Computational neuroscience - Wikipedia, the free encyclopedia. Computational neuroscience (also theoretical neuroscience) is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. These models capture the essential features of the biological system at multiple spatial- temporal scales, from membrane currents, proteins, 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 biological or psychological experiments. History. Schwartz, who organized a conference, held in 1.
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 1. Computational Neuroscience.
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Bower and John Miller in San Francisco, California in 1. CNS meeting . Lapicque introduced the integrate and fire model of the neuron in a seminal article published in 1.
Hubel & Wiesel discovered that neurons in the primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns. Computational modeling of biophysically realistic neurons and dendrites began with the work of Wilfrid Rall, with the first multicompartmental model using cable theory. Major topics. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena. Single- neuron modeling. Hodgkin and Huxley's original model only employed two voltage- sensitive currents (Voltage sensitive ion channels are glycoprotein molecules which extend through the lipid bilayer, allowing ions to traverse under certain conditions through the axolemma), 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. There is a large body of literature regarding how different currents interact with geometric properties of neurons. Blue Brain, a project founded by Henry Markram from the . So, researchers that study large neural circuits typically represent each neuron and synapse simply, ignoring much of the biological detail. This is unfortunate as there is evidence that the richness of biophysical properties on the single neuron scale can supply mechanisms that serve as the building blocks for network dynamics. Algorithms have been developed to produce faithful, faster running, simplified surrogate neuron models from computationally expensive, detailed neuron models.?
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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? 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 are 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.
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Title: Theoretical Neuroscience: Computational Modeling of Neural Systems and Mathematical Created Date: 3/8/2005 9:32:26 PM. The University of Chicago Press. Chicago Distribution Center. Theoretical and Computational Neuroscience .
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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 a biophysical modelling of different subsystems and a more theoretical modelling 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. 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.
Title: Theoretical Neuroscience Computational And Mathematical Modeling Of Neural Systems Peter Dayan PDF Author: Matthias Nussbaum Subject: theoretical neuroscience computational and mathematical modeling of neural 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. 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. 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.
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. There has been some recent evidence that suggests that dynamics of arbitrary neuronal networks can be reduced to pairwise interactions.
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. 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. 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. 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 bases for some quantitative modeling of large- scale brain activity. Francis Crick and Christof Koch made some attempts to formulate a consistent framework for future work in neural correlates of consciousness (NCC), though much of the work in this field remains speculative.? Churchland, Christof Koch, Terrence J. Computational neuroscience.
Cambridge, Mass: MIT Press. Computational neuroscience. Berlin, Germany: Springer. Frontiers in Computational Neuroscience. Foundations of cellular neurophysiology. Cambridge, Mass: MIT Press. Biophysics of computation: information processing in single neurons.
Frontiers in Computational Neuroscience. Bibcode: 2. 00. 4Natur. C. Review article^Durstewitz D, Seamans JK, Sejnowski TJ (2. Bibcode: 2. 00. 5Sci.. C. Bibcode: 2. 00. Natur. 4. 40. 1. 00. S. Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems (Computational Neuroscience).
Cambridge, Mass: The MIT Press. Bibcode: 2. 00. 5Sci.. M. Philosophical Transactions of the Royal Society B. Sejnowski, Terrence J.; Churchland, Patricia Smith (1. The computational brain. Cambridge, Mass: MIT Press.
F.; Dayan, Peter (2. Theoretical neuroscience: computational and mathematical modeling of neural systems.
Cambridge, Mass: MIT Press. Eliasmith, Chris; Anderson, Charles H. Neural engineering: Representation, computation, and dynamics in neurobiological systems. Cambridge, Mass: MIT Press.
Hodgkin AL, Huxley AF (2. August 1. 95. 2). William Bialek; Rieke, Fred; David Warland; Rob de Ruyter van Steveninck (1. Spikes: exploring the neural code. Schutter, Erik de (2. Computational neuroscience: realistic modeling for experimentalists. Sejnowski, Terrence J.; Hemmen, J.
Arbib; Shun- ichi Amari; Prudence H. The Handbook of Brain Theory and Neural Networks. Cambridge, Massachusetts: The MIT Press. The goal is to help researchers understand brain function at the level of the topographic maps that make up sensory and motor systems.
Topographica is intended to complement the many good low- level neuron simulators that are available, such as Genesis and Neuron. Topographica focuses on the large- scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections.