Computational neuroscience

Computational neuroscience is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science, electrical engineering, computer science, physics and mathematics. Historically, the term 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", MIT Press(1990). The early historical roots of the field can be traced to the work of people such as Hodgkin & Huxley, Hubel & Wiesel, and David Marr, to name but a few. Hodgkin & Huxley developed the voltage clamp and created the first mathematical 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 (Hubel & Wiesel, 1962). 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.

Computational neuroscience is 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 test hypotheses that can be directly verified by current or future biological 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 collaboration between IBM and École Polytechnique Fédérale de Lausanne, aims to construct a biophysically detailed simulation of a cortical column on the Blue Gene supercomputer.

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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 (for reference, see Johnston and Wu, 1994).

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 (for reference, see Koch, 1998).

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. (for a review, see Chklovskii, 2004)

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. (For review, see Durstewitz et al, 2000)

One of the major problems in biological 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 (Fusi et al, 2005) 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 (Coggan et al, 2005). 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 have 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.) 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_(software) or Neuron_(software). There have been some attempts to provide unified methods that bridge, and integrate, these levels of complexity (Eliasmith & Anderson, 2003).

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 (Machens et al, 2005).

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.

Consciousness

The ultimate goal of 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. (for a review, see Koch and Crick, 2003). Another attempt is done by Andrew & Alexander Fingelkurts: they are developing the Operational Architectonics theory of brain-mind functioning. This theory treats consciousness as a biological phenomenon in the brain which is realized by the highly organized macro-level electrophysiological (EEG) phenomena (metastable operational modules), which are brought to existence by the coordinated electrical activity (operational synchrony) of many neuronal populations dispersed throughout the brain (for a review see Fingelkurts An.A. and Fingelkurts Al.A., 2001; 2004; 2006).

See also

• Connectionism
• Neural network
• Biological neuron models
• Electrophysiology
• Important publications in neuroscience
• Brain-computer interface
• Neural engineering
• Neurotechnology
• Neuroinformatics
• Computational Neurogenetic Modeling

References

• Chklovskii, DB (2004) "Synaptic connectivity and neuronal morphology: two sides of the same coin", Neuron. 43(5):609-17
• Churchland, P. S. & T. J. Sejnowski (1992) The Computational Brain, MIT Press, ISBN 0-262-03188-4.
• Eliasmith, C & C.H. Anderson (2003). Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems MIT Press, ISBN 0-262-55060-1.
• Coggan JS, Bartol TM, Esquenazi E et al, "Evidence for ectopic neurotransmission at a neuronal synapse.", Science.,2005 Jul 15;309(5733):446-51
• Peter Dayan, L.F. Abbott: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2001, ISBN 0-262-04199-5.
• Durstewitz D, Seamans JK, Sejnowski TJ., (2000) "Neurocomputational models of working memory.", Nat Neurosci. 2000 Nov; Suppl:1184-91.
• Fingelkurts An.A. and Fingelkurts Al.A., (2001) "Operational architectonics of the human brain biopotential field: towards solving the mind-brain problem." Brain and Mind 2:261–296
• Fingelkurts An.A. and Fingelkurts Al.A., (2004) "Making complexity simpler: multivariability and metastability in the Brain." Int. J. Neurosci., vol. 114, pp. 843-862
• Fingelkurts An.A. and Fingelkurts Al.A., (2006) "Timing in cognition and EEG brain dynamics: discreteness versus continuity." Cognitive Proces., vol. 7, pp. 135-162
• Fusi S, Drew PJ, Abbott LF., "Cascade models of synaptically stored memories", Neuron. 2005 Feb 17;45(4):599-611
• Johnston D and Wu SM, Foundations of Cellular Neurophysiology, MIT Press, 1994, ISBN 0-262-10053-3.
• Hodgkin, A. L. and Huxley, A. F. (1952) "A Quantitative Description of Membrane Current and its Application to Conduction and Excitation in Nerve" Journal of Physiology 117:500-544
• Hubel DH, Wiesel TN (1962) "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex" Journal of Physiology 160:106-154.
• Koch C Biophysics of Computation: Information Processing in Single Neurons, Oxford University Press, 1998, ISBN 0-19-510491-9.
• Koch, C and Crick, F (2003) "A framework for consciousness", Nat Neurosci. 2003 Feb;6(2):119-26.
• Machens CK, Romo R, Brody CD. (2005) "Flexible control of mutual inhibition: a neural model of two-interval discrimination.", Science. 2005 Feb 18;307(5712):1121-4.
• F. Rieke, D. Warland, W. Bialek and R. de Ruyter van Steveninck: Spikes: Exploring the Neural Code, MIT Press, 1999, ISBN 0-262-68108-0.
• Schneidman E, Berry MJ 2nd, Segev R, Bialek W. (2006) "Weak pairwise correlations imply strongly correlated network states in a neural population.", Nature. 2006 Apr 20;440(7087):1007-12.
• Eric L. Schwartz, ed.: Computational Neuroscience, MIT Press, 1990, ISBN 0-262-19291-8.
• Erik de Schutter, ed.: Computational Neuroscience - Realistic Modeling for Experimentalists, CRC Press, 2000, ISBN 0-8493-2068-2.
• J. L.. van Hemmen, T. J. Sejnowski, eds.: 23 Problems in Systems Neuroscience Oxford University Press, 2005 ISBN 0-19-514822-3.

Software

• Genesis, a general neural simulation system
• Neuron, a neural network simulator
• 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
• HHsim, a neuronal membrane simulator
• MCell, A Monte Carlo Simulator of Cellular Microphysiology
• Emergent, neural simulation software
• Python tools : http://neuralensemble.org/
  • pyNN

Conferences

• Computational and Systems Neuroscience (COSYNE) - a computational neuroscience meeting with a systems neuroscience focus.
• Annual Computational Neuroscience Meeting (CNS) - a yearly computational neuroscience meeting.
• Neural Information Processing Systems (NIPS) - a leading annual conference covering other machine learning topics as well.
• Computational Cognitive Neuroscience Conference (CCNC) - a yearly conference.
• International Conference on Cognitive Neurodynamics (ICCN) - a yearly conference.

Websites

• Neurosecurity, Articles and lectures on Computational neuroscience.
• Perlewitz's computational neuroscience on the web
• compneuro.org, books and programs for neural modeling
• Encyclopedia of Computational Neuroscience, part of Scholarpedia, an online expert curated encyclopedia on computational neuroscience, dynamical systems and machine intelligence
• NeuroWiki, a wiki discussion forum about neuroscience research, especially systems, theoretical/computational, and cognitive neuroscience

Courses

• NeuroWiki:CompNeuroCourses, a list of comp neuro courses with material available online
• http://www.mbl.edu/education/courses/special_topics/mcn.html Summer course at the MBL, which features major figures in the field (Abbott, Bialek, Sejnowski, et.al.) as guest faculty.

Research Groups

• Bernstein Centers for Computational Neuroscience Germany
• Bernstein Center for Computational Neuroscience Berlin
• Bernstein Center for Computational Neuroscience Freiburg
• Bernstein Center for Computational Neuroscience Goettingen
• Bernstein Center for Computational Neuroscience Munich
• BM-Science - Brain & Mind Technologies Research Centre, Finland
• Neuroengineering Laboratory at the University of Pennsylvania
• Computational Neuroscience Group at the KFKI RIPNP of the Hungarian Academy of Sciences
• Computational Neurobiology Laboratory at the Salk Institute (CNL)
• Centre for Theoretical Neuroscience (CTN) at the University of Waterloo
• MIT Media Lab, Neuroengineering and Neuromedia Group
• Institute for Theoretical Biology, Humboldt-Universitaet zu Berlin
• Computational Neuroscience Group at King's College London
• MIT Center for Biological & Computational Learning (CBCL)
• Center for Theoretical Neuroscience at Columbia University
• Interdisciplinary Center for Neural Computation at Hebrew University
• Gatsby Computational Neuroscience Unit at University College London
• Martinos Computational Neuroscience Center for integrating neuroimaging and computational neuroscience
• Georgetown Laboratory for Computational Cognitive Neuroscience
• Hertie Center for Clinical Brain Research, Laboratory for Action Representation and Learning
• Computational Neuroscience Lab, University of Queensland
• Computational Cognitive Neuroscience Lab, University of Colorado at Boulder
• Theoretical Neuroscience Group, Florida Atlantic University
• Centre for Cognitive Neuroscience and Cognitive Systems at the University of Kent
• Computational Neuroscience Engineering Lab, University of Florida
• Institute for Adaptive and Neural Computation, University of Edinburgh
• Centre for Theoretical and Computational Neuroscience, University of Plymouth
• Theoretical Neurobiology Lab, University of Antwerp
• Computational Neuroscience Unit, Okinawa Institute of Science and Technology
• Group for Neural Theory, Ecole normale superieure, Paris

Papers

• Review - Sejnowski, T. J.; Koch, C.; Churchland, P. S.; Computational Neuroscience, Science, 241, 1299-1306, 1988
• A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex - Biologically-based vision algorithm