Computational neuroscience
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Computational Neuroscience is an interdisciplinary science that links the diverse fields of neuroscience, computer science, physics and applied mathematics together. It serves as the primary theoretical method for investigating the function and mechanism of the nervous system. Computational neuroscience traces its historical roots to the work of people such as Andrew Huxley, Alan Hodgkin, and David Marr. Hodgkin and Huxley's developed the voltage clamp and created the first mathematical model of the action potential. 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 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 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 Neuron, that allow rapid and systematic in silico modeling of realistic neurons. A controversial, multi-institutional attempt, named Blue Brain, is underway to conduct large scale modeling of a cortical column and structures beyond.
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[edit] 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.
[edit] Single Neuron Modeling
Even single neurons have complex biophysical characteristics. In 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. We now know 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 dendrite 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).
[edit] 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 maintains maximal information storage. (for a review, see Chklovskii, 2004)
[edit] Sensory processing
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.
[edit] Memory and 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, 2004) 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.
[edit] 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 pattern 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.
[edit] 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.
[edit] 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)
[edit] See also
- Connectionism
- Neural network
- Electrophysiology
- Important publications in neuroscience
- Brain-computer interface
- Neural engineering
- Neurotechnology
- Neuroinformatics
- Computational Neurogenetic Modeling
[edit] References
- Chklovskii, DB (2004) "Synaptic connectivity and neuronal morphology: two sides of the same coin", Neuron. 43(5):609-17
- Coggan JS, Bartol TM, Esquenazi E et al, "Evidence for ectopic neurotransmission at a neuronal synapse.", Science.,2005 Jul 15;309(5733):446-51
- Durstewitz D, Seamans JK, Sejnowski TJ., (2004) "Neurocomputational models of working memory.", Nat Neurosci. 2000 Nov; Suppl:1184-91.
- 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
- 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.
- 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.
- Fusi S, Drew PJ, Abbott LF., "Cascade models of synaptically stored memories", Neuron. 2005 Feb 17;45(4):599-611
- Patricia S. Churchland, Terrence J. Sejnowski: The Computational Brain, MIT Press, 1992, ISBN 0-262-03188-4.
- Koch C Biophysics of Computation: Information Processing in Single Neurons, Oxford University Press, 1998, ISBN 0-19-510491-9.
- Jonhston D and Wu SM, Foundations of Cellular Neurophysiology, MIT Press, 1994, ISBN 0-262-10053-3.
- 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.
- Erik de Schutter, ed.: Computational Neuroscience - Realistic Modeling for Experimentalists, CRC Press, 2000, ISBN 0-8493-2068-2.
- Peter Dayan, L.F. Abbott: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, MIT Press, 2001, ISBN 0-262-04199-5.
- J. L.. van Hemmen, T. J. Sejnowski, eds.: 23 Problems in Systems Neuroscience Oxford University Press, 2005 ISBN 0-19-514822-3.
[edit] External links
[edit] Journals
- Network: Computation in Neural Systems
- Biological Cybernetics Journal
- The Journal of Computational Neuroscience
- Neural Computation
[edit] Software
- Genesis, a general neural simulation system
- Neuron, a neural network simulator
- HHsim, a neuronal membrane simulator
- MCell, A Monte Carlo Simulator of Cellular Microphysiology
- PDP++, neural simulation software
[edit] Conferences
- Computational NeuroSciences, the yearly computational neuroscience meeting
- Cosyne, a major computational neuroscience meeting
- Neural Information Processing Systems (NIPS), a leading annual conference covering other machine learning topics as well
- Computational Cognitive Neuroscience Conference (CCNC) - yearly conference
[edit] Websites
- Neurosecurity, Articles and lectures on Computational neuroscience.
- Perlewitz's computational neuroscience on the web
- compneuro.org, books and programs for neural modeling
- 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
[edit] Courses
- NeuroWiki:CompNeuroCourses, a list of comp neuro courses with material available online
[edit] Research Groups
- Computational Neuroscience Group at the KFKI RIPNP of the Hungarian Academy of Sciences
- Computational Neurobiology Laboratory at the Salk Institute (CNL)
- 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
[edit] 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
