Computational neuroscience

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.[1][2][3][4]

Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous.[5] The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.[6]

Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory;[7][8][9][10] although mutual inspiration exists and sometimes there is no strict limit between fields,[11][12][13][14] with model abstraction in computational neuroscience depending on research scope and the granularity at which biological entities are analyzed.

Models in theoretical neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, nuclei, all the way up to psychological faculties like memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.


The term 'computational neuroscience' was introduced by Eric L. Schwartz, who organized a conference, held in 1985 in Carmel, California, at the request of the Systems Development Foundation to provide a summary of the current status of a field which until that point was referred to by a variety of names, such as neural modeling, brain theory and neural networks. The proceedings of this definitional meeting were published in 1990 as the book Computational Neuroscience.[15] The first of the annual open international meetings focused on Computational Neuroscience was organized by James M. Bower and John Miller in San Francisco, California in 1989.[16] The first graduate educational program in computational neuroscience was organized as the Computational and Neural Systems Ph.D. program at the California Institute of Technology in 1985.

The early historical roots of the field can be traced to the work of people including Louis Lapicque, Hodgkin & Huxley, Hubel and Wiesel, and David Marr. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907,[17] a model still popular for artificial neural networks studies because of its simplicity (see a recent review[18]).


Major topics[edit]


Single-neuron modeling[edit]





Modeling the richness of biophysical properties on the single-neuron scale can supply mechanisms that serve as the building blocks for network dynamics.[23] However, detailed neuron descriptions are computationally expensive and this can handicap the pursuit of realistic network investigations, where many neurons need to be simulated. As a result, researchers that study large neural circuits typically represent each neuron and synapse with an artificially simple model, ignoring much of the biological detail. Hence there is a drive to produce simplified neuron models that can retain significant biological fidelity at a low computational overhead. Algorithms have been developed to produce faithful, faster running, simplified surrogate neuron models from computationally expensive, detailed neuron models.[24]

Development, axonal patterning, and guidance[edit]



Sensory processing[edit]

Early models on sensory processing understood within a theoretical framework are credited to Horace Barlow. Somewhat similar to the minimal wiring hypothesis described in the preceding section, Barlow understood the processing of the early sensory systems to be a form of efficient coding, where the neurons encoded information which minimized the number of spikes. Experimental and computational work have since supported this hypothesis in one form or another. For the example of visual processing, efficient coding is manifested in the forms of efficient spatial coding, color coding, temporal/motion coding, stereo coding, and combinations of them.[26]

Further along the visual pathway, even the efficiently coded visual information is too much for the capacity of the information bottleneck, the visual attentional bottleneck.[27] A subsequent theory, V1 Saliency Hypothesis (V1SH), has been developed on exogenous attentional selection of a fraction of visual input for further processing, guided by a bottom-up saliency map in the primary visual cortex.[28]


Motor control[edit]

Many models of the way the brain controls movement have been developed. This includes models of processing in the brain such as the cerebellum's role for error correction, skill learning in motor cortex and the basal ganglia, or the control of the vestibulo ocular reflex. This also includes many normative models, such as those of the Bayesian or optimal control flavor which are built on the idea that the brain efficiently solves its problems.

Memory and synaptic plasticity[edit]

Earlier models of memory are primarily based on the postulates of Hebbian learning. Biologically relevant models such as Hopfield net have been developed to address the properties of associative (also known as "content-addressable") style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium- and long-term memory, localizing in the hippocampus. Models of working memory, relying on theories of network oscillations and persistent activity, have been built to capture some features of the prefrontal cortex in context-related memory.[31] Additional models look at the close relationship between the basal ganglia and the prefrontal cortex and how that contributes to working memory.[32]

One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable synapses are easy to train but also prone to stochastic disruption. Stable synapses forget less easily, but they are also harder to consolidate. One recent computational hypothesis involves cascades of plasticity that allow synapses to function at multiple time scales.[33] Stereochemically detailed models of the acetylcholine receptor-based synapse with the Monte Carlo method, working at the time scale of microseconds, have been built.[34] It is likely that computational tools will contribute greatly to our understanding of how synapses function and change in relation to external stimulus in the coming decades.

Behaviors of networks[edit]

Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most artificial neural networks, sparse and usually specific. It is not known how information is transmitted through such sparsely connected networks, although specific areas of the brain, such as the visual cortex, are understood in some detail.[35] It is also unknown what the computational functions of these specific connectivity patterns are, if any.

The interactions of neurons in a small network can be often reduced to simple models such as the Ising model. The statistical mechanics of such simple systems are well-characterized theoretically. Some recent evidence suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions.[36] It is not known, however, whether such descriptive dynamics impart any important computational function. With the emergence of two-photon microscopy and calcium imaging, we now have powerful experimental methods with which to test the new theories regarding neuronal networks.

In some cases the complex interactions between inhibitory and excitatory neurons can be simplified using mean-field theory, which gives rise to the population model of neural networks.[37] While many neurotheorists prefer such models with reduced complexity, others argue that uncovering structural-functional relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulation platforms like GENESIS or NEURON. There have been some attempts to provide unified methods that bridge and integrate these levels of complexity.[38]

Visual attention, identification, and categorization[edit]

Visual attention can be described as a set of mechanisms that limit some processing to a subset of incoming stimuli.[39] Attentional mechanisms shape what we see and what we can act upon. They allow for concurrent selection of some (preferably, relevant) information and inhibition of other information. In order to have a more concrete specification of the mechanism underlying visual attention and the binding of features, a number of computational models have been proposed aiming to explain psychophysical findings. In general, all models postulate the existence of a saliency or priority map for registering the potentially interesting areas of the retinal input, and a gating mechanism for reducing the amount of incoming visual information, so that the limited computational resources of the brain can handle it.[40] An example theory that is being extensively tested behaviorally and physiologically is the V1 Saliency Hypothesis that a bottom-up saliency map is created in the primary visual cortex to guide attention exogenously.[28] Computational neuroscience provides a mathematical framework for studying the mechanisms involved in brain function and allows complete simulation and prediction of neuropsychological syndromes.

Cognition, discrimination, and learning[edit]

Computational modeling of higher cognitive functions has only recently[when?] begun. Experimental data comes primarily from single-unit recording in primates. The frontal lobe and parietal lobe function as integrators of information from multiple sensory modalities. There are some tentative ideas regarding how simple mutually inhibitory functional circuits in these areas may carry out biologically relevant computation.[41]





One of the ultimate goals of psychology/neuroscience is to be able to explain the everyday experience of conscious life. Francis Crick, Giulio Tononi and Christof Koch made some attempts to formulate consistent frameworks for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative.[43] Specifically, Crick[44] cautioned the field of neuroscience to not approach topics that are traditionally left to philosophy and religion.[45]

Computational clinical neuroscience[edit]

Computational clinical neuroscience is a field that brings together experts in neuroscience, neurology, psychiatry, decision sciences and computational modeling to quantitatively define and investigate problems in neurological and psychiatric diseases, and to train scientists and clinicians that wish to apply these models to diagnosis and treatment.[46][47]


Neuromorphic computing[edit]

A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations (See: neuromorphic computing, physical neural network). One of the advantages of using a physical model computer such as this is that it takes the computational load of the processor (in the sense that the structural and some of the functional elements don't have to be programmed since they are in hardware). In recent times,[when?] neuromorphic technology has been used to build supercomputers which are used in international neuroscience collaborations. Examples include the Human Brain Project SpiNNaker supercomputer and the BrainScaleS computer.

See also[edit]

Notes and references[edit]

  1. ^ Trappenberg, Thomas P. (2010). Fundamentals of Computational Neuroscience. United States: Oxford University Press Inc. pp. 2. ISBN 978-0-19-851582-1.
  2. ^ What is computational neuroscience? Patricia S. Churchland, Christof Koch, Terrence J. Sejnowski. in Computational Neuroscience pp.46-55. Edited by Eric L. Schwartz. 1993. MIT Press "Archived copy". Archived from the original on 2011-06-04. Retrieved 2009-06-11.CS1 maint: archived copy as title (link)
  3. ^ Press, The MIT. "Theoretical Neuroscience". The MIT Press. Archived from the original on 2018-05-31. Retrieved 2018-05-24.
  4. ^ Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014). Neuronal Dynamics. Cambridge, UK: Cambridge University Press. ISBN 9781107447615.
  5. ^ Thomas, Trappenberg (2010). Fundamentals of Computational Neuroscience. OUP Oxford. p. 2. ISBN 978-0199568413. Retrieved 17 January 2017.
  6. ^ Gutkin, Boris; Pinto, David; Ermentrout, Bard (2003-03-01). "Mathematical neuroscience: from neurons to circuits to systems". Journal of Physiology-Paris. Neurogeometry and visual perception. 97 (2): 209–219. doi:10.1016/j.jphysparis.2003.09.005. ISSN 0928-4257. PMID 14766142. S2CID 10040483.
  7. ^ Kriegeskorte, Nikolaus; Douglas, Pamela K. (September 2018). "Cognitive computational neuroscience". Nature Neuroscience. 21 (9): 1148–1160. arXiv:1807.11819. Bibcode:2018arXiv180711819K. doi:10.1038/s41593-018-0210-5. ISSN 1546-1726. PMC 6706072. PMID 30127428.
  8. ^ "Encephalos Journal". Retrieved 2018-02-20.
  9. ^ "Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop". S2CID 15349751. Cite journal requires |journal= (help)
  10. ^ Brooks, R.; Hassabis, D.; Bray, D.; Shashua, A. (2012-02-22). "Turing centenary: Is the brain a good model for machine intelligence?". Nature. 482 (7386): 462–463. Bibcode:2012Natur.482..462.. doi:10.1038/482462a. ISSN 0028-0836. PMID 22358812. S2CID 205070106.
  12. ^ "Aditya Gilra: Biologically-plausible learning in neural networks for movement control and cognitive tasks". Retrieved 2019-12-11.
  13. ^ Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P. (2013-08-20). "Modeling language and cognition with deep unsupervised learning: a tutorial overview". Frontiers in Psychology. 4: 515. doi:10.3389/fpsyg.2013.00515. ISSN 1664-1078. PMC 3747356. PMID 23970869.
  14. ^ Shai, Adam; Larkum, Matthew Evan (2017-12-05). "Branching into brains". eLife. 6. doi:10.7554/eLife.33066. ISSN 2050-084X. PMC 5716658. PMID 29205152.
  15. ^ Schwartz, Eric (1990). Computational neuroscience. Cambridge, Mass: MIT Press. ISBN 978-0-262-19291-0.
  19. ^ Hubel DH, Wiesel TN (1962). "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex". J. Physiol. 160 (1): 106–54. doi:10.1113/jphysiol.1962.sp006837. PMC 1359523. PMID 14449617.
  21. ^ Wu, Samuel Miao-sin; Johnston, Daniel (1995). Foundations of cellular neurophysiology. Cambridge, Mass: MIT Press. ISBN 978-0-262-10053-3.
  22. ^ Koch, Christof (1999). Biophysics of computation: information processing in single neurons. Oxford [Oxfordshire]: Oxford University Press. ISBN 978-0-19-510491-2.
  25. ^ Chklovskii DB, Mel BW, Svoboda K (October 2004). "Cortical rewiring and information storage". Nature. 431 (7010): 782–8. Bibcode:2004Natur.431..782C. doi:10.1038/nature03012. PMID 15483599. S2CID 4430167.
    Review article
  26. ^ Zhaoping L. 2014, The efficient coding principle , chapter 3, of the textbook Understanding vision: theory, models, and data
  27. ^ see visual spational attention
  28. ^ a b Li. Z. 2002 A saliency map in primary visual cortex Trends in Cognitive Sciences vol. 6, Pages 9-16, and Zhaoping, L. 2014, The V1 hypothesis—creating a bottom-up saliency map for preattentive selection and segmentation in the book Understanding Vision: Theory, Models, and Data
  29. ^ Weiss, Yair; Simoncelli, Eero P.; Adelson, Edward H. (20 May 2002). "Motion illusions as optimal percepts". Nature Neuroscience. 5 (6): 598–604. doi:10.1038/nn0602-858. PMID 12021763. S2CID 2777968.
  30. ^ Ernst, Marc O.; Bülthoff, Heinrich H. (April 2004). "Merging the senses into a robust percept". Trends in Cognitive Sciences. 8 (4): 162–169. CiteSeerX doi:10.1016/j.tics.2004.02.002. PMID 15050512. S2CID 7837073.
  31. ^ Durstewitz D, Seamans JK, Sejnowski TJ (2000). "Neurocomputational models of working memory". Nat. Neurosci. 3 (Suppl): 1184–91. doi:10.1038/81460. PMID 11127836. S2CID 8204235.
  32. ^ Frank, M. J.; Loughry, B.; O'Reilly, R. C. (2001). "Interactions between frontal cortex and basal ganglia in working memory: A computational model" (PDF). Cognitive, Affective, & Behavioral Neuroscience. 1 (2): 137–160. doi:10.3758/cabn.1.2.137. PMID 12467110. S2CID 964100. Retrieved 2018-12-06.
  33. ^ Fusi S, Drew PJ, Abbott LF (2005). "Cascade models of synaptically stored memories". Neuron. 45 (4): 599–611. doi:10.1016/j.neuron.2005.02.001. PMID 15721245. S2CID 2981869.
  34. ^ Coggan JS, Bartol TM, Esquenazi E, et al. (2005). "Evidence for ectopic neurotransmission at a neuronal synapse". Science. 309 (5733): 446–51. Bibcode:2005Sci...309..446C. doi:10.1126/science.1108239. PMC 2915764. PMID 16020730.
  35. ^ Olshausen, Bruno A.; Field, David J. (1997-12-01). "Sparse coding with an overcomplete basis set: A strategy employed by V1?". Vision Research. 37 (23): 3311–3325. doi:10.1016/S0042-6989(97)00169-7. PMID 9425546. S2CID 14208692.
  36. ^ Schneidman E, Berry MJ, Segev R, Bialek W (2006). "Weak pairwise correlations imply strongly correlated network states in a neural population". Nature. 440 (7087): 1007–12. arXiv:q-bio/0512013. Bibcode:2006Natur.440.1007S. doi:10.1038/nature04701. PMC 1785327. PMID 16625187.
  37. ^ Wilson, H. R.; Cowan, J.D. (1973). "A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue". Kybernetik. 13 (2): 55–80. doi:10.1007/BF00288786. PMID 4767470. S2CID 292546.
  39. ^ Marvin M. Chun; Jeremy M. Wolfe; E. B. Goldstein (2001). Blackwell Handbook of Sensation and Perception. Blackwell Publishing Ltd. pp. 272–310. ISBN 978-0-631-20684-2.
  40. ^ Edmund Rolls; Gustavo Deco (2012). Computational Neuroscience of Vision. Oxford Scholarship Online. ISBN 978-0-198-52488-5.
  41. ^ Machens CK, Romo R, Brody CD (2005). "Flexible control of mutual inhibition: a neural model of two-interval discrimination". Science. 307 (5712): 1121–4. Bibcode:2005Sci...307.1121M. CiteSeerX doi:10.1126/science.1104171. PMID 15718474. S2CID 45378154.
  42. ^ Robinson PA, Rennie CJ, Rowe DL, O'Connor SC, Gordon E (2005). "Multiscale brain modelling". Philosophical Transactions of the Royal Society B. 360 (1457): 1043–1050. doi:10.1098/rstb.2005.1638. PMC 1854922. PMID 16087447.
  43. ^ Crick F, Koch C (2003). "A framework for consciousness". Nat. Neurosci. 6 (2): 119–26. doi:10.1038/nn0203-119. PMID 12555104. S2CID 13960489.
  44. ^ Crick, Francis (1994). The Astonishing Hypothesis: The Scientific Search for the Soul. New York: Scribner.
  45. ^ Crick, Francis; Koch, Christopher (1998). Consciousness and neuroscience. Cerebral Cortex (New York, N.Y. : 1991). 8. Essential Sources In The Scientific Study of Consciousness. Roscience. pp. 97–107. doi:10.1093/cercor/8.2.97. PMID 9542889.
  46. ^ Adaszewski S1, Dukart J, Kherif F, Frackowiak R, Draganski B; Alzheimer's Disease Neuroimaging Initiative (2013). "How early can we predict Alzheimer's disease using computational anatomy?". Neurobiol Aging. 34 (12): 2815–26. doi:10.1016/j.neurobiolaging.2013.06.015. PMID 23890839. S2CID 1025210.CS1 maint: multiple names: authors list (link)
  47. ^ Friston KJ, Stephan KE, Montague R, Dolan RJ (2014). "Computational psychiatry: the brain as a phantastic organ". Lancet Psychiatry. 1 (2): 148–58. doi:10.1016/S2215-0366(14)70275-5. PMID 26360579.


See also[edit]


  • BRIAN, a Python based simulator
  • Budapest Reference Connectome, web based 3D visualization tool to browse connections in the human brain
  • Emergent, neural simulation software.
  • GENESIS, a general neural simulation system.

External links[edit]