The question arises: Is it possible that the AP waveform contains information about habituation when a single neuron is stimulated with intracellular current impulses and habituates to the stimulation?

The most desirable experiment for answering this question might consider conductance injection into the single neuron. However, there are major limitations to conductance injection. It has to be fast and temporally consistent. For this reason, we decided to stimulate a single neuron with the injection of short current impulses and investigate habituation to the stimulation. We investigated the possible relation of changes of the AP's parameters (and consequently the AP waveform) with the process of habituation.

#### **2. Materials and methods**

The isolated nervous system of the mollusc *Helix pomatia* was used in the experiments. Each snail was anesthetized by the injection of isotonic MgCl2, described elsewhere (Patsvania et al., 2008). Then the nervous system was separated from the body. Ganglia were treated with 0.5% Pronaze solution (Protease from Streptomyces griseus - "Sigma-Aldrich") for 30 minutes at room temperature. After proteolytic treatment, the conjunctive tissue was carefully removed using fine micro scissors. Then, the ganglia were washed several times with Ringer solution. This solution consists of: NaCl - 80 mmol, KCl - 4 mmol, CaCl2 - 35 mmol, MgCl2 .6 H2O - 5 mmol, Tris -7 mmol at pH=7.5. The nervous system was placed in a Petri dish and positioned in a Faraday cage to filter out any environmental electromagnetic noise. The identified giant

Is the Action Potential Waveform Constant? 7

For intracellular stimulation, the neuron was impaled with two glass microelectrodes (ME) filled with 2.5 mol KCl. For this purpose, "Piezo Mikromanipulators - PM 20" (Märzhäuser, Wetzlar, Germany) were used. The ME were prepared from capillaries - Borosilicate Tubing (PYREX©) BF 150 75 10 - with filaments (Sutter Instrument Company, Novato, CA, USA). The size of the microelectrode tip was less than 1 µm. The resistance of each microelectrode did not exceed 15 mOhm. Microelectrodes were connected to the "intracellular electrometer IE 251A" (Warner Instruments). One microelectrode served for registration and the other for intracellular stimulation. "Picoamper source K 261" (Keithley Instruments Inc., Cleveland, OH, USA) was used for intracellular stimulation. The output of this device was controlled by a specially designed pulse breaker guaranteeing the application of depolarizing current impulses to the neuron. The intracellular stimuli consisted of a train of depolarizing current impulses, each with a 4 ms width. The frequency of these impulses was 0.9 Hz. This value is close to the frequency of the AP firing of many of the pacemaker neurons of the mollusc ganglion which are synaptically connected to the investigated LPG#3. The PowerLab ML866 data acquisition unit (ADInstruments Co., Castle Hill, NSW, Australia) was used for the registration of experimental data. In Table 1, the variables measured in our experiments are given. All of these variables were measured for each AP. Consequently, sets of numbers were obtained for each experiment. The sets contain data about latent periods, W20s, Tr and Areas. Measurements were performed using the "peak analysis module" for

Parameter description. Notation

Latency Period

W20

Tr

The time interval between the leading edge of the intracellular stimulating impulse and the appearance of AP triggering.

corresponds to the neuron resting potential.

on the leading edge of the AP.

**3. Results**

Table 1. Designations used in the text.

Width of the AP at the level of 20% from the baseline. The baseline

habituation dynamics for one of the neurons is shown in Figure 2.

Increasing time between two levels (10% and 90 % from the baseline)

Area between the waveform and the baseline. Area

Neuron reactions to the intracellular current impulses depend on the amplitude and width of the stimulating impulses. The neuron reacted to the intracellular stimulating impulses with an AP several times, and then habituation arose. Habituation was expressed as a decline of the stimulus-induced AP. The time necessary for habituation varied from several seconds to 1-2 minutes. This time span covers the period between the first intracellular stimulating impulse and the last intracellular impulse after which no more AP was evoked. There is a one-to-one relationship between the time of habituation and the number of APs (or stimulating impulses). For this reason, we define the time of habituation as the amount of stimulating impulses necessary to give rise to habituation. For the purposes of illustration, a sample of the

the software "Chart-5".

neuron #3 of the Left Parietal Ganglion was selected for the investigation. The average diameter of this neuron was 150-200 µm. Identifications were made according to Arakelov et al. (1991). The most naturalistic means of stimulating the neuron is through synaptic input. Antidromic excitation of the nerve with direct current (DC) or pulsed voltage causes AP firing in the inter-neurons, which in turn excites the investigated neuron synaptically. However, if voltage is applied at different locations, the nerve reactions will differ from each other. For this reason, with nerve stimulation it is impossible to maintain any invariance of stimulation. This was the reason for which we chose neuron stimulation with intracellular short current impulses. In this case, it is easy to obtain a condition where the neuron reacts with one AP to one intracellular stimulating impulse. On the other hand, the current stimulating impulse has to be much shorter than the latency period in order to avoid the blending of the AP with the stimulating impulse. The reaction of the neuron depends on the amplitude of the intracellular stimulating impulses. Stimulation was always begun with impulses of a very low amplitude. At the beginning of each recording, the critical value of the intracellular stimulating impulse was established which depolarized the neuron to the threshold and caused the firing of an AP. The experiments showed that this value was always above 0.05 nA and varied from neuron to neuron. Each stimulus triggered only one AP. As such, the reaction pattern consisted of one AP as a reaction to one intracellular stimulating impulse. First, we established the value of the threshold (i.e. we found the critical value of the stimulus that depolarised a neuron to the threshold and caused the firing of only one AP). Next, we increased the amplitude of the stimulus to slightly higher than this critical value.

Figure 1 illustrates a neuron reaction to stimulation with intracellular impulses of an increasing amplitude.

Fig. 1. Neuron reaction to stimulation with increased intracellular current impulses. Current stimulating impulses are shown schematically under the recording. The first four impulses evoke only small shift of membrane potential towards depolarization. The fifth stimulating impulse, with an amplitude 0.5 nA evoked the AP firing of the neuron. The sixth stimulating impulse and those following it have an amplitude of 0.6 nA. The duration of each intracellular impulse is 0.4 ms. It is obvious that this duration is much shorter than the latency period (the latency period is defined as the time interval between the front edge of stimulating impulse and the time moment when membrane potential crosses the threshold value).

neuron #3 of the Left Parietal Ganglion was selected for the investigation. The average diameter of this neuron was 150-200 µm. Identifications were made according to Arakelov et al. (1991). The most naturalistic means of stimulating the neuron is through synaptic input. Antidromic excitation of the nerve with direct current (DC) or pulsed voltage causes AP firing in the inter-neurons, which in turn excites the investigated neuron synaptically. However, if voltage is applied at different locations, the nerve reactions will differ from each other. For this reason, with nerve stimulation it is impossible to maintain any invariance of stimulation. This was the reason for which we chose neuron stimulation with intracellular short current impulses. In this case, it is easy to obtain a condition where the neuron reacts with one AP to one intracellular stimulating impulse. On the other hand, the current stimulating impulse has to be much shorter than the latency period in order to avoid the blending of the AP with the stimulating impulse. The reaction of the neuron depends on the amplitude of the intracellular stimulating impulses. Stimulation was always begun with impulses of a very low amplitude. At the beginning of each recording, the critical value of the intracellular stimulating impulse was established which depolarized the neuron to the threshold and caused the firing of an AP. The experiments showed that this value was always above 0.05 nA and varied from neuron to neuron. Each stimulus triggered only one AP. As such, the reaction pattern consisted of one AP as a reaction to one intracellular stimulating impulse. First, we established the value of the threshold (i.e. we found the critical value of the stimulus that depolarised a neuron to the threshold and caused the firing of only one AP). Next, we increased the

Figure 1 illustrates a neuron reaction to stimulation with intracellular impulses of an

Fig. 1. Neuron reaction to stimulation with increased intracellular current impulses. Current stimulating impulses are shown schematically under the recording. The first four impulses evoke only small shift of membrane potential towards depolarization. The fifth stimulating impulse, with an amplitude 0.5 nA evoked the AP firing of the neuron. The sixth stimulating impulse and those following it have an amplitude of 0.6 nA. The duration of each intracellular impulse is 0.4 ms. It is obvious that this duration is much shorter than the latency period (the latency period is defined as the time interval between the front edge of stimulating impulse

and the time moment when membrane potential crosses the threshold value).

amplitude of the stimulus to slightly higher than this critical value.

increasing amplitude.

For intracellular stimulation, the neuron was impaled with two glass microelectrodes (ME) filled with 2.5 mol KCl. For this purpose, "Piezo Mikromanipulators - PM 20" (Märzhäuser, Wetzlar, Germany) were used. The ME were prepared from capillaries - Borosilicate Tubing (PYREX©) BF 150 75 10 - with filaments (Sutter Instrument Company, Novato, CA, USA). The size of the microelectrode tip was less than 1 µm. The resistance of each microelectrode did not exceed 15 mOhm. Microelectrodes were connected to the "intracellular electrometer IE 251A" (Warner Instruments). One microelectrode served for registration and the other for intracellular stimulation. "Picoamper source K 261" (Keithley Instruments Inc., Cleveland, OH, USA) was used for intracellular stimulation. The output of this device was controlled by a specially designed pulse breaker guaranteeing the application of depolarizing current impulses to the neuron. The intracellular stimuli consisted of a train of depolarizing current impulses, each with a 4 ms width. The frequency of these impulses was 0.9 Hz. This value is close to the frequency of the AP firing of many of the pacemaker neurons of the mollusc ganglion which are synaptically connected to the investigated LPG#3. The PowerLab ML866 data acquisition unit (ADInstruments Co., Castle Hill, NSW, Australia) was used for the registration of experimental data. In Table 1, the variables measured in our experiments are given. All of these variables were measured for each AP. Consequently, sets of numbers were obtained for each experiment. The sets contain data about latent periods, W20s, Tr and Areas. Measurements were performed using the "peak analysis module" for the software "Chart-5".

Table 1. Designations used in the text.

#### **3. Results**

Neuron reactions to the intracellular current impulses depend on the amplitude and width of the stimulating impulses. The neuron reacted to the intracellular stimulating impulses with an AP several times, and then habituation arose. Habituation was expressed as a decline of the stimulus-induced AP. The time necessary for habituation varied from several seconds to 1-2 minutes. This time span covers the period between the first intracellular stimulating impulse and the last intracellular impulse after which no more AP was evoked. There is a one-to-one relationship between the time of habituation and the number of APs (or stimulating impulses). For this reason, we define the time of habituation as the amount of stimulating impulses necessary to give rise to habituation. For the purposes of illustration, a sample of the habituation dynamics for one of the neurons is shown in Figure 2.

Is the Action Potential Waveform Constant? 9

The parameters of the AP and the latent period varied irregularly during an increase of the number of the applied intracellular current impulses. The numerical values of AP parameters and the latent period trended to increase during an increase of the number of stimulating impulses (i.e. with an increase of the AP number, since the stimulus number coincides with the AP number). The typical dependence of the latent period, W20, Tr and Area on the stimulating impulse number (consequently on AP number) for one of the

We determined the amount of information about habituation contained in the AP parameters (W20, Tr, Area) and the latent period. For this purpose, a mathematical model was selected for each parameter of the AP and the latent period. It was shown that this amount of information changes during an increase of the AP number. We deal with experimentally measured data. Consequently, it is essential that corresponding mathematical models have a stochastic character. In particular, sequences of latent periods and AP parameters (W20, Tr, Area) create a time series with a linear trend. According to the statistical investigations (the appendix), a time series with a linear trend represents a sufficiently good mathematical model. Concerning the time necessary for establishing habituation - it was not constant and it varied from neuron to neuron. Consequently, we obtained a numerical sequence containing 32 observed values of the time of habituation. The time of habituation might be regarded as a random variable. The theoretical mean value (mathematical expectation) of this random variable is unknown*.* A 99% confidence interval of this unknown mean value is 22, 45 -- 57, 49. The amount of information was experimentally calculated. The dynamics of the calculated information for the latent period are given in Figure 8. The information amount reaches its maximum value and then begins

1 6 11 16 21 26 31 36

**ST #**

Fig. 4. An example of the latent period dependence on the stimulating impulse number for one neuron. On the abscissa, the number of intracellular stimulating impulses is plotted, while on the ordinate the latent period in milliseconds is plotted. Mean values of the measured latent period vary irregularly; however, the trend increases steadily. The amplitude of the applied intracellular current impulses was 0.1 nA and the duration of each of these impulse was 4 ms.

neurons is shown, respectively, in the Figures 4-7.

0

10

20

30

40

**Latent Period ms**

After habituation, the recovery of the neuron required 15-20 minutes and a new series of stimulation might trigger APs. However, the new response was shorter and habituation was established sooner. For this reason, each neuron was stimulated with the train of intracellular impulses only once.

The parameters of AP, latent period (and consequently AP waveform) were not constant and varied during stimulation. Figure 3 shows a typical change of AP parameters and latent period evoked by the application of the recurrent intracellular current impulses.

Fig. 2. Neuronal response to stimulation with intracellular current impulses. The neuron responded to stimulation with 93 AP after which all of the stimuli ceased to evoke an AP, Complete habituation was established and small depolarizing artefacts were present in recordings instead of the AP. The amplitude of the stimulus pulses was 0.7 nA. the width of the intracellular stimulating impulses was 4 msec. Current stimulating impulses are not shown because each of these impulses causes the firing of one AP or the appearance of one artefact on the recordings. The calibration is 10 mV, 5 s.

Fig. 3. The different AP parameters correspond to a different stimulus number: a) AP fired by the neuron in response to the 1st intracellular current impulse. The parameters are: Latent period = 20.8 ms; W20 = 25.6 ms; Tr = 1.92 ms; Area = 0.106 V.s. b) AP fired by the neuron in response to the 10th intracellular current impulse. The parameters are: Latent period = 33.6 ms; W20 = 33.6 ms; Tr = 2.56 ms; Area = 0.135 V.s. The broadening of the AP is evident. The intracellular stimulating impulses are schematically shown at the left lower corner of the recordings.

After habituation, the recovery of the neuron required 15-20 minutes and a new series of stimulation might trigger APs. However, the new response was shorter and habituation was established sooner. For this reason, each neuron was stimulated with the train of

The parameters of AP, latent period (and consequently AP waveform) were not constant and varied during stimulation. Figure 3 shows a typical change of AP parameters and latent

Fig. 2. Neuronal response to stimulation with intracellular current impulses. The neuron responded to stimulation with 93 AP after which all of the stimuli ceased to evoke an AP, Complete habituation was established and small depolarizing artefacts were present in recordings instead of the AP. The amplitude of the stimulus pulses was 0.7 nA. the width of the intracellular stimulating impulses was 4 msec. Current stimulating impulses are not shown because each of these impulses causes the firing of one AP or the appearance of one

Fig. 3. The different AP parameters correspond to a different stimulus number:

b) AP fired by the neuron in response to the 10th intracellular current impulse. The

are: Latent period = 20.8 ms; W20 = 25.6 ms; Tr = 1.92 ms; Area = 0.106 V.s.

shown at the left lower corner of the recordings.

a) AP fired by the neuron in response to the 1st intracellular current impulse. The parameters

parameters are: Latent period = 33.6 ms; W20 = 33.6 ms; Tr = 2.56 ms; Area = 0.135 V.s. The broadening of the AP is evident. The intracellular stimulating impulses are schematically

artefact on the recordings. The calibration is 10 mV, 5 s.

period evoked by the application of the recurrent intracellular current impulses.

intracellular impulses only once.

The parameters of the AP and the latent period varied irregularly during an increase of the number of the applied intracellular current impulses. The numerical values of AP parameters and the latent period trended to increase during an increase of the number of stimulating impulses (i.e. with an increase of the AP number, since the stimulus number coincides with the AP number). The typical dependence of the latent period, W20, Tr and Area on the stimulating impulse number (consequently on AP number) for one of the neurons is shown, respectively, in the Figures 4-7.

We determined the amount of information about habituation contained in the AP parameters (W20, Tr, Area) and the latent period. For this purpose, a mathematical model was selected for each parameter of the AP and the latent period. It was shown that this amount of information changes during an increase of the AP number. We deal with experimentally measured data. Consequently, it is essential that corresponding mathematical models have a stochastic character. In particular, sequences of latent periods and AP parameters (W20, Tr, Area) create a time series with a linear trend. According to the statistical investigations (the appendix), a time series with a linear trend represents a sufficiently good mathematical model. Concerning the time necessary for establishing habituation - it was not constant and it varied from neuron to neuron. Consequently, we obtained a numerical sequence containing 32 observed values of the time of habituation. The time of habituation might be regarded as a random variable. The theoretical mean value (mathematical expectation) of this random variable is unknown*.* A 99% confidence interval of this unknown mean value is 22, 45 -- 57, 49. The amount of information was experimentally calculated. The dynamics of the calculated information for the latent period are given in Figure 8. The information amount reaches its maximum value and then begins

Fig. 4. An example of the latent period dependence on the stimulating impulse number for one neuron. On the abscissa, the number of intracellular stimulating impulses is plotted, while on the ordinate the latent period in milliseconds is plotted. Mean values of the measured latent period vary irregularly; however, the trend increases steadily. The amplitude of the applied intracellular current impulses was 0.1 nA and the duration of each of these impulse was 4 ms.

Is the Action Potential Waveform Constant? 11

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65

Fig. 7. Area dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted the Area in Volt•seconds. The variations and trends are similar to the other parameters. The amplitude of the applied intracellular current impulses was 0.1 nA, while the duration of each of these

to decrease during the above mentioned 99% confidence interval. The dynamics of the calculated information for the other AP parameters were similar and, for this reason, are not

For more detailed consideration, let us consider the case of the latent period and then generalize it for the AP parameters. As is known (Yaglom & Yaglom, 1983), one random experiment contains some information about another random experiment. It is obvious that experiments must be interconnected. In our case, the first random experiment is the latent period's variation relative to its own trend. Let us denote this random experiment as

The stimulating impulse numbers are plotted on the X axis. The amount of information

The number of the AP (which coincides with the stimulating impulse number) is plotted on the X axis. The calculated amount of information is plotted on the Y axis. As is noticeable, the amount of information firstly increases very quickly with an increase of the AP number. Then, the amount of information reaches a maximum value, after which it decreases. The 99% confidence interval is 22, 45 -- 57, 49. This confidence interval is shown through the

.

experiment in respect of the

. The second random experiment shows whether habituation is established*.*

experiment (i.e., about habituation) is

**ST #**

0

pulses was 4 ms.

shown here.

experiment

contained in the

lighter colouration.

Let us denote this experiment as

plotted on the Y axis (Figure 8).

**Area** **V.s**

0.01 0.02 0.03 0.04 0.05 0.06 0.07

Fig. 5. An example of W20 dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted W20 in seconds. The increase of the W20 is evident. The character of the trend is towards an increase. The amplitude of the applied intracellular current impulses was 0.1 nA, while the duration of each of these impulses was 4 ms.

Fig. 6. Tr dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted Tr in seconds. Tr varies irregularly, like the latent period. The trend also increases. The amplitude of the applied intracellular current impulses was 0.1 nA and the duration of each these was 4 ms.

1 6 11 16 21 26 31 36

1 6 11 16 21 26 31 36

Fig. 6. Tr dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted Tr in seconds. Tr varies irregularly, like the latent period. The trend also increases. The amplitude of the applied intracellular current impulses was 0.1 nA and the duration of each these was 4 ms.

Fig. 5. An example of W20 dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted W20 in seconds. The increase of the W20 is evident. The character of the trend is towards an increase. The amplitude of the applied intracellular current impulses was 0.1 nA, while the

**ST #**

**ST#**

0.0031

0

0.01

0.02

0.03

0.0032

0.0033

0.0034

0.0035

**W20 s**

duration of each of these impulses was 4 ms.

**Tr s**

Fig. 7. Area dependence on the stimulating impulse number. On the abscissa is plotted the number of intracellular stimulating impulses, while on the ordinate is plotted the Area in Volt•seconds. The variations and trends are similar to the other parameters. The amplitude of the applied intracellular current impulses was 0.1 nA, while the duration of each of these pulses was 4 ms.

to decrease during the above mentioned 99% confidence interval. The dynamics of the calculated information for the other AP parameters were similar and, for this reason, are not shown here.

For more detailed consideration, let us consider the case of the latent period and then generalize it for the AP parameters. As is known (Yaglom & Yaglom, 1983), one random experiment contains some information about another random experiment. It is obvious that experiments must be interconnected. In our case, the first random experiment is the latent period's variation relative to its own trend. Let us denote this random experiment as experiment . The second random experiment shows whether habituation is established*.* Let us denote this experiment as .

The stimulating impulse numbers are plotted on the X axis. The amount of information contained in the experiment in respect of the experiment (i.e., about habituation) is plotted on the Y axis (Figure 8).

The number of the AP (which coincides with the stimulating impulse number) is plotted on the X axis. The calculated amount of information is plotted on the Y axis. As is noticeable, the amount of information firstly increases very quickly with an increase of the AP number. Then, the amount of information reaches a maximum value, after which it decreases. The 99% confidence interval is 22, 45 -- 57, 49. This confidence interval is shown through the lighter colouration.

Is the Action Potential Waveform Constant? 13

amount of information and so we emphasise that the AP parameters contain certain

Results similar to each other were observed for all variables. Particularly, at the beginning of stimulation up to the 7th - 10th application of intracellular current pulses the amount of information contained in the AP parameters or in the latent period is equal to zero- see figure 8. This means that 7-10 stimuli are not "understood" by the neuron as a signal that has to be learnt, i.e., to be habituated. With continuing stimulation, the amount of information begins to rapidly increase and reaches a value of approximately 1 bit. Although the absolutely value 1 bit is not high, nevertheless it shows that AP parameters and latent period are sensitive to information processing. Then the information begins to decline. This is also essential- apparently signals become "common" to the neuron and the amount of information begins to decrease. It might be speculated that a neuron does not receive more "new" information and "makes decision" to stop AP firing as a response to the stimulation

As was mentioned above, the experiments revealed that the AP parameters and the latent period vary irregularly. Variation of any of these variables in time is well-described by the

*Y t a bt t*

where *a* and *b* are constant parameters, *t* is a time variable (the number of applied

latent period, W20, Tr, or Area. As the corresponding statistical procedures of hypothesistesting confirm, all of the above mentioned variables display an increasing trend (see

It can be stated that the mechanisms producing the W20, Tr, Area and latent period are responsible for the process of information processing. The speculative explanation of the variation of the AP parameters and the latent period can be described as follows: the recurrent application of current stimulating impulses might increase the resistance of the membrane around the tip of the stimulus microelectrode. This, in turn, might reduce the amplitude of the perturbation of the membrane potential and slow down the electro-tonic propagation of the depolarization to the AP triggering zone. The result is an increase of the latent period and a broadening of the AP parameters. This, in turn, leads to habituation to

A neuron habituates to repeated stimulation with intracellular current impulses. The parameters of an AP and the latency period vary during habituation (i.e., the waveform of the AP changes). These variations contain information about habituation. Therefore, the question "Is it possible that the AP waveform contains some information about habituation when a single neuron is stimulated intracellularly?" has to be answered

,

*t* is a random component with zero mathematical expectation. *Y t* may be the

information about habituation (habituation time).

(see the appendix).

stimuli),

appendix).

intracellular stimulation.

**5. Conclusion**

positively.

time series with a linear trend:

Fig. 8. Dependence of the amount of information on the AP number.

#### **4. Discussion**

Habituation is regarded as one form of learning (Kandel, 1976). Consequently, habituation might be understood as a result of the processing and saving of information. This phenomenon appears at different levels of the organism. Habituation also appears at the level of the individual neuron. Therefore, it could be stated that the observed effect of neuronal habituation in response to intracellular stimulation with current impulses might be thought of as a result of information processing at the level of the individual neuron.

We measured the defined quantities in our experiments. These quantities represent interconnected random variables. The variation of one of them inevitably causes the variation of the corresponding probability of the other random variable. Consequently, it is possible to speak about the amount of information which is contained in one random variable (random experiment) about another random variable.

As the parameters of the AP and the latent period are interconnected random variables, it is possible to calculate, experimentally, the amount of information with respect to the habituation contained in these variables.

The calculations show that an amount of information contained in each of the separate AP parameters or in the latent period differs from zero. Their behaviour is given by Figure 8. This enables one to discuss and analyse the semantics of the information; however, this is an issue for a separate study. In the present study, we are concerned only with changes of the

Fig. 8. Dependence of the amount of information on the AP number.

variable (random experiment) about another random variable.

habituation contained in these variables.

Habituation is regarded as one form of learning (Kandel, 1976). Consequently, habituation might be understood as a result of the processing and saving of information. This phenomenon appears at different levels of the organism. Habituation also appears at the level of the individual neuron. Therefore, it could be stated that the observed effect of neuronal habituation in response to intracellular stimulation with current impulses might be thought of as a result of information processing at the level of the individual

We measured the defined quantities in our experiments. These quantities represent interconnected random variables. The variation of one of them inevitably causes the variation of the corresponding probability of the other random variable. Consequently, it is possible to speak about the amount of information which is contained in one random

As the parameters of the AP and the latent period are interconnected random variables, it is possible to calculate, experimentally, the amount of information with respect to the

The calculations show that an amount of information contained in each of the separate AP parameters or in the latent period differs from zero. Their behaviour is given by Figure 8. This enables one to discuss and analyse the semantics of the information; however, this is an issue for a separate study. In the present study, we are concerned only with changes of the

**4. Discussion**

neuron.

amount of information and so we emphasise that the AP parameters contain certain information about habituation (habituation time).

Results similar to each other were observed for all variables. Particularly, at the beginning of stimulation up to the 7th - 10th application of intracellular current pulses the amount of information contained in the AP parameters or in the latent period is equal to zero- see figure 8. This means that 7-10 stimuli are not "understood" by the neuron as a signal that has to be learnt, i.e., to be habituated. With continuing stimulation, the amount of information begins to rapidly increase and reaches a value of approximately 1 bit. Although the absolutely value 1 bit is not high, nevertheless it shows that AP parameters and latent period are sensitive to information processing. Then the information begins to decline. This is also essential- apparently signals become "common" to the neuron and the amount of information begins to decrease. It might be speculated that a neuron does not receive more "new" information and "makes decision" to stop AP firing as a response to the stimulation (see the appendix).

As was mentioned above, the experiments revealed that the AP parameters and the latent period vary irregularly. Variation of any of these variables in time is well-described by the time series with a linear trend:

$$Y(t) = a + bt + \varepsilon(t)\_{\prime\prime}$$

where *a* and *b* are constant parameters, *t* is a time variable (the number of applied stimuli), *t* is a random component with zero mathematical expectation. *Y t* may be the latent period, W20, Tr, or Area. As the corresponding statistical procedures of hypothesistesting confirm, all of the above mentioned variables display an increasing trend (see appendix).

It can be stated that the mechanisms producing the W20, Tr, Area and latent period are responsible for the process of information processing. The speculative explanation of the variation of the AP parameters and the latent period can be described as follows: the recurrent application of current stimulating impulses might increase the resistance of the membrane around the tip of the stimulus microelectrode. This, in turn, might reduce the amplitude of the perturbation of the membrane potential and slow down the electro-tonic propagation of the depolarization to the AP triggering zone. The result is an increase of the latent period and a broadening of the AP parameters. This, in turn, leads to habituation to intracellular stimulation.

#### **5. Conclusion**

A neuron habituates to repeated stimulation with intracellular current impulses. The parameters of an AP and the latency period vary during habituation (i.e., the waveform of the AP changes). These variations contain information about habituation. Therefore, the question "Is it possible that the AP waveform contains some information about habituation when a single neuron is stimulated intracellularly?" has to be answered positively.

Is the Action Potential Waveform Constant? 15

1 *mi <sup>t</sup> i i <sup>m</sup> <sup>t</sup> <sup>i</sup>*

of conducting the hypothesis testing procedures (the so-called *t* test). Statistics

1

and the arithmetical

for each ith neuron for the purpose

*s*

as criteria

of the

: 0 0 *H Et*

: 0 1 *H Et*

, ,..., 1 2 32

statistics – so-called *t* statistics.

1 32 <sup>32</sup> <sup>1</sup> *<sup>i</sup> <sup>i</sup>*

Here,

neuron.

: 0 <sup>0</sup> *H b* (main hypothesis)

Here, *E t*

(main hypothesis),

(alternative hypothesis).

mean comprised by these differences <sup>1</sup>

is the mathematical expectation for the fixed *t* .

Let us consider the differences *t Yt X t t m* , 1,2,..., *i ii i*

and

1. Numerical value of *t* - statistics is -0.474 and *P* value is 0.64 for W20. 2. Numerical value of *t* - statistics is -0.531 and *P* value is 0.6 for Tr. 3. Numerical value of *t* - statistics is -0.232 and *P* value is 0.82 for Area.

*<sup>s</sup> <sup>i</sup> <sup>i</sup>*

AP parameters also. The corresponding calculations show that:

will then be obtained. Let us take the statistics *<sup>T</sup>* <sup>32</sup>

<sup>32</sup> 2 2 <sup>2</sup> <sup>1</sup> <sup>32</sup> . <sup>31</sup> <sup>1</sup> <sup>31</sup>

We use standard normal distribution tables because the statistical sample consists of the 32 terms and the corresponding tables of the *t* -statistics is comprised by not more than 30 terms from the sample. The corresponding calculation for the latent periods shows that the numerical value of the *t* statistics is approximately equal to -0.199, and the *P* value is approximately equal to 0.846. The above mentioned mathematical model was applied to the

It is obvious that the *P* values are sufficiently large in all cases. Consequently, it could be stated that there is no basis for discarding 0 *<sup>H</sup>* hypothesizes. This means, in turn, that the application of the mathematical model (1) of the time sequence is relevant for all AP parameters and the latent period. Now, let us consider the statistical hypothesis test for proving that there are increasing trends for the latent period and the AP parameters before establishing habituation. To avoid overloading the corresponding formulas with too much notation, let us provide a procedure of statistical hypothesis testing for the latent period (the so-called *t* test). Then, generalize this *t* test

for all of the AP parameters. Let us calculate statistical estimations *bi*

Statistics , ,..., 1 2 32 *bb b* will be obtained as a result. The statistical hypotheses

unknown *b* coefficients included in formula (1) by means of formula (2) for each ith

#### **6. Appendix**

One and the same type of mathematical model is used for all of the variables - W20, Tr, Area and the latent period. The model is called a "time series with linear trend". Consequently, for the purpose of notational simplification, let us first consider the mathematical model of latent period variation and then generalize it for the AP parameters.

$$Y(t) = a + bt + \varepsilon(t),\tag{1}$$

Here:*Y t* is the latent period, *t* is the number of intracellular impulses (its time variable), *a* and *b* are constant parameters and *t* represents a random component with zero mathematical expectation. Experiments were performed on 32 neurons. Consequently, the latent period was measured in a series of 32 experiments. From formula (1), it is derived that the result of measurements for an ith series (i.e., for ith neuron) could be written as follows:

$$\mathcal{Y}\_{\dot{l}}\left(t\right) = a\_{\dot{l}}^{\ast} + b\_{\dot{l}}^{\ast}t + \varepsilon\_{\dot{l}}\left(t\right), \text{ i } = 1, 2, \dots, 32.$$

where *a i* and *<sup>b</sup> i* represent statistical estimations of unknown *a* and *b* coefficients.

As is well known (Cramer, 1945), *a i* and *bi* are given by the following formulae:

$$b\_{i}"\begin{split}b\_{i}"&=\frac{m\_{i}}{\sum\_{t=1}^{\sum}tY\_{i}(t)-m\_{i}\left(\frac{1+m\_{i}}{2}\right)\overline{Y\_{i}}},\\&\sum\_{t=1}^{m\_{i}}t^{2}-m\_{i}\left(\frac{1+m\_{i}}{2}\right)^{2}\end{split}\tag{2}$$

$$a\_{\dot{i}}{}^{\*} = \overline{Y\_{\dot{i}}} - \frac{1 + m\_{\dot{i}}}{2} b\_{\dot{i}}{}^{\*} , \tag{3}$$

$$\overline{Y\_{\dot{l}}} = \frac{1}{m\_{\dot{l}}} \sum\_{t=1}^{m\_{\dot{l}}} Y\_{\dot{l}}(t). \tag{4}$$

where *mi* is the time of habituation for the *i*th neuron.

The adequacy of the selected model (1) must first be tested with statistical methods.

Let us introduce the notation *X t a bt* for the linear trend. Consequently, we will have notations for observed data of the ith neuron, such that:

$$X\_{\dot{i}}^{\
\ast}(t) = a\_{\dot{i}}^{\
\ast} + b\_{\dot{i}}^{\
\ast}t, \quad i = 1, 2, \dots, 32$$

Let us consider the difference *t Yt Xt* for the testing of the adequacy of the model (1) and let us set the following task for hypothesis testing:

: 0 0 *H Et* (main hypothesis),

14 Neuroscience

One and the same type of mathematical model is used for all of the variables - W20, Tr, Area and the latent period. The model is called a "time series with linear trend". Consequently, for the purpose of notational simplification, let us first consider the mathematical model of

*Y t a bt t*

Here:*Y t* is the latent period, *t* is the number of intracellular impulses (its time

zero mathematical expectation. Experiments were performed on 32 neurons. Consequently, the latent period was measured in a series of 32 experiments. From formula (1), it is derived that the result of measurements for an ith series (i.e., for ith neuron) could

> *Yt a bt t i* , 1,2,...,32 *i ii i*

> > <sup>1</sup>

1 2

*mi mi tY t m Y ii i <sup>t</sup> bi mi mi t mi <sup>t</sup>*

*mi aY b ii i*

The adequacy of the selected model (1) must first be tested with statistical methods.

<sup>1</sup> <sup>2</sup> , <sup>2</sup> <sup>1</sup> <sup>2</sup>

<sup>1</sup> , <sup>2</sup>

 <sup>1</sup> . <sup>1</sup> *mi Y Yt i i <sup>m</sup> <sup>t</sup> <sup>i</sup>*

Let us introduce the notation *X t a bt* for the linear trend. Consequently, we will have

*X t a bt i* , 1,2,...,32 *i ii*

represent statistical estimations of unknown *a* and *b* coefficients.

, (1)

*t* represents a random component with

(2)

are given by the following formulae:

(3)

*t Yt Xt* for the testing of the adequacy of the model

(4)

latent period variation and then generalize it for the AP parameters.

*i*

where *mi* is the time of habituation for the *i*th neuron.

notations for observed data of the ith neuron, such that:

(1) and let us set the following task for hypothesis testing:

Let us consider the difference

and *bi*

variable), *a* and *b* are constant parameters and

**6. Appendix**

be written as follows:

As is well known (Cramer, 1945), *a*

where *a i* and *<sup>b</sup> i* : 0 1 *H Et* (alternative hypothesis).

Here, *E t* is the mathematical expectation for the fixed *t* .

Let us consider the differences *t Yt X t t m* , 1,2,..., *i ii i* and the arithmetical mean comprised by these differences <sup>1</sup> 1 *mi <sup>t</sup> i i <sup>m</sup> <sup>t</sup> <sup>i</sup>* for each ith neuron for the purpose of conducting the hypothesis testing procedures (the so-called *t* test). Statistics , ,..., 1 2 32 will then be obtained. Let us take the statistics *<sup>T</sup>* <sup>32</sup> *s* as criteria statistics – so-called *t* statistics.

$$\text{Here, } \overline{\varepsilon^\*} = \frac{1}{32} \sum\_{i=1}^{32} \overline{\varepsilon\_i^\*} \text{ and } \text{ s} = \left( \frac{1}{31} \sum\_{i=1}^{32} \left( \overline{\varepsilon\_i^\*} \right)^2 - \frac{32}{31} \left( \overline{\varepsilon^\*} \right)^2 \right)^{1/2}.$$

We use standard normal distribution tables because the statistical sample consists of the 32 terms and the corresponding tables of the *t* -statistics is comprised by not more than 30 terms from the sample. The corresponding calculation for the latent periods shows that the numerical value of the *t* statistics is approximately equal to -0.199, and the *P* value is approximately equal to 0.846. The above mentioned mathematical model was applied to the AP parameters also. The corresponding calculations show that:

It is obvious that the *P* values are sufficiently large in all cases. Consequently, it could be stated that there is no basis for discarding 0 *<sup>H</sup>* hypothesizes. This means, in turn, that the application of the mathematical model (1) of the time sequence is relevant for all AP parameters and the latent period. Now, let us consider the statistical hypothesis test for proving that there are increasing trends for the latent period and the AP parameters before establishing habituation. To avoid overloading the corresponding formulas with too much notation, let us provide a procedure of statistical hypothesis testing for the latent period (the so-called *t* test). Then, generalize this *t* test for all of the AP parameters. Let us calculate statistical estimations *bi* of the unknown *b* coefficients included in formula (1) by means of formula (2) for each ith neuron.

Statistics , ,..., 1 2 32 *bb b* will be obtained as a result. The statistical hypotheses

: 0 <sup>0</sup> *H b* (main hypothesis)

Is the Action Potential Waveform Constant? 17

the amount of information depends upon the stimulating impulse number *n* . For this

information's behaviour during increase of *n* . The charts of the amount of information

probabilities *PA PB P B ij* , , , , 1,2 *i iAj <sup>i</sup>* . The stimulating impulse numbers are plotted on the abscissa axis, while the numerical values of the amount of information

amount of information increases up to certain value, and then it decreases and habituation increases. It might be stated that after a certain number of stimulating impulses no "novelty" occurs for the neuron, and so the amount of information decreases

Arakelov, G. Marakujieva, I. & Palikhova, T. (1991). Structural and functional analysis of

De Polavieja, G. Harsch, A. Kleppe, I. Robinson, H. & Juusola, M. (2005). Stimulus History

Gerstner, W. & Kistler, W. (2002)*. Spiking Neuron Models. Single Neurons, Populations,*

Häusser, M. Major, G. & Stuart, G. (2001). Differential shunting of EPSPs by Action

Juusola, M. Robinson, H. & de Polavieja, G. (2007). Coding with spike shapes and

Koch, C. & Segev, I. (2000). The role of single neurons in information processing, Nature

Partsvania, B. Shoshiashvili, L. Sulaberidze T. & Modebadze, Z. (2008). Extremely Low

Robinson, H. (1994). Conductance injection. *Trends Neurosci.* Vol. 17, No. 4, pp. 147-

Shu, Y. Hasenstaub, A. Duque A. Yu, Y. & McCormick, D. (2006). Modulation of

Kandel, E. (1976). *Cellular Basis of Behavior*, W.H. Freeman & Company, San Francisco. Koch, C. (1999). *Biophysics of Computation,* Oxford Univ. Press, ISBN, New York.

Cramer, H. (1945). *Mathematical methods of statistics*. Almqvist & Wiksells, Uppsala.

monosynaptic connections between identified neurons of Helix lucorum. *Simpler nervous systems* (Eds. D.A. Sakharov and W. Winlow), pp. 258-269, University Press,

Reliably Shapes Waveforms of Cortical Neurons. *J. Neurosci.* Vol. 25, No. 23 , pp.

graded potentials in cortical networks. *Bioessays* Vol. 29, No. 2, pp. 178-

Frequency Magnetic Fields effects on the Snail Single Neurons. *Electromagnetic*

intracortical synaptic potentials by presynaptic somatic membrane potential.

,

are obtained as a result of experimental calculations of the above given

are plotted on the ordinate axis in these charts. As these charts show, the

. We are interested in the amount of

will be written instead of *I*

reason, *<sup>I</sup>* , *<sup>n</sup>*

and habituation increases.

Manchester.

5657–5665.

187.

148.

*Plasticity, 2002.* Available from:

http://icwww.epfl.ch/~gerstner/SPNM/node8.html

Neurosience, Vol. 3, No. Supp, pp. 1171-1177.

*biology and Medicine*. Vol. 27, No. 4, pp. 409-418.

*Nature.* Vol. 441, No. 7094, pp. 761-765.

Potentials. *Science* Vol. 291, No. 5501 pp. 138-141.

**7. References**

*<sup>I</sup>* , *<sup>n</sup>*

*<sup>I</sup>* , *<sup>n</sup>*

#### : 0 <sup>1</sup> *H b* (alternative hypothesis)

must be tested based on these statistics. The alternative hypothesis obviously implies that the line of the trend is rising.

The numerical value of the so-called *t* statistics 32 *<sup>b</sup> <sup>T</sup> s* has to be calculated for the use

$$\text{of the } \ t-\text{test. Here, } \overline{\begin{array}{c} \overline{b^\*} = 1 \\ 32 \ i = 1 \end{array}} \sum\_{\mathbf{i}} b\_{\mathbf{i}}^{\mathbf{a}} \text{ and } \ s = \left( \frac{1}{31} \sum\_{i=1}^{32} \left( \overline{b\_{\mathbf{i}}^{\mathbf{a}}} \right)^2 - \frac{32}{31} \left( \overline{b^\*} \right)^2 \right)^{\frac{1}{2}}. \text{ The calculations are:} $$

show that the numerical value of the *t* statistics for the latent period is approximately equal to 2.77. The *P* value is approximately equal to 0.003.

For the AP parameters, similar calculations show that:

It is obvious that the *<sup>P</sup>* values are such small numbers that 0 *<sup>H</sup>* hypothesis must be discarded for the AP parameters and the latent period. Correspondingly, 1 *<sup>H</sup>* must be accepted. Therefore, it could be stated that the AP parameters and latent periods have an increasing trend. Let us calculate the amount of information. We take " experiment" for the determination of the variable's location relative to its own trend (above or under) and " experiment" for the determination of the habituation. Under the term " experiment" we imply the results of measurements of the variables. Under the term "variables", values of the latent period, W20, Tr and Area are implied. Under the term " experiment", we imply the determination of whether habituation is established. *I HH* , is the amount of information retained in the " experiment" regarding the " experiment" – (Yaglom & Yaglom, 1983). Here, *H* is the entropy of the " experiment" and *H* is the conditional entropy of the " experiment" in relation to the " experiment". It is known that log log 121 2 22 *H PB PB PB PB* . Here, 1*B* is the state of affairs where habituation exists, while 2*B* is the state of affairs where habituation is absent.

Correspondingly:

$$H\_{\alpha} \left( \boldsymbol{\beta} \right) = P \left( A\_1 \right) H\_{A\_1} \left( \boldsymbol{\beta} \right) + P \left( A\_2 \right) H\_{A\_2} \left( \boldsymbol{\beta} \right)$$

$$H\_{A\_{\dot{\mathbf{i}}}}(\boldsymbol{\beta}) = -P\_{A\_{\dot{\mathbf{i}}}}(\boldsymbol{B\_{1}}) \log\_{2} P\_{A\_{\dot{\mathbf{i}}}}(\boldsymbol{B\_{1}}) - P\_{A\_{\dot{\mathbf{i}}}}(\boldsymbol{B\_{2}}) \log\_{2} P\_{A\_{\dot{\mathbf{i}}}}(\boldsymbol{B\_{2}}), \; i = 1, 2.$$

Here 1 *<sup>A</sup>* is state of affairs where the numerical value is over the trend line, while 2 *<sup>A</sup>* is the state of affairs where the numerical value is under the trend line. In our experiments, the amount of information depends upon the stimulating impulse number *n* . For this reason, *<sup>I</sup>* , *<sup>n</sup>* will be written instead of *I*, . We are interested in the amount of information's behaviour during increase of *n* . The charts of the amount of information *<sup>I</sup>* , *<sup>n</sup>* are obtained as a result of experimental calculations of the above given probabilities *PA PB P B ij* , , , , 1,2 *i iAj <sup>i</sup>* . The stimulating impulse numbers are plotted on the abscissa axis, while the numerical values of the amount of information *<sup>I</sup>* , *<sup>n</sup>* are plotted on the ordinate axis in these charts. As these charts show, the amount of information increases up to certain value, and then it decreases and habituation increases. It might be stated that after a certain number of stimulating impulses no "novelty" occurs for the neuron, and so the amount of information decreases and habituation increases.

#### **7. References**

16 Neuroscience

must be tested based on these statistics. The alternative hypothesis obviously implies that

*s*

*s bb <sup>i</sup> <sup>i</sup>*

<sup>32</sup> 2 2 <sup>2</sup> <sup>1</sup> <sup>32</sup> . <sup>31</sup> <sup>1</sup> <sup>31</sup>

and

show that the numerical value of the *t* statistics for the latent period is approximately

1. The numerical value of the *t* -statistics is 3.254. The *P* value is approximately equal to

2. The numerical value of the *t* -statistics is 2.498. The *P* value is approximately equal to

3. The numerical value of the *t* -statistics is 3.07. The *P* value is approximately equal to

It is obvious that the *<sup>P</sup>* values are such small numbers that 0 *<sup>H</sup>* hypothesis must be discarded for the AP parameters and the latent period. Correspondingly, 1 *<sup>H</sup>* must be accepted. Therefore, it could be stated that the AP parameters and latent periods have an

the determination of the variable's location relative to its own trend (above or under) and

we imply the results of measurements of the variables. Under the term "variables", values of

is the entropy of the "

experiment" in relation to the "

 1 2 1 2 *H PA H PA H A A*

log log , 1,2 12 1 22 2 *H PB PB PB PB i <sup>A</sup> A AA A <sup>i</sup> i ii i*

Here 1 *<sup>A</sup>* is state of affairs where the numerical value is over the trend line, while 2 *<sup>A</sup>* is the state of affairs where the numerical value is under the trend line. In our experiments,

. Here, 1*B* is the state of affairs where

increasing trend. Let us calculate the amount of information. We take "

the latent period, W20, Tr and Area are implied. Under the term "

amount of information retained in the "

that log log 121 2 22 *H PB PB PB PB*

(Yaglom & Yaglom, 1983). Here, *H*

the conditional entropy of the "

Correspondingly:

experiment" for the determination of the habituation. Under the term "

the determination of whether habituation is established. *I HH*

habituation exists, while 2*B* is the state of affairs where habituation is absent.

has to be calculated for the use

1

experiment" and *H*

,

experiment" regarding the "

experiment", we imply

experiment". It is known

experiment" for

experiment"

is the

 is

experiment" –

The calculations

: 0 <sup>1</sup> *H b* (alternative hypothesis)

The numerical value of the so-called *t* statistics 32 *<sup>b</sup> <sup>T</sup>*

32 1 *b bi <sup>i</sup>*

equal to 2.77. The *P* value is approximately equal to 0.003.

For the AP parameters, similar calculations show that:

the line of the trend is rising.

of the *<sup>t</sup>* test. Here, <sup>1</sup> <sup>32</sup>

0.0001 for the W20

0.007 for the Tr.

"  0.002 for the Area.

http://icwww.epfl.ch/~gerstner/SPNM/node8.html

**2**

*USA*

Thomas Heinbockel *Department of Anatomy,*

**Electrophysiological Recording and**

*Howard University College of Medicine, Washington,*

**Imaging of Neuronal Signals in Brain Slices**

A pressing issue in our understanding of neuronal circuitry is the functional significance of neuromodulator systems. Recent experiments in brain slices that employed new methods and technical innovations have described novel aspects of classic brain signaling mechanisms or revealed unknown mechanisms of cellular communication involving specific neuromodulator systems. Here, we focus on two of these systems, (1) the neurotransmitter glutamate and its metabotropic receptors and (2) novel brain signaling molecules, endocannabinoids, and their receptors, cannabinoid receptors. The study of these two neuromodulator systems has been greatly aided by technological advances in recording and imaging techniques. We describe these techniques and present data to illustrate how these two neuromodulator systems regulate intrinsic properties of neurons and shape sensory and synaptic responses of neurons

The first part of this review will focus on electrophysiological recording and imaging techniques that helped to study the amino acid glutamate as the principal excitatory neurotransmitter in the MOB of the brain. The MOB is the first relay station in the CNS for processing sensory information that comes from olfactory receptor neurons in the nasal epithelium. Neuroanatomical studies demonstrate that projection neurons (mitral and tufted cells) as well as inhibitory interneurons such as GABAergic granule cells in the MOB express high levels of metabotropic glutamate receptors (mGluRs), suggesting that these receptors play a role in the function of the MOB network. Olfactory nerve terminals synapse onto MOB projection neurons, which is mediated by glutamate acting at AMPA and NMDA ionotropic glutamate receptor (iGluR) subtypes as well as mGluRs. New experiments in the MOB have used patch-clamp recordings, microsurgery, optical-imaging (voltage-sensitive dye imaging) and use of mGluR gene knockout mice. These experiments have illuminated the role of mGluRs in the MOB and point toward novel and potent regulatory roles of these receptors in shaping olfactory output from the MOB to higher olfactory centers in the brain. The second part of this review will concentrate on electrophysiological recording and imaging techniques that have established the role of retrograde signaling by endocannabinoids in the hippocampus. Cannabinoids are the active ingredient of marijuana. In addition to being known and used as recreational drugs, cannabinoids are produced endogenously by neurons in the brain (endocannabinoids) and serve as important

in the olfactory (main olfactory bulb, MOB) and limbic system (hippocampus).

**1. Introduction**

Tateno, T. & Robinson, H. (2006). Rate Coding and Spike-Time Variability in Cortical Neurons with Two Types of Threshold Dynamics. *J. Neurophysiol*. Vol. 95, No. 4, pp. 2650-2663.

Yaglom, A. & Yaglom, I. (1983). *Probability and Information*. Reidel Publishing Co., Delhi.

## **Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices**

Thomas Heinbockel

*Department of Anatomy, Howard University College of Medicine, Washington, USA*

#### **1. Introduction**

18 Neuroscience

Tateno, T. & Robinson, H. (2006). Rate Coding and Spike-Time Variability in Cortical

Yaglom, A. & Yaglom, I. (1983). *Probability and Information*. Reidel Publishing Co., Delhi.

pp. 2650-2663.

Neurons with Two Types of Threshold Dynamics. *J. Neurophysiol*. Vol. 95, No. 4,

A pressing issue in our understanding of neuronal circuitry is the functional significance of neuromodulator systems. Recent experiments in brain slices that employed new methods and technical innovations have described novel aspects of classic brain signaling mechanisms or revealed unknown mechanisms of cellular communication involving specific neuromodulator systems. Here, we focus on two of these systems, (1) the neurotransmitter glutamate and its metabotropic receptors and (2) novel brain signaling molecules, endocannabinoids, and their receptors, cannabinoid receptors. The study of these two neuromodulator systems has been greatly aided by technological advances in recording and imaging techniques. We describe these techniques and present data to illustrate how these two neuromodulator systems regulate intrinsic properties of neurons and shape sensory and synaptic responses of neurons in the olfactory (main olfactory bulb, MOB) and limbic system (hippocampus).

The first part of this review will focus on electrophysiological recording and imaging techniques that helped to study the amino acid glutamate as the principal excitatory neurotransmitter in the MOB of the brain. The MOB is the first relay station in the CNS for processing sensory information that comes from olfactory receptor neurons in the nasal epithelium. Neuroanatomical studies demonstrate that projection neurons (mitral and tufted cells) as well as inhibitory interneurons such as GABAergic granule cells in the MOB express high levels of metabotropic glutamate receptors (mGluRs), suggesting that these receptors play a role in the function of the MOB network. Olfactory nerve terminals synapse onto MOB projection neurons, which is mediated by glutamate acting at AMPA and NMDA ionotropic glutamate receptor (iGluR) subtypes as well as mGluRs. New experiments in the MOB have used patch-clamp recordings, microsurgery, optical-imaging (voltage-sensitive dye imaging) and use of mGluR gene knockout mice. These experiments have illuminated the role of mGluRs in the MOB and point toward novel and potent regulatory roles of these receptors in shaping olfactory output from the MOB to higher olfactory centers in the brain.

The second part of this review will concentrate on electrophysiological recording and imaging techniques that have established the role of retrograde signaling by endocannabinoids in the hippocampus. Cannabinoids are the active ingredient of marijuana. In addition to being known and used as recreational drugs, cannabinoids are produced endogenously by neurons in the brain (endocannabinoids) and serve as important

Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices 21

MOB, glutamate is the major excitatory neurotransmitter. Synapses in the MOB most often are formed as serial or reciprocal glutamatergic/GABAergic circuits in discrete layers of the tissue, typically between dendrites of MOB output neurons and local interneurons

Metabotropic glutamate receptors are expressed at unusually high density in the MOB (reviewed in Ennis et al, 2007)*.* Selective mGluR pharmacological reagents as well as mGluR-specific knockout mice have revealed a wide variety of physiological responses and ion channels that are modulated by mGluRs (Conn and Pin, 1997; Schoepp et al, 1999)*.* Eight mGluR subtypes have been cloned and are subdivided into three groups based on second messenger linkage, sequence homology and pharmacological sensitivity of the receptors: Group I (mGluR1 & mGluR5), Group II (mGluR2 & mGluR3) and Group III (mGluR4, mGluR6, mGluR7 & mGluR8). Six of these eight mGluR subtypes have been found in the MOB (reviewed in Heinbockel and Ennis, 2008). While many functions of ionotropic glutamate receptors in the MOB are now well understood (reviewed in Heinbockel and Heyward, 2009), relatively little is known about the function of mGluRs*.* Recent experiments that employed a combination of patch-clamp electrophysiology, voltage-sensitive dye imaging, and use of mGluR gene knockout mice have determined the role of mGluRs in the MOB and demonstrate potent regulatory roles of these receptors in shaping MOB synaptic

mGluRs and iGluRs are distinctly different in their mode of ion channel regulation and time course of action as well as in their sensitivity to glutamate (ED50: mGluR1, 5: 10 μM; AMPAR: 200-500 μM; NMDAR: 2.5-3 μM) (Meldrum, 2000). mGluRs can be much more sensitive to glutamate than iGluRs (AMPA receptors), even though the ED50 for glutamate at AMPA/kainate receptors is significantly determined by subunit composition (Dingledine et al, 1999; Meldrum, 2000). This has important functional consequences for tonic mGluR activation and sensitivity to glutamate spillover under conditions where glutamate

Mitral and tufted cells express high levels of mGluR1 (Masu et al, 1991; Martin et al, 1992; Shigemoto et al, 1992; van den Pol, 1995; Sahara et al, 2001) (Figs. 1, 3). mGluR1 is present on the cell body, and apical and lateral dendrites of mitral and tufted cells, and the splice variant mGluR1a has been localized postsynaptically at olfactory nerve synapses (van den Pol, 1995) which suggests that mGluR1 mediates responses of mitral and tufted cells to glutamatergic inputs from olfactory nerve terminals, and/or could function as auto- or heteroreceptors for glutamate released from the apical or lateral dendrites of mitral and tufted cells. In contrast to their counterparts in the accessory olfactory bulb (where responses to pheromones are processed in many mammals), mitral and tufted cells in the MOB do not express Group II receptors (mGluR2/3) (Ohishi et al, 1993; 1998). mGluR7 and mGluR8 are present on the axon terminals of mitral and tufted cells in piriform cortex

Granule cells express mGluR5 (Group I) (Romano et al., 1995). mGluR5 staining is strongest in the granule cell layer where cell bodies of granule cells are located (Sahara et al., 2001). mGluR5 is found on portions of granule cell dendrites in the external plexiform layer (EPL) apposed to presynaptic glutamatergic synapses from mitral and tufted cell lateral dendrites

(Kinzie et al, 1995; Saugstad et al, 1997; Kinoshita et al, 1998; Wada et al, 1998).

(reviewed in Heinbockel and Heyward, 2009).

output (Heinbockel and Ennis, 2008; Dong et al., 2009).

concentrations are too low to activate AMPA receptors.

**2.1 Metabotropic glutamate receptors in the Main Olfactory Bulb**

signaling molecules in the nervous system and the rest of the body. A combination of patchclamp electrophysiology in cultured brain slices, calcium measurements, and flash photolysis of novel caged compounds has allowed determining the temporal kinetics of the hippocampal endocannabinoid signaling cascade.

#### **2. Synaptic signaling in the Main Olfactory Bulb**

The development of an acute slice preparation of the rodent MOB has greatly improved the functional exploration and understanding of the mammalian olfactory system (Shipley and Ennis, 1996)*.* The intrinsic MOB circuitry is functionally preserved in MOB slice preparations (~400 µm thickness). The axon of each olfactory receptor neuron projects through the olfactory nerve layer (ONL) of the MOB and allows electrical stimulation of afferent inputs (Fig. 1). The functional layers of the MOB and their intrinsic cell types can easily be identified visually. Likewise, axons of output neurons, mitral/tufted cells, leaving the MOB through the lateral olfactory tract (LOT) can be accessed (Nickell et al, 1994; Liu et al, 1994; Aroniadou-Anderjaska et al, 1997; Keller et al, 1998; Aroniadou-Anderjaska et al, 1999). Microdissection cuts in MOB slices allow to isolate specific synaptic pathways, generate slices that contain only selected layers of the MOB (Aungst et al, 2003) or preserve axon pathways projecting back into the MOB from olfactory cortex (Laaris et al, 2007; Balu et al, 2007). These preparations have permitted studying specific synaptic pathways at different levels of processing using electrophysiological and imaging techniques. In the

Fig. 1. Left panel: Simplified diagram of the MOB to illustrate neuronal elements under study (mitral and granule cells). For clarity, other cell types have been omitted from the diagram. EPL – external plexiform layer, GCL – granule cell layer, GL – glomerular layer, IPL – internal plexiform layer, ONL – olfactory nerve layer, MCL – mitral cell layer, MC – mitral cell, GC – granule cell, CFF – centrifugal fibers. Right: Confocal micrograph of a section of the adult mouse main olfactory bulb with a single mitral cell intracellularly filled with biocytin (red) and nuclei stained with counterstain Sytox Green (green). The mitral cell soma is located in the mitral cell layer (MCL). One apical dendrite reaches into one glomerulus and several lateral dendrites span the MOB. Mitral cells are key output neurons from the MOB. Modified from Heinbockel and Ennis, 2008.

signaling molecules in the nervous system and the rest of the body. A combination of patchclamp electrophysiology in cultured brain slices, calcium measurements, and flash photolysis of novel caged compounds has allowed determining the temporal kinetics of the

The development of an acute slice preparation of the rodent MOB has greatly improved the functional exploration and understanding of the mammalian olfactory system (Shipley and Ennis, 1996)*.* The intrinsic MOB circuitry is functionally preserved in MOB slice preparations (~400 µm thickness). The axon of each olfactory receptor neuron projects through the olfactory nerve layer (ONL) of the MOB and allows electrical stimulation of afferent inputs (Fig. 1). The functional layers of the MOB and their intrinsic cell types can easily be identified visually. Likewise, axons of output neurons, mitral/tufted cells, leaving the MOB through the lateral olfactory tract (LOT) can be accessed (Nickell et al, 1994; Liu et al, 1994; Aroniadou-Anderjaska et al, 1997; Keller et al, 1998; Aroniadou-Anderjaska et al, 1999). Microdissection cuts in MOB slices allow to isolate specific synaptic pathways, generate slices that contain only selected layers of the MOB (Aungst et al, 2003) or preserve axon pathways projecting back into the MOB from olfactory cortex (Laaris et al, 2007; Balu et al, 2007). These preparations have permitted studying specific synaptic pathways at different levels of processing using electrophysiological and imaging techniques. In the

Fig. 1. Left panel: Simplified diagram of the MOB to illustrate neuronal elements under study (mitral and granule cells). For clarity, other cell types have been omitted from the diagram. EPL – external plexiform layer, GCL – granule cell layer, GL – glomerular layer, IPL – internal plexiform layer, ONL – olfactory nerve layer, MCL – mitral cell layer, MC – mitral cell, GC – granule cell, CFF – centrifugal fibers. Right: Confocal micrograph of a section of the adult mouse main olfactory bulb with a single mitral cell intracellularly filled with biocytin (red) and nuclei stained with counterstain Sytox Green (green). The mitral cell

soma is located in the mitral cell layer (MCL). One apical dendrite reaches into one

from the MOB. Modified from Heinbockel and Ennis, 2008.

glomerulus and several lateral dendrites span the MOB. Mitral cells are key output neurons

hippocampal endocannabinoid signaling cascade.

**2. Synaptic signaling in the Main Olfactory Bulb**

MOB, glutamate is the major excitatory neurotransmitter. Synapses in the MOB most often are formed as serial or reciprocal glutamatergic/GABAergic circuits in discrete layers of the tissue, typically between dendrites of MOB output neurons and local interneurons (reviewed in Heinbockel and Heyward, 2009).

#### **2.1 Metabotropic glutamate receptors in the Main Olfactory Bulb**

Metabotropic glutamate receptors are expressed at unusually high density in the MOB (reviewed in Ennis et al, 2007)*.* Selective mGluR pharmacological reagents as well as mGluR-specific knockout mice have revealed a wide variety of physiological responses and ion channels that are modulated by mGluRs (Conn and Pin, 1997; Schoepp et al, 1999)*.* Eight mGluR subtypes have been cloned and are subdivided into three groups based on second messenger linkage, sequence homology and pharmacological sensitivity of the receptors: Group I (mGluR1 & mGluR5), Group II (mGluR2 & mGluR3) and Group III (mGluR4, mGluR6, mGluR7 & mGluR8). Six of these eight mGluR subtypes have been found in the MOB (reviewed in Heinbockel and Ennis, 2008). While many functions of ionotropic glutamate receptors in the MOB are now well understood (reviewed in Heinbockel and Heyward, 2009), relatively little is known about the function of mGluRs*.* Recent experiments that employed a combination of patch-clamp electrophysiology, voltage-sensitive dye imaging, and use of mGluR gene knockout mice have determined the role of mGluRs in the MOB and demonstrate potent regulatory roles of these receptors in shaping MOB synaptic output (Heinbockel and Ennis, 2008; Dong et al., 2009).

mGluRs and iGluRs are distinctly different in their mode of ion channel regulation and time course of action as well as in their sensitivity to glutamate (ED50: mGluR1, 5: 10 μM; AMPAR: 200-500 μM; NMDAR: 2.5-3 μM) (Meldrum, 2000). mGluRs can be much more sensitive to glutamate than iGluRs (AMPA receptors), even though the ED50 for glutamate at AMPA/kainate receptors is significantly determined by subunit composition (Dingledine et al, 1999; Meldrum, 2000). This has important functional consequences for tonic mGluR activation and sensitivity to glutamate spillover under conditions where glutamate concentrations are too low to activate AMPA receptors.

Mitral and tufted cells express high levels of mGluR1 (Masu et al, 1991; Martin et al, 1992; Shigemoto et al, 1992; van den Pol, 1995; Sahara et al, 2001) (Figs. 1, 3). mGluR1 is present on the cell body, and apical and lateral dendrites of mitral and tufted cells, and the splice variant mGluR1a has been localized postsynaptically at olfactory nerve synapses (van den Pol, 1995) which suggests that mGluR1 mediates responses of mitral and tufted cells to glutamatergic inputs from olfactory nerve terminals, and/or could function as auto- or heteroreceptors for glutamate released from the apical or lateral dendrites of mitral and tufted cells. In contrast to their counterparts in the accessory olfactory bulb (where responses to pheromones are processed in many mammals), mitral and tufted cells in the MOB do not express Group II receptors (mGluR2/3) (Ohishi et al, 1993; 1998). mGluR7 and mGluR8 are present on the axon terminals of mitral and tufted cells in piriform cortex (Kinzie et al, 1995; Saugstad et al, 1997; Kinoshita et al, 1998; Wada et al, 1998).

Granule cells express mGluR5 (Group I) (Romano et al., 1995). mGluR5 staining is strongest in the granule cell layer where cell bodies of granule cells are located (Sahara et al., 2001). mGluR5 is found on portions of granule cell dendrites in the external plexiform layer (EPL) apposed to presynaptic glutamatergic synapses from mitral and tufted cell lateral dendrites

Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices 23

Horizontal slices (400-µm-thick) of the MOB were cut parallel to its long axis using a vibratome (Vibratome Series 1000, Ted Pella, Redding, CA). After a period of recovery (30 min) at 30°C, the slices were incubated in a holding bath at room temperature (22°C) until used. For recording, a single brain slice was placed in a perfusion-bath recording chamber mounted on a microscope stage and maintained at 30 ± 0.5°C. Slices were submerged in

Visually guided recordings from neurons in the mitral cell layer or granule cell layer were made with near-infrared differential interference contrast (NIR DIC) optics, waterimmersion objectives, and a BX50WI microscope (Olympus Optical, Tokyo) (Stuart et al. 1993). NIR transillumination was at 900 nm (filter transmission, 850–950 nm) concentric with the objective and optimized for DIC. A 0.25-in CCD camera (CCD 100, Dage, Stamford, CT), fitted with a 3-to-1 zooming coupler (Optem, Fairport, NY) was used. Contrast was enhanced in real-time using an image processor (Model 794, Hughes Aircraft Company, Carlsbad, CA) and the image was displayed on a monochrome monitor (Dage HR120, Dage-

Recordings were made using conventional whole cell patch-clamp methods. Recording pipettes were pulled on a Flaming-Brown P-97 puller (Sutter Instrument, Novato, CA) from standard-wall filamented 1.5-mm-diameter borosilicate glass; tip diameter was 2–3 µm, tip resistance was 5–8 MΩ. Seal resistance was routinely >1GΩ and liquid junction potential was 9-10 mV; reported measurements were not corrected for this potential. Data were obtained using a Multiclamp 700B amplifier (Molecular Devices, Sunnyvale, CA). Signals were low-pass Bessel filtered at 2 kHz and digitized on computer disc *(*Clampex 10.1, Molecular Devices*)*. Data were also collected through a Digidata 1440A Interface (Molecular Devices) and digitized at 10 kHz. Holding currents were generated under control of the Multiclamp 700B Commander. Membrane resistance was calculated from the amount of steady-state current

To allow histological confirmation of recorded cells, biocytin (0.05% to 0.1%, Molecular Probes) was added to the pipette solution in some experiments. The presence of biocytin had no evident effect on neuronal electrophysiology. After recording, slices were fixed overnight in phosphate-buffered 4% paraformaldehyde at 4°C. Slices were incubated with Cy3-conjugated Streptavidin (Jackson ImmunoResearch Laboratories) and processed for later visualization and cell identification with laser-scanning confocal microscopy (FluoView confocal microscope, Olympus Instruments, Long Beach, CA) as described previously

Since the axon of each olfactory receptor neuron projected through the olfactory nerve layer, it was possible in the MOB slice preparation to mimic olfactory stimulation by electrically stimulating afferent inputs. The olfactory nerve layer was focally stimulated with a bipolar electrode constructed from a pair of twisted stainless-steel wires (70 µm), insulated except for bluntly cut tips. The tips of the wires were placed tangentially along the peripheral surface of the olfactory nerve layer, slightly rostral to the estimated location of the recorded MC. Stimuli were isolated monophasic square wave pulses (10–200 µA in amplitude, 0.1 ms in duration) delivered by a Grass S8800 stimulator (Astro-Med, West Warwick, RI) and an

The ACSF used for perfusion of slices in the recording bath consisted of (in mM) 120 NaCl, 3 KCl, 1.3 CaCl2, 1.3 MgSO4, 10 glucose, 25 NaHCO3, and 5 *N,N*-bis(2-hydroxyethyl)-2-

required to hyperpolarize the cell by 10 mV, typically from -60 mV to -70 mV.

isolated constant current source (Grass PSIU6, Astro-Med).

ACSF flowing at 2.5–3 ml/min.

MTI, Michigan City, IN).

(Puche and Shipley 2001).

(van den Pol, 1995). mGluR5 may mediate, at least in part, responses of granule cells to glutamatergic inputs from mitral and tufted cells. Granule cells in the granule cell layer do not express mGluR1 (van den Pol, 1995; Heinbockel et al., 2004). Immunocytochemistry shows only weak staining for mGluR1 (Martin et al. 1992; Sahara et al. 2001; van den Pol 1995), and in situ hybridization reveals faint mGluR1 expression (Shigemoto et al. 1992). Instead, granule cells may express low to moderate levels of mGluR2 (Group II, Ohishi et al., 1993, 1998), and low levels of mGluR4 and mGluR7 (Group III; Kinzie et al., 1995; Ohishi et al., 1995; Saugstad, 1997; Wada et al., 1998).

Given the abundant expression in MOB neurons and in light of the limited knowledge of mGluR function, a pressing issue in the organization and operation of the olfactory system is the analysis of mGluR actions on adult MOB neurons and upon MOB network function. For several reasons, the MOB is an ideal platform for investigating how mGluRs modulate neural network dynamics to enhance sensory processing. (1) Output neurons in the MOB receive direct sensory input, even though this input is strongly modified by intrinsic inhibitory interneurons that provide two discrete tiers for lateral interactions, namely, in the glomerular layer and in the external plexiform layer. (2) Vast cortical and subcortical centrifugal inputs project to the MOB and modulate local circuit processing at multiple levels. (3) The structural organization and the diverse neuronal input-output relationships make the MOB preparation distinctly different from hippocampus, amygdala, neocortex, and cerebellum for addressing functional questions of mGluR modulation in the brain. mGluRs in these and other brain regions are involved in diseases such as stroke, epilepsy, Alzheimer's, Parkinson's and Huntington's disease, as well as in pain, schizophrenia, and anxiety (Lea and Faden, 2003; Schoepp and Conn, 1993). Consequently, our studies about the mechanisms through which mGluR activation shapes synaptic information in a model neural circuit such as the MOB helps to fill major gaps in our understanding of the functioning of both the olfactory system and other brain regions.

#### **2.2 Main Olfactory Bulb slice preparation, recording and imaging techniques**

In order to prepare acute slices of the MOB, we used commercially available male and female rats (Sprague-Dawley; Zivic Laboratories, Zelienople, PA) and mice (C57Bl/6J, Jackson Laboratory, Bar Harbor, ME) and mGluR1 and mGluR5 mutant mice (C57Bl/6J background) (Chiamulera et al. 2001; Conquet et al. 1994) from our colony. mGluR5 mice were provided as a homozygous mutant line, whereas mGluR1 mice, generated as a lacZ knock-in, were maintained by heterozygous mating and were genotyped by polymerase chain reaction (PCR) of DNA from tail tip digests (Heinbockel et al., 2004). Homozygous mGluR1 mutant mice were also identified phenotypically by their characteristic progressive mobility deficit at several weeks of age (Conquet et al. 1994).

We used conventional immunohistochemical techniques to map the distribution of neurons expressing β-galactosidase (β-gal) in tissue sections harvested from mGluR1 transgenic animals (Heinbockel et al., 2004). Sections were examined under brightfield optics using a Leica DMRX photomicroscope (Leica, Deerfield, IL). Digital microscopy images were captured using a Phase I digital camera (PhaseOne, Northport, NY), sized, and balanced for brightness and contrast using Adobe Photoshop 5.0 (Adobe Systems, San Jose, CA).

Juvenile (21- to 31-day old) rats or mice were decapitated, the MOBs dissected out and immersed in artificial cerebrospinal fluid (ACSF) at 4°C (Heinbockel et al., 2004, 2007a, b).

(van den Pol, 1995). mGluR5 may mediate, at least in part, responses of granule cells to glutamatergic inputs from mitral and tufted cells. Granule cells in the granule cell layer do not express mGluR1 (van den Pol, 1995; Heinbockel et al., 2004). Immunocytochemistry shows only weak staining for mGluR1 (Martin et al. 1992; Sahara et al. 2001; van den Pol 1995), and in situ hybridization reveals faint mGluR1 expression (Shigemoto et al. 1992). Instead, granule cells may express low to moderate levels of mGluR2 (Group II, Ohishi et al., 1993, 1998), and low levels of mGluR4 and mGluR7 (Group III; Kinzie et al., 1995; Ohishi et

Given the abundant expression in MOB neurons and in light of the limited knowledge of mGluR function, a pressing issue in the organization and operation of the olfactory system is the analysis of mGluR actions on adult MOB neurons and upon MOB network function. For several reasons, the MOB is an ideal platform for investigating how mGluRs modulate neural network dynamics to enhance sensory processing. (1) Output neurons in the MOB receive direct sensory input, even though this input is strongly modified by intrinsic inhibitory interneurons that provide two discrete tiers for lateral interactions, namely, in the glomerular layer and in the external plexiform layer. (2) Vast cortical and subcortical centrifugal inputs project to the MOB and modulate local circuit processing at multiple levels. (3) The structural organization and the diverse neuronal input-output relationships make the MOB preparation distinctly different from hippocampus, amygdala, neocortex, and cerebellum for addressing functional questions of mGluR modulation in the brain. mGluRs in these and other brain regions are involved in diseases such as stroke, epilepsy, Alzheimer's, Parkinson's and Huntington's disease, as well as in pain, schizophrenia, and anxiety (Lea and Faden, 2003; Schoepp and Conn, 1993). Consequently, our studies about the mechanisms through which mGluR activation shapes synaptic information in a model neural circuit such as the MOB helps to fill major gaps in our understanding of the

al., 1995; Saugstad, 1997; Wada et al., 1998).

functioning of both the olfactory system and other brain regions.

mobility deficit at several weeks of age (Conquet et al. 1994).

**2.2 Main Olfactory Bulb slice preparation, recording and imaging techniques**

In order to prepare acute slices of the MOB, we used commercially available male and female rats (Sprague-Dawley; Zivic Laboratories, Zelienople, PA) and mice (C57Bl/6J, Jackson Laboratory, Bar Harbor, ME) and mGluR1 and mGluR5 mutant mice (C57Bl/6J background) (Chiamulera et al. 2001; Conquet et al. 1994) from our colony. mGluR5 mice were provided as a homozygous mutant line, whereas mGluR1 mice, generated as a lacZ knock-in, were maintained by heterozygous mating and were genotyped by polymerase chain reaction (PCR) of DNA from tail tip digests (Heinbockel et al., 2004). Homozygous mGluR1 mutant mice were also identified phenotypically by their characteristic progressive

We used conventional immunohistochemical techniques to map the distribution of neurons expressing β-galactosidase (β-gal) in tissue sections harvested from mGluR1 transgenic animals (Heinbockel et al., 2004). Sections were examined under brightfield optics using a Leica DMRX photomicroscope (Leica, Deerfield, IL). Digital microscopy images were captured using a Phase I digital camera (PhaseOne, Northport, NY), sized, and balanced for

Juvenile (21- to 31-day old) rats or mice were decapitated, the MOBs dissected out and immersed in artificial cerebrospinal fluid (ACSF) at 4°C (Heinbockel et al., 2004, 2007a, b).

brightness and contrast using Adobe Photoshop 5.0 (Adobe Systems, San Jose, CA).

Horizontal slices (400-µm-thick) of the MOB were cut parallel to its long axis using a vibratome (Vibratome Series 1000, Ted Pella, Redding, CA). After a period of recovery (30 min) at 30°C, the slices were incubated in a holding bath at room temperature (22°C) until used. For recording, a single brain slice was placed in a perfusion-bath recording chamber mounted on a microscope stage and maintained at 30 ± 0.5°C. Slices were submerged in ACSF flowing at 2.5–3 ml/min.

Visually guided recordings from neurons in the mitral cell layer or granule cell layer were made with near-infrared differential interference contrast (NIR DIC) optics, waterimmersion objectives, and a BX50WI microscope (Olympus Optical, Tokyo) (Stuart et al. 1993). NIR transillumination was at 900 nm (filter transmission, 850–950 nm) concentric with the objective and optimized for DIC. A 0.25-in CCD camera (CCD 100, Dage, Stamford, CT), fitted with a 3-to-1 zooming coupler (Optem, Fairport, NY) was used. Contrast was enhanced in real-time using an image processor (Model 794, Hughes Aircraft Company, Carlsbad, CA) and the image was displayed on a monochrome monitor (Dage HR120, Dage-MTI, Michigan City, IN).

Recordings were made using conventional whole cell patch-clamp methods. Recording pipettes were pulled on a Flaming-Brown P-97 puller (Sutter Instrument, Novato, CA) from standard-wall filamented 1.5-mm-diameter borosilicate glass; tip diameter was 2–3 µm, tip resistance was 5–8 MΩ. Seal resistance was routinely >1GΩ and liquid junction potential was 9-10 mV; reported measurements were not corrected for this potential. Data were obtained using a Multiclamp 700B amplifier (Molecular Devices, Sunnyvale, CA). Signals were low-pass Bessel filtered at 2 kHz and digitized on computer disc *(*Clampex 10.1, Molecular Devices*)*. Data were also collected through a Digidata 1440A Interface (Molecular Devices) and digitized at 10 kHz. Holding currents were generated under control of the Multiclamp 700B Commander. Membrane resistance was calculated from the amount of steady-state current required to hyperpolarize the cell by 10 mV, typically from -60 mV to -70 mV.

To allow histological confirmation of recorded cells, biocytin (0.05% to 0.1%, Molecular Probes) was added to the pipette solution in some experiments. The presence of biocytin had no evident effect on neuronal electrophysiology. After recording, slices were fixed overnight in phosphate-buffered 4% paraformaldehyde at 4°C. Slices were incubated with Cy3-conjugated Streptavidin (Jackson ImmunoResearch Laboratories) and processed for later visualization and cell identification with laser-scanning confocal microscopy (FluoView confocal microscope, Olympus Instruments, Long Beach, CA) as described previously (Puche and Shipley 2001).

Since the axon of each olfactory receptor neuron projected through the olfactory nerve layer, it was possible in the MOB slice preparation to mimic olfactory stimulation by electrically stimulating afferent inputs. The olfactory nerve layer was focally stimulated with a bipolar electrode constructed from a pair of twisted stainless-steel wires (70 µm), insulated except for bluntly cut tips. The tips of the wires were placed tangentially along the peripheral surface of the olfactory nerve layer, slightly rostral to the estimated location of the recorded MC. Stimuli were isolated monophasic square wave pulses (10–200 µA in amplitude, 0.1 ms in duration) delivered by a Grass S8800 stimulator (Astro-Med, West Warwick, RI) and an isolated constant current source (Grass PSIU6, Astro-Med).

The ACSF used for perfusion of slices in the recording bath consisted of (in mM) 120 NaCl, 3 KCl, 1.3 CaCl2, 1.3 MgSO4, 10 glucose, 25 NaHCO3, and 5 *N,N*-bis(2-hydroxyethyl)-2-

Electrophysiological Recording and Imaging of Neuronal Signals in Brain Slices 25

Fig. 2. Bath application of the Group I mGluR agonist DHPG activated mitral cells whereas the selective mGluR1 antagonist LY367385 blocked the effect of DHPG (C, D). A, B: Group II and III mGluR agonists had no effect on firing and membrane potential of mitral cells. Modified from Heinbockel et al., 2004 with permission of The American Physiological Society.

were a result of direct activation of mGluR1 on mitral cells as they persisted in the presence of blockers of synaptic transmission (NMDA receptor blocker [DL-2-amino-5 phosphonopentanoic acid (APV)], AMPA receptor blocker [6-cyano-7-nitroquinoxaline-2-3 dione (CNQX)], and GABAA receptor blocker [gabazine]) and/or TTX. The effects of DHPG were blocked by a preferential mGluR1 antagonist, LY367385, which has negligible actions on Group II and III mGluRs, and antagonizes mGluR5 only at concentrations in excess of

**2.4 mGluR-mediated excitation of mitral cells in mGluR5 knockout mice and not in**

The specificity of mGluR agonists and antagonists is limited and can vary with concentration. Therefore, we took advantage of mice with targeted deletion of the mGluR1 receptor gene to further investigate the function of this receptor subtype on mitral cells (Conquet et al. 1994). In this transgenic strain, the mGluR1 gene sequence has been replaced with that for β-galactosidase (β-gal) by homologous recombination. Therefore, in "knockout" (KO) mice β-gal is present only in cells that would normally express mGluR1. We first examined the location of β-gal-positive (β-gal+) neurons in mGluR1 KO and wild-type (WT) littermate mice using immunohistochemistry. As shown in Fig. 3, the distribution of β-gal+

100 μM (Clark et al., 1997; Salt et al., 1999).

**mGluR1 knockout mice**

aminoethanesulfonic acid (BES), 95% O2-5% CO2-saturated, pH 7.27, 300 mOsm (Heinbockel et al., 2004). Recording pipettes were filled with the following solution (in mM) 125 K gluconate, 2 MgCl2, 10 HEPES, 2 Mg2ATP, 0.2 Na3GTP, 1 NaCl, and 0.2 EGTA.

Optical imaging of voltage-sensitive dye signals was used to investigate network activity patterns evoked by stimulation of the lateral olfactory tract (LOT) (Heinbockel et al., 2007a). Slices were harvested as described above for electrophysiological recording. Slices were kept at room temperature in ACSF and stained with the voltage-sensitive dye RH-414 (100 µM; Molecular Probes, Eugene, OR) dissolved in ACSF. A single slice was placed in a static bath containing the dye solution, continuously aerated with 95% O2-5% CO2 for 30–45 min. Subsequently, the stained slice was transferred to an immersion-type recording chamber and continuously perfused at 2 ml/min with ACSF at room temperature. Unbound dye was washed out from stained slices by perfusion with ACSF for ≥15 min prior to optical recording. Methods used for recording voltage-sensitive optical signals were similar to those described in detail by Keller et al., 1998 and Laaris et al., 2007. Light from a 100 W tungstenhalogen lamp was band-limited with interference (540 ± 30 nm band-pass, Omega Optical, Brattleboro, VT) and heat filters. A 10x water-immersion objective (Olympus) was used to collect light from the preparation and projected onto a hexagonal 464 element array of photodiodes (NeuroPlex, OptImaging, Fairfield, CT). An individual photodiode sampled optical signals from a region of ~60 x 60 µm2. Current output from each photodiode was separately converted to voltages, amplified in two separate stages (x1,000), multiplexed, and digitized at 12-bit resolution with an A/D converter. Before digitizing, optical signals were filtered at 500 Hz. Collection of optical signals was at a sampling rate of 1.63 kHz. Data were recorded and saved on a personal computer controlled by NeuroPlex software (OptImaging). Data analysis was performed on an Apple Macintosh computer, using routines developed in Igor (WaveMetrics, Lake Oswego, OR). A custom-designed beam splitting device (Microscope Services, Rockville, MD) was used to simultaneously project the images of the slice and light from light-emitting diodes embedded in the photodiode array onto the image plane of a CCD camera (Dage CCD72, Michigan City, IN). This allowed for the precise identification of the regions in the slice from which optical recordings were collected. Thus, this setup helped to correlate voltage-sensitive dye optical signals with the laminar borders of different layers in MOB slices (Figs. 1, 7).

#### **2.3 Activation of mitral cells in the Main Olfactory Bulb by a selective group I mGluR agonist**