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Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system[1]. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology, computer science, physics and mathematics.

Computational neuroscience is somewhat distinct from psychological connectionism and theories of learning from disciplines such as machine learning, neural networks and statistical learning theory in that it emphasizes descriptions of functional and biologically realistic neurons (and neural systems) and their physiology and dynamics. These models capture the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, protein and chemical coupling to network oscillations, columnar and topographic architecture and learning and memory. These computational models are used to frame hypotheses that can be directly tested by current or future biological and/or psychological experiments.

Currently, the field is undergoing a rapid expansion. There are many software packages, such as GENESIS and NEURON, that allow rapid and systematic in silico modeling of realistic neurons. Blue Brain, a project founded by Henry Markram from the École Polytechnique Fédérale de Lausanne, aims to construct a biophysically detailed simulation of a cortical column on the Blue Gene supercomputer.


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 later published as the book "Computational Neuroscience" (1990).[2]

The early historical roots of the field can be traced to the work of people such as Louis Lapicque, Hodgkin & Huxley, Hubel & Wiesel, and David Marr, to name but a few. Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1907 [3]; this model is still one of the most popular models in computational neuroscience for both cellular and neural networks studies, as well as in mathematical neuroscience because of its simplicity (see the recent review article [4] published recently for the centenary of the original Lapicque's 1907 paper - this review also contains an english translation of the original paper). About 40 years later, Hodgkin & Huxley developed the voltage clamp and created the first biophysical model of the action potential. Hubel & Wiesel discovered that neurons in primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns.[5] David Marr's work focused on the interactions between neurons, suggesting computational approaches to the study of how functional groups of neurons within the hippocampus and neocortex interact, store, process, and transmit information. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory.


The Organization for Computational Neuroscience is a non-profit organization one of whose tasks is to organize the annual international Computational Neuroscience meeting.

Major topics

Research in computational neuroscience can be roughly categorized into several lines of inquiries. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.

Single-neuron modeling

Main article: Biological neuron models

Even single neurons have complex biophysical characteristics. Hodgkin and Huxley's original model only employed two voltage-sensitive currents, the fast-acting sodium and the inward-rectifying potassium. Though successful in predicting the timing and qualitative features of the action potential, it nevertheless failed to predict a number of important features such as adaptation and shunting. Scientists now believe that there are a wide variety of voltage-sensitive currents, and the implications of the differing dynamics, modulations and sensitivity of these currents is an important topic of computational neuroscience.[6]

The computational functions of complex dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.[7]

Some models are also tracking biochemical pathways at very small scales such as spines or synaptic clefts.

Development, axonal patterning and guidance

How do axons and dendrites form during development? How do axons know where to target and how to reach these targets? How do neurons migrate to the proper position in the central and peripheral systems? How do synapses form? We know from molecular biology that distinct parts of the nervous system release distinct chemical cues, from growth factors to hormones that modulate and influence the growth and development of functional connections between neurons.

Theoretical investigations into the formation and patterning of synaptic connection and morphology is still nascent. One hypothesis that has recently garnered some attention is the minimal wiring hypothesis, which postulates that the formation of axons and dendrites effectively minimizes resource allocation while maintaining maximal information storage.[8]

Sensory processing

Early models of sensory processing understood within a theoretical framework is 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.

Current research in sensory processing is divided among biophysical modelling of different subsystems and more theoretical modelling function of perception. Current models of perception have suggested that the brain performs some form of Bayesian inference and integration of different sensory information in generating our perception of the physical world.

Memory and synaptic plasticity

Main article: Synaptic plasticity

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, rather than content-addressable style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium-term 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.[9]

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[10] that allow synapses to function at multiple time scales. Stereochemically detailed models of the acetylcholine receptor-based synapse with Monte Carlo method, working at the time scale of microseconds, have been built.[11] 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

Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most artificial neural networks, sparse and most likely, specific. It is not known how information is transmitted through such sparsely connected networks. It is also unknown what the computational functions, if any, of these specific connectivity patterns are.

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. There has been some recent evidence that suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions.(Schneidman et al., 2006; Shlens et al., 2006.)[12] It's unknown, 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.

While many neuro-theorists prefer models with reduced complexity, others argue that uncovering structure function relations depends on including as much neuronal and network structure as possible. Models of this type are typically built in large simulations platforms like GENESIS or Neuron. There have been some attempts to provide unified methods that bridge, and integrate, these levels of complexity.[13]

Cognition, discrimination and learning

Computational modeling of higher cognitive functions has only begun recently. 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.[14]

The brain seems to be able to discriminate and adapt particularly well in certain contexts. For instance, human beings seem to have an enormous capacity for memorizing and recognizing faces. One of the key goals of computational neuroscience is to dissect how biological systems carry out these complex computations efficiently and potentially replicate these processes in building intelligent machines.

The brain's large-scale organizational principles are illuminated by many fields, including biology, psychology, and clinical practice. Integrative neuroscience attempts to consolidate these observations through unified descriptive models, and databases of behavioral measures and recordings. These are the basis for some quantitative modeling of large-scale brain activity.[15]


The ultimate goal of psychology/neuroscience is to be able to explain the every day experience of conscious life. Francis Crick and Christof Koch made some attempts in formulating a consistent framework for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative.[16]

See also


  1. 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 [1]
  2. Schwartz, Eric (1990). Computational neuroscience, Cambridge, Mass: MIT Press.
  3. Lapicque L (1907). Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation. J. Physiol. Pathol. Gen. 9: 620–635.
  4. Brunel N, Van Rossum MC (2007). Lapicque's 1907 paper: from frogs to integrate-and-fire. Biol. Cybern. 97: 337–339.
  5. Hubel DH, Wiesel TN (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. (Lond.) 160: 106–54.
  6. Wu, Samuel Miao-sin; Johnston, Daniel (1995). Foundations of cellular neurophysiology, Cambridge, Mass: MIT Press.
  7. Koch, Christof (1999). Biophysics of computation: information processing in single neurons, Oxford [Oxfordshire]: Oxford University Press.
  8. Chklovskii DB, Mel BW, Svoboda K (October 2004). Cortical rewiring and information storage. Nature 431 (7010): 782–8.
    Review article
  9. Durstewitz D, Seamans JK, Sejnowski TJ (2000). Neurocomputational models of working memory. Nat Neurosci. 3 (Suppl): 1184–91.
  10. Fusi S, Drew PJ, Abbott LF (2005). Cascade models of synaptically stored memories. Neuron 45 (4): 599–611.
  11. Coggan JS, Bartol TM, Esquenazi E, et al. (2005). Evidence for ectopic neurotransmission at a neuronal synapse. Science 309 (5733): 446–51.
  12. 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.
  13. Anderson, Charles H.; Eliasmith, Chris (2004). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience), Cambridge, Mass: The MIT Press.
  14. Machens CK, Romo R, Brody CD (2005). Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307 (5712): 1121–4.
  15. 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.
  16. Crick F, Koch C (2003). A framework for consciousness. Nat Neurosci. 6 (2): 119–26.
  • Chklovskii DB (2004). Synaptic connectivity and neuronal morphology: two sides of the same coin. Neuron 43 (5): 609–17.
  • Sejnowski, Terrence J.; Churchland, Patricia Smith (1992). The computational brain, Cambridge, Mass: MIT Press.
  • Abbott, L. F.; Dayan, Peter (2001). Theoretical neuroscience: computational and mathematical modeling of neural systems, Cambridge, Mass: MIT Press.
  • Hodgkin AL, Huxley AF (28 August 1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. (Lond.) 117 (4): 500–44.
  • William Bialek; Rieke, Fred; David Warland; Rob de Ruyter van Steveninck (1999). Spikes: exploring the neural code, Cambridge, Mass: MIT.
  • Schutter, Erik de (2001). Computational neuroscience: realistic modeling for experimentalists, Boca Raton: CRC.
  • Sejnowski, Terrence J.; Hemmen, J. L. van (2006). 23 problems in systems neuroscience, Oxford [Oxfordshire]: Oxford University Press.
  • Michael A. Arbib, Shun-ichi Amari, Prudence H. Arbib (2002). The Handbook of Brain Theory and Neural Networks, Cambridge, Massachusetts: The MIT Press.

External links



  • Brian, a simulator for spiking neural networks.
  • Emergent, neural simulation software.
  • Genesis, a general neural simulation system.
  • HHsim, a neuronal membrane simulator.
  • HNeT, Holographic Neural Technology.
  • MCell, A Monte Carlo Simulator of Cellular Microphysiology.
  • ModelDB, a large open-access database of program codes of published computational neuroscience models.
  • NEST, a simulation tool for large neuronal systems.
  • Neuroconstruct, software for developing biologically realistic 3D neural networks.
  • Neurofitter, a parameter tuning package for electrophysiological neuron models.
  • Neurojet, a neural network simulator specialized for the hippocampus.
  • NEURON, a neuron simulator also useful to simulate neural networks.
  • Neurospaces, an efficient neural simulation system that uses software engineering principles from the industry.
  • Neurscience related Python tools
  • SNNAP, a single neuron and neural network simulator tool.




Research groups


Edit General subfields and scientists in Cybernetics
K1 Polycontexturality, Second-order cybernetics
K2 Catastrophe theory, Connectionism, Control theory, Decision theory, Information theory, Semiotics, Synergetics, Sociosynergetics, Systems theory
K3 Biological cybernetics, Biomedical cybernetics, Biorobotics, Computational neuroscience, Homeostasis, Medical cybernetics, Neuro cybernetics, Sociocybernetics
Cyberneticians William Ross Ashby, Claude Bernard, Valentin Braitenberg, Ludwig von Bertalanffy, George S. Chandy, Joseph J. DiStefano III, Heinz von Foerster, Charles François, Jay Forrester, Buckminster Fuller, Ernst von Glasersfeld, Francis Heylighen, Erich von Holst, Stuart Kauffman, Sergei P. Kurdyumov, Niklas Luhmann, Warren McCulloch, Humberto Maturana, Horst Mittelstaedt, Talcott Parsons, Walter Pitts, Alfred Radcliffe-Brown, Robert Trappl, Valentin Turchin, Francisco Varela, Frederic Vester, John N. Warfield, Kevin Warwick, Norbert Wiener
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