Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit
[['Lateral inhibition is one of the critical mechanisms underlying responses to sensory neurons [1] , but the detailed mechanisms at the network level in the olfactory system are not clear [e . g . 2] .', 'In the Limulus eye [1] and the cat retina [3] it mediates contrast enhancement between areas of differing illumination .', 'It has also been found in the auditory pathway ( reviewed in [4] ) and the somatosensory system [5] .', 'In the olfactory system , the clearest evidence for lateral inhibition is the interaction between mitral cells in the olfactory bulb , mediated through inhibitory granule cells [6]–[7] and periglomerular cells [8] .', 'The possible underlying circuits and their computational properties have been widely investigated experimentally [9]–[11] especially in terms of odor selectivity and dynamics of mitral cell responses [12]–[14] .', 'A major problem in interpreting the experimental findings in vivo is that they are usually obtained in single cells or in small randomly selected sets of cells , whereas a clear understanding of fundamental processes , such as the spatio-temporal organization of the mitral-granule cell network , requires simultaneous recording from a relevant subset of cells activated by any given odor .', 'The functional effects of network-wide processes , in relation to the patterns of glomeruli activated by different odors , therefore remain relatively unknown and extremely difficult to explore experimentally .', 'To gain insight into this problem we have focused on mitral-granule cell interactions , the best understood circuit in the olfactory bulb .', 'For this purpose we have constructed a biophysical network model of multicompartment mitral and granule cells with connections similar to those in the real olfactory bulb .', 'As input we have used the activation of individual glomeruli by a large set of odors identified by intrinsic imaging [15] .', 'This model has allowed us to investigate several fundamental questions:', '1 ) How does the network self-organize and modulate mitral cell responses to different odor molecules ?', 'Lateral inhibition has been suggested to be the primary mechanism .', 'However , experimentally the focus is almost exclusively on individual mitral and granule cell interactions , and whether lateral inhibition is able to shape network-wide connectivity is not known .', '2 ) How does the precise timing of mitral cell action potentials subject to lateral inhibition relate to the sniff cycle ( as shown in [16] ) ?', 'This appears to be one of the critical processes affecting responses in awake mice [2] , but the mechanisms responsible for the firing behavior at the network level are not understood .', '3 ) Why is the olfactory bulb network connectivity sparse and distributed ( as shown in [10] and [17] ) ?', 'There are no experimental data for describing the underlying mechanism .', 'We have previously proposed a physiologically plausible process using a small network with simulated odors and all-to-all possible connectivity [18] .', 'Its validity for more realistic odor inputs , network size , and intrinsically sparse connectivity required testing , which was carried out in the present study .', 'The results show that:', '1 ) Lateral inhibition mediated through broadly tuned granule cells results in mitral cell output that reflects the spatial distribution of the glomerular input and the temporal structure of the inhibitory processing .', 'Together , the mitral-granule cell circuit operates to generate a unique spatiotemporal representation for each odor .', '2 ) Odor identity can emerge from a single sniff as a specific distribution of spikes in which each mitral cell makes its own contribution according to the specific type of input it receives and the network of granule cells it activates .', '3 ) The mitral<->granule cell interactions through dendrodendritic synapses can account for the distributed network connectivity observed experimentally .', 'We discuss how these specific predictions from the model provide new hypotheses for experimental testing .'], ['In the experiments , 72 different odor molecules were used for stimulation .', 'Individual glomeruli in each of the clusters illustrated in Fig . 1B were differentially activated by these odors , as shown in the table of Fig . 2 .', 'Each odor induced activity in a group of glomeruli , usually belonging to the same cluster but often with outliers .', 'Mori et al . ( 2006 ) [15] classified these responses into 4 different intensity levels: very strong , strong , moderate and weak , represented by the different size circles in their table .', 'Since it can be assumed that the intensities reflected the postsynaptic dendritic depolarization of the mitral cell glomerular tufts [29] , in our model we used the response levels to set 4 different levels for the peak synaptic excitatory conductance activated in the glomerular tufts .', 'The aggregate activation of many OSN inputs onto a given tuft with a single EPSP was implemented using a double exponential conductance change with 20 and 200 ms for rise and decay time , respectively [18] , [30] .', 'To represent the range of intensities with adequate sensitivity down to the weakest concentration without saturating the network at the highest concentration , we set the peak conductance sensitivity to give suprathreshold responses to levels 3 and 4 .', 'We simulated each of the 72 odor responses [15] in Fig . 2 in order to analyze and compare the network responses .', 'Key aspects of the network properties are illustrated by three examples , highlighted in the table in Fig . 2: a relatively strong glomerular response ( to octanal , in cluster B ) , a relatively widely distributed odor response ( to k3-3 , in clusters B and C-D ) , and a relatively weak response ( to ( + ) -Cvn , in clusters B and D ) .', 'Histograms in Fig . 2B show the relative strengths and distribution of the glomerular responses , which are the input magnitudes used to activate the mitral cells .', 'The network self-organization in these representative cases is illustrated in Fig . 3 , where we show the raster plots for mitral and granule cell spike discharges during the first seven seconds of odor presentation for three odors .', 'During a moderate glomerular input ( e . g . Fig . 3A , odor ( + ) -Cvn ) , the spiking response dynamics of the most active mitral cells ( around site 240 ) showed the progressive appearance of a bursting pattern , accompanied by weak inhibition of surrounding mitral cells to an extent of approximately 1 . 5 mm on either side of site 240 ( note the lighter area in the mitral cells raster plot after the first 2 sec of simulation ) , reflecting the extent of the lateral dendrites of the activated mitral cells .', 'Bursts were aligned with odor input activation ( sniffs ) , with most spikes at the onset of the sniff and fewer spikes later in the sniff cycle .', 'The mitral cell inhibition was correlated with activation of the granule to mitral cell inhibitory synapses , as shown by the firing of granule cells in Fig . 3A ( right panel ) , again reflecting the 1 . 5 mm extent of the mitral cell lateral dendrites .', 'This granule cell spiking produced the mitral cell inhibition , as evidenced by the similarity between the granule cell spiking population and the extent of the mitral cell inhibition in Fig . 3A .', 'These results demonstrate several basic properties of the network response to a glomerular activity pattern .', 'As expected , the odor drives the mitral cells receiving direct input from the activated glomeruli .', 'This defines the mitral cell cluster related to the activated glomerular cluster .', 'A new property shown by this realistic simulation is that the action potentials in the lateral dendrites of the activated mitral cells bring about synaptic excitation of the connected granule cells , which elicits spiking in these cells .', 'The sharp cutoff of the granule cell spiking at 1 . 5 mm on either side of site 240 provides a novel indication of precisely the extents of the lateral dendrites of the driven mitral cells .', 'This spatial pattern may be considered a 1-dimensional representation of 2-dimensional lateral , or surround , inhibition .', 'The surround inhibition in this case is relatively weak because of the relatively weak input and the consequent weak granule cells activity .', 'These results thus indicate an unexpectedly extensive engagement of the granule cells in the dorsal olfactory bulb area by activation of just a few glomeruli .', 'As we will discuss later , this is not in contrast with the discontinuous mosaic of clusters of mitral-granule cell “glomerular units” shown by tracing experiments [10] but , rather , it suggests possible constraints on the transsynaptic virus transport mechanisms .', 'Very strong localized odor activation produced much stronger network responses .', 'As shown for octanal , in Fig . 3B , the activated mitral cells ( sites 430–490 ) developed an intense intermittent bursting pattern .', 'In comparison with ( + ) -Cvn , the lateral inhibition was much stronger , as shown by the complete lack of spikes in the surrounding mitral cells .', 'This strong bursting and surround inhibitory activity is explained , respectively , by the intense activation of the granule cell population at the site of the activated mitral cells ( through feedback inhibition ) and on either side ( through lateral inhibition ) .', 'In this example the same properties evidenced in the moderately activated network are seen intensified .', 'The glomerular cluster is larger and more strongly activated , leading to a larger and more intense mitral cell response .', 'The granule cell response is correspondingly more intense and widespread , and is associated with virtually complete inhibition of surrounding mitral cells .', 'The discharge patterns in both cases include bursting , occasionally overwhelmed by the strength of the activation .', 'To contrast with these examples of localized glomerular input , in Fig . 3C we illustrate a third example , odor k3-3 ( an aliphatic ketone ) , where a less localized , more distributed pattern of glomerular activation involved the activation of two main groups of mitral cells , stronger at the 460–490 site than at the 240–270 site .', 'This provided the opportunity to analyze the interactions of the lateral inhibition elicited by the two sites .', 'The mitral cell discharges ( Fig . 3C , left ) at both of these sites showed patterns of oscillatory bursts .', 'Because of the separation of the two sites , the lateral dendrites of the two mitral cell populations spanned the entire network , thus activating the entire granule cell population , developing relatively uniform bursting spike discharges ( Fig . 3C , right ) .', 'This led to strong lateral inhibition of nearly all the mitral cells not driven by the input .', 'Note however that this inhibition was slightly weaker over the network from sites 0 to 240 , reflecting the slightly weaker input to the mitral cells at the 240 site .', 'This indicates that the inhibition of the mitral cells reflects a balance between the amount of excitation of the mitral cell lateral dendrites and the corresponding excitation of the granule cells .', 'Comparison of these three responses to odor stimulation thus indicates several basic properties underlying odor coding in the olfactory bulb network .', 'The three different odors are initially represented by the three different , spatially restricted , distributions of activated glomeruli .', 'These spatial patterns are processed by the more extensive spatial distributions of lateral inhibition brought about by the interactions with broadly tuned granule cells .', 'The result is a mitral cell output reflecting the spatial distribution of the glomerular input and the temporal structure of the inhibitory processing , which together represent a unique spatiotemporal representation for each odor .', 'The spiking activity shown in Fig . 3 provides the driving force for the formation of the synaptic conductances that represent the odor training of the network in laying down the neural substrate for an odor perception [18] .', 'The spiking activity in turn is driven by the weights of the tuft and dendrodendritic synapses .', 'In order to visualize and analyze the distribution of the synaptic weights , we arranged plots of the mitral to granule cell excitatory weights and granule to mitral inhibitory weights as shown in Fig . 4 .', 'Results for all 72 odors are reported in Fig . S1 .', 'In each panel , the top histogram shows the relative strengths and distribution of the glomerular responses .', 'The middle graph shows the final weight configuration , after a 10 sec simulation of odor input , of the excitatory synapses from mitral to granule cells , with the 500 mitral cells on the abscissa and the 10 , 000 granule cells on the ordinate .', 'The background control is illustrated in Fig . 4A , in which there is no odor input; weights of mitral to granule cell ( excitatory ) synapses show only random low values of peak conductance , generated by the background input ( Fig . 4A , middle ) ; this activity is not enough to generate any potentiation of the inhibitory synapses ( Fig . 4A , bottom ) , which stay at their initial value of zero .', 'The moderately intense focal glomerular activation by ( + ) -Cvn ( Fig . 4B , top ) at mitral cell sites 240–250 elicited action potentials that propagated through their secondary dendrites .', 'This activated the excitatory conductances of the mitral to granule cell synapses between granule cell sites 2 , 000–8 , 000 ( Fig . 4B , middle , yellow areas ) .', 'Note the slight increase in excitatory weights at mitral cell sites 400–590; this reflects the summation of the very weak glomerular input with the background activity ) .', 'This excitatory activity in turn activated granule cells and , thus , potentiation of granule-to-mitral cell inhibitory synaptic conductances ( Fig . 4B , bottom ) .', 'Yellow to white pseudocolors represent synaptic weights fully potentiated , and in this case they are distributed throughout the extent of the most active mitral cell lateral dendrites .', 'Comparison with the spiking data of Fig . 3A shows a close correlation between the location and strength of the synaptic weights and the patterns of mitral and granule cell spiking .', 'For example , the thin horizontal bands of potentiated inhibitory weights ( at mitral cell sites 240–250 in the bottom graph of Fig . 4B ) correspond to granule cells connected to the most active mitral cells; the strong feedback inhibition generated by their activation is responsible for the emergence of the bursting behavior observed in Fig . 3A .', 'The analysis of synaptic weight distribution in the network for the case of a much stronger odor is shown in Fig . 4C ( octanal ) .', 'In this case , the wider range of potentiated excitatory synapses ( Fig . 4C , middle ) generated a strong and widespread potentiation of inhibitory weights ( Fig . 4C , bottom ) , with an evident inhibition of the mitral cells surrounding the most active ones , as indicated by the darker purple area in the excitatory weights map in the range of mitral cells 280–400 and 490–120 .', 'Note that this correlates with the strong lateral inhibition shown in the left panels of Fig . 3B .', 'For a more distributed input , such as odor k3-3 ( Fig . 4D , top ) , the excitatory weights reflected the glomerular activation at the two sites ( Fig . 4D , middle ) , and the consequent strong inhibitory weights on the most active mitral cells ( Fig . 4D , bottom ) .', 'In this case , the distributed input involved more or less synaptic plasticity of the entire network of inhibitory weights , with the flanking weights reflecting the overlap of mitral cell lateral dendrites , for example granule cells in the range around sites 2300 , 6200 , and 8000 .', 'The overall effect on network activity was a widespread bursting behavior that involved the entire granule cell network ( see Fig . 3C , right ) .', 'The formation of different excitatory and inhibitory clusters in response to all odors is shown in Fig . S1 .', 'In summary , these results demonstrated how the learning of different odors can generate , through the differential activation of distributed glomeruli , widely different network behavior with:', 'i ) distinct firing properties involving a variable population of granule cells ,', 'ii ) an emergent oscillatory bursting behavior that can span a large portion of the olfactory bulb , and', 'iii ) a powerful lateral inhibition surrounding the most active glomeruli .', 'The relation between lateral inhibition and glomerular activation is critical to odor representation and processing .', 'To understand this property in a more quantitative way , for each odor we calculated the average inhibitory conductance on any given mitral cell as a function of the input strength .', 'We were particularly interested in mitral cells receiving a weak input ( i . e . an odor strength of 1 or 2 ) .', 'For any given odor , these cells correspond to flanking components .', 'Typical cases can be identified in the histograms representing odor input for each odor , for example mitral cell 292 for Eug ( Fig . 5A , left ) , mitral cell 422 for octanal ( Fig . 4C , top ) , and mitral cell 235 for Gua ( Fig . 5A , right ) .', 'They all receive a weak input , but the input of their respective neighbors is quite different , as can be seen in the different histograms for these odors: weak neighbors for Eug , very strong for octanal , and medium for Gua .', 'Given the low connection probability ( 10% ) and the spatially distributed glomerular activation , the emergence of a significant lateral inhibition cannot be taken for granted , and it would be difficult to explore experimentally .', 'In order to analyze the relation between input strength and the effect of the lateral inhibition it generates , mitral cells activated by any given odor were grouped in two input classes , low ( strength 1–2 ) and strong ( strength 3–4 ) .', 'For each odor and each group , the average overall inhibitory conductance was then calculated from the final weight configuration at the end of the 10 sec simulation , including both feedback and lateral actions .', 'The results are shown in Fig . 5B , as a function of the proportion of cells receiving a strong input .', 'Note that without any lateral inhibition the peak inhibitory conductance on mitral cells receiving a weak input ( Fig . 5B , red circles ) would be 0 , since their firing rate would be too low to generate any feedback inhibition .', 'However , the results show that lateral inhibition is developed as an odor activates a small proportion of mitral cells with a strong input .', 'The differential effect on the weaker flanking components can be clearly seen in Fig . 5C , where we show the somatic membrane potential during the first few seconds of a simulation for three mitral cells .', 'As the weights develop , a strong odor , such as octanal , will completely silence a flanking component , such as cell 422 ( Fig . 5C , top ) , whereas a progressively lower effect can be seen for cell 235 during presentation of the less strong Gua ( Fig . 5C , middle ) , and for cell 292 during presentation of the weak Eug ( Fig . 5C , bottom ) .', 'These results demonstrate that significant lateral inhibition can be developed by any given odor that is able to generate strong activity in a relatively small proportion of mitral cells , independently of their spatial location ( but within the reach of their lateral dendrites ) .', 'The olfactory code relayed from mitral cells to the cortex for odor recognition is sculpted by the activity of granule cells , which are ideally positioned for this role in the olfactory bulb circuit .', 'The granule-mitral cell connection probability can thus be expected to have a paramount role in modulating mitral cell firing .', 'Experimentally , the average probability with which a granule cell forms synapses with mitral cells is unknown , although there are findings suggesting that , in general , it is sparse and spatially distributed ( see Discussion ) .', 'To test to what extent a sparse connectivity can significantly modulate mitral cell firing we carried out additional simulations for odor k3-3 using different connection probabilities between granule and mitral cells ( Fig . 6 ) .', 'The configuration of inhibitory weights after a 10 sec simulation using an average maximum potential connectivity of 2 , 5 , and 15% is shown in Fig . 6A .', 'In comparing the results obtained with the control value of 10% ( Fig . 4D ) , with those obtained using a higher connectivity ( Fig . 6A , 15% ) , we observed a sharp difference in the clustering of high synaptic weights around the two regions with active mitral cells with respect to those in other regions .', 'This difference tended to be smaller with 5% connection probabilities and almost disappeared with 2% connectivity .', 'We hypothesized that these differences may result in a significant change of mitral cell firing properties .', 'We tested this in the model by analyzing the average instantaneous firing rate , which is the most relevant parameter to characterize and understand mitral cell responses to an odor sniff .', 'Without GC all mitral cells were more or less active , depending on background activity and/or odor input ( Fig . 6B , top ) .', 'The background activity of mitral cells not receiving any odor input ( e . g . cells in the 325–425 range ) was much reduced already with only 2% connectivity , and almost completely suppressed with connection probabilities above 5% .', 'A higher connection probability ( 10 and 15% ) also suppressed firing of flanking components , a typical contrast enhancement effect .', 'Most interestingly , the firing rate of strongly activated mitral cells was little affected by connection probability .', 'This was more clearly evident from the analysis of their ISI distribution in the range of 10–50 ms , as shown in Fig . 6C ( left ) for 10% connection probability .', 'The two distributions ( with or without granule cells in the network ) were statistically indistinguishable ( Mann-Whitney Rank Sum Test , p\u200a=\u200a0 . 88 ) , in contrast with the distributions of 50–200 ms ISIs ( Fig . 6C , right , Mann-Whitney Rank Sum Test p\u200a=\u200a0 . 021 ) .', 'This effect was very robust with changes in connection probability ( Fig . 6D ) .', 'The distributions of 50–200 ms ISIs with or without GC in the network were significantly different in all cases except 2% connectivity ( Mann-Whitney Rank Sum Test p\u200a=\u200a0 . 115 ) .', 'These model results predict that a sparse granule-mitral connectivity will be able to significantly affect mitral cell firing in two different ways:', '1 ) suppressing flanking or weaker components , and', '2 ) changing the firing pattern structure of the stronger components .', 'The overall computational effects for odor coding of these mechanism will be investigated in a future work .', 'The relative importance of the feedback and lateral inhibition generated by a sparsely connected network of granule cells is poorly understood .', 'We have shown how they can modulate the overall mitral cell firing structure by different odors ( Fig .', '3 ) and under different connectivity ( Fig . 6 ) .', 'To test their effects on different odor concentrations we carried out , without granule cell synapses in the network , a 50 sec long simulation of odor k3-3 .', 'Odor strength was progressively increased every 5 sec , from 0 ( no odor ) to 1 ( the maximum strength used in all simulations ) in steps of 0 . 1 .', 'The results , shown in Fig . 7 ( top ) for mitral cells 420–500 , show a rather high and diffuse activity of all mitral cells , with those receiving relatively lower inputs ( <0 . 4 ) practically indistinguishable from the background activity ( average S/N\u200a=\u200a0 . 25 dB ) .', 'We then repeated the simulation including granule cells and starting from the weights configuration obtained after odor learning ( Fig . 4D ) .', 'The results , illustrated in Fig . 7 , showed a surprising effect of feedback inhibition for low odor concentrations , which selectively suppressed the instantaneous firing activity of the most active mitral cells ( e . g . cells 475–484 ) , making them clearly distinguishable ( average S/N\u200a=\u200a−13 . 1 dB ) from neighbor cells activated by the background noise ( e . g . cells 430–439 ) or by weaker components ( 486–500 ) .', 'These results suggest that lateral and feedback inhibition work together , in a complementary way , to enhance the contrast between an odor signal and the background noise over a wide range of odor concentrations , including subthreshold inputs .', 'We wished to test the network model against physiological recordings of mitral cells responding to odor stimuli .', 'We have previously shown that a reduced network model gives generic mitral cell responses consistent with those reported for different odors in a homologous series [7] .', 'A more detailed , although qualitative , comparison with the present model can be carried out with the data reported in [16] .', 'These authors found that the temporal firing structure of mitral cells in response to a sniff cycle is very precise , with different mitral cells responding with different onset times and firing rate to the same odor ( Fig . 8A ) .', 'We were particularly interested in understanding the reasons for the temporal firing distribution of the response with respect to a sniff cycle .', 'The experimental findings ( Fig . 6A , see [16] ) are quite clear from this point of view: Different cells respond to the same odor with different timing .', 'This is increasingly recognized as an important computational property for odor coding and discrimination ( e . g . [1] , [31] , [32] ) , but the underlying processes are unknown .', 'In order to investigate this issue , which is otherwise experimentally limited within a network framework , we calculated the average distribution of spike times of all the mitral cells activated during 70 sniffs by a strong , a medium , and a weak odor .', 'As shown in Fig . 8B , the distributions were different , and all were clearly distinguishable from the “no odor” condition ( Fig . 8B , gray line ) .', 'A more specific analysis of mitral cells activated by k3-3 , obtained by grouping the mitral cells according to the strength of their input ( Fig . 8C ) , suggested that the different distributions are correlated with the interaction between the odor input strength and granule cell activity .', 'Mitral cells receiving a weak or very weak input ( Fig . 8C , top two panels ) are mostly modulated by weak lateral inhibition generated later in the sniff cycle , whereas cells receiving medium and strong inputs ( Fig . 8C , bottom two panels ) generate earlier strong inhibition that impacts the response latency .', 'Taken together these results indicate how odor identity could emerge from a single sniff as a specific distribution of spikes composed of spatially and temporally positive or negative contributions ( with respect to the “no odor” condition ) from all the mitral cells activated by the odor , each mitral cell making its own contribution according to the specific type of input it receives and the underlying network of granule cells it activates .', 'It is also of interest to compare the model properties with recent experimental studies which revealed sparse and segregated lateral connectivity between mitral and granule cells [10] , [17] , [33] , as illustrated in Fig . 9 ( left panel ) .', 'It showed the connectivity in terms of widely distributed columns of granule cells labeled by widely distributed clusters of mitral cells , formed during the lifetime of the animal .', 'It therefore represents the “maximum” average connectivity that can be obtained between any mitral and any granule cell in the network .', 'During odor presentation , each synaptic weight will independently follow the synaptic plasticity rule to increase/decrease its value , according to the local spiking activity , shaping the actual network connectivity .', 'We have previously shown [18] that this process , through the interaction among odor inputs , action potential backpropagation , and dendrodendritic synapses can generate the kind of distributed interconnectivity observed experimentally [10] .', 'We tested whether our dynamic circuit model could produce similar patterns .', 'As a typical example , we considered all mitral cells activated by odor k3-3 and calculated the peak inhibitory synaptic conductance along their lateral dendrites after odor learning .', 'This is equivalent to the experimental protocol used in the PRV injection experiments , where a few nearby glomeruli are injected with the virus to give labeling of the widely distributed granule cell clusters .', 'Although it contained no inherent cluster connectivity , as shown in Fig . 9 ( right ) , the circuit model generated narrow labeled granule cell patterns similar to those observed experimentally .', 'This included the strong clustering of inhibitory synapses below the most active mitral cells in the model ( Fig . 9 , right ) , corresponding to a region of intense staining of mitral and granule cells in the experiments ( Fig . 9 , left ) , and the weaker clustering of granule cells in a region with no active mitral cells ( Fig . 9 , indicated in orange ) .', 'In the top traces in Fig . 9 , right , it can be seen that , associated with this pattern , the soma of strongly activated mitral cells showed strong action potential firing , whereas the invasion of distal lateral dendrites was weaker .', 'This suggests that the granule cell inhibitory synapses , once established , can gate the backpropagation of a train of action potentials in a precise location along a mitral cell lateral dendrite as suggested experimentally ( e . g . [9] , [25] , [34] ) .', 'These results confirm our previous suggestion [18] , [26] that the sparse and distributed patterns observed experimentally can emerge from the interaction between mitral and granule cell activity during an odor presentation .'], ['Lateral inhibition of mitral and tufted cells has been firmly established by many experiments at the single cell level over the past nearly 50 years [6]–[7] , [35]–[41] , providing evidence that excitation of mitral cells leads to dendrodendritic activation of the granule cells , which then brings about feedback and lateral inhibition of the mitral cells .', 'The interpretation of these studies has been supported by a number of realistic ( e . g . [24] , [26] ) , simplified ( e . g . [39] , [42] ) , or artificial ( e . g . [43]–[44] ) computational models .', 'The present study has enabled us to extend the characterization of lateral inhibition from the single cell to a realistic network level , and provided a clearer representation of the sharpening by the lateral inhibition of the neural response to different odor inputs .', 'We have shown that activity-dependent mechanisms are capable of sculpting the network , leading to the formation of dendrodendritic synaptic clusters in a large , sparsely-connected network .', 'The independent evolution of each synaptic weight , according to the local dendritic spiking activity , will shape the actual network by forming the widespread mosaic of clustered connectivity observed experimentally [10] .', 'This mechanism may be in effect , for example , during the important process of merging newborn granule cells , with their facilitated synaptic plasticity [45] , into the existing bulb network to drive stimulus response decorrelation [44] .', 'The spatial extent of the lateral inhibition was correlated with the strength of mitral cell activation; stronger glomerular activation results in more extensive lateral inhibition .', 'Stronger antidromic activation of mitral cells has been known to be associated with more extensive lateral inhibition of surrounding mitral cells [46] .', 'The model indicates how orthodromic mitral cell activation from the glomeruli produces the same result in the mitral cell ensemble .', 'In relation to recent experimental findings [16] , the model explains in terms of different input strengths the results showing different timing among the mitral cells activated by an odor , and supports the suggestion that the time-to-first-spike can be a critical property for odor identification as the mitral cells project their ensemble response to olfactory cortex [31]
['Introduction', 'Results', 'Discussion', 'Materials and Methods']
['In the olfactory bulb , lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing , thereby shaping the representation of input odorants .', 'Current experimental techniques , however , do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally .', 'To address this critical step in the neural basis of odor recognition , we built a biophysical network model of mitral and granule cells , corresponding to 1/100th of the real system in the rat , and used direct experimental imaging data of glomeruli activated by various odors .', 'The model allows the systematic investigation and generation of testable hypotheses of the functional mechanisms underlying odor representation in the olfactory bulb circuit .', 'Specifically , we demonstrate that lateral inhibition emerges within the olfactory bulb network through recurrent dendrodendritic synapses when constrained by a range of balanced excitatory and inhibitory conductances .', 'We find that the spatio-temporal dynamics of lateral inhibition plays a critical role in building the glomerular-related cell clusters observed in experiments , through the modulation of synaptic weights during odor training .', 'Lateral inhibition also mediates the development of sparse and synchronized spiking patterns of mitral cells related to odor inputs within the network , with the frequency of these synchronized spiking patterns also modulated by the sniff cycle .']
['In the paper we address the role of lateral inhibition in a neuronal network .', 'It is an essential and widespread mechanism of neural processing that has been demonstrated in many brain systems .', 'A key finding that would reveal how and to what extent it can modulate input signals and give rise to some form of perception would involve network-wide recording of individual cells during in vivo behavioral experiments .', 'While this problem has been intensely investigated , it is beyond current methods to record from a reasonable set of cells experimentally to decipher the emergent properties and behavior of the network , leaving the underlying computational and functional roles of lateral inhibition still poorly understood .', 'We addressed this problem using a large-scale model of the olfactory bulb .', 'The model demonstrates how lateral inhibition modulates the evolving dynamics of the olfactory bulb network , generating mitral and granule cell responses that account for critical experimental findings .', 'It also suggests how odor identity can be represented by a combination of temporal and spatial patterns of mitral cell activity , with both feedforward excitation and lateral inhibition via dendrodendritic synapses as the underlying mechanisms facilitating network self-organization and the emergence of synchronized oscillations .']
['circuit models', 'computational neuroscience', 'olfactory system', 'biology', 'sensory systems', 'neuroscience', 'coding mechanisms']