Computational neuroscience
This article may be too technical for most readers to understand.(March 2014) |
Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of
Computational neuroscience employs computational simulations[5] 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.[6] The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.[7]
Computational neuroscience focuses on the description of
[10] although mutual inspiration exists and sometimes there is no strict limit between fields,[11][12][13] 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.
History
The term 'computational neuroscience' was introduced by
The early historical roots of the field
About 40 years later,
Major topics
Research in computational neuroscience can be roughly categorized into several lines of inquiry. Most computational neuroscientists collaborate closely with experimentalists in analyzing novel data and synthesizing new models of biological phenomena.
Single-neuron modeling
Even a single neuron has complex biophysical characteristics and can perform computations (e.g.[20]). 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.[21]
The computational functions of complex
There are many software packages, such as
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 computing cost can limit 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]
Modeling Neuron-glia interactions
Glial cells participate significantly in the regulation of neuronal activity at both the cellular and the network level. Modeling this interaction allows to clarify the potassium cycle,[25][26] so important for maintaining homeostatis and to prevent epileptic seizures. Modeling reveals the role of glial protrusions that can penetrate in some cases the synaptic cleft to interfere with the synpatic transmission and thus control synaptic communication.[27]
Development, axonal patterning, and guidance
Computational neuroscience aims to address a wide array of questions. How do
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.[28]
Sensory processing
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.[29]
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.[30] 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.[31]
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.[32][33]
Motor control
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
Earlier models of
One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable
Behaviors of networks
Biological neurons are connected to each other in a complex, recurrent fashion. These connections are, unlike most
The interactions of neurons in a small network can be often reduced to simple models such as the
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.[36] 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.[37]
Visual attention, identification, and categorization
Visual attention can be described as a set of mechanisms that limit some processing to a subset of incoming stimuli.[38] 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.[39] 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.[31] 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
Computational modeling of higher cognitive functions has only recently[
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 bases for some quantitative modeling of large-scale brain activity.[41]
The Computational Representational Understanding of Mind (
Consciousness
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.[42]
Computational clinical neuroscience
Predictive computational neuroscience
Predictive computational neuroscience is a recent field that combines signal processing, neuroscience, clinical data and machine learning to predict the brain during coma [45] or anesthesia.[46] For example, it is possible to anticipate deep brain states using the EEG signal. These states can be used to anticipate hypnotic concentration to administrate to the patient.
Computational Psychiatry
Technology
Neuromorphic computing
A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations (See:
See also
- Action potential
- Biological neuron models
- Bayesian brain
- Brain simulation
- Computational anatomy
- Connectomics
- Differentiable programming
- Electrophysiology
- FitzHugh–Nagumo model
- Galves–Löcherbach model
- Goldman equation
- Hodgkin–Huxley model
- Information theory
- Mathematical model
- Nonlinear dynamics
- Neural coding
- Neural decoding
- Neural oscillation
- Neuroinformatics
- Neuroplasticity
- Neurophysiology
- Systems neuroscience
- Theoretical biology
- Theta model
Notes and references
- ISBN 978-0-19-851582-1.
- ^ Patricia S. Churchland; Christof Koch; Terrence J. Sejnowski (1993). "What is computational neuroscience?". In Eric L. Schwartz (ed.). Computational Neuroscience. MIT Press. pp. 46–55. Archived from the original on 2011-06-04. Retrieved 2009-06-11.
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- ^ Lapicque L (1907). "Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation". J. Physiol. Pathol. Gen. 9: 620–635.
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- ^ "Dynamics of Ion Fluxes between Neurons, Astrocytes and the Extracellular Space during Neurotransmission". cyberleninka.ru. Retrieved 2023-03-14.
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Review article - ^ Zhaoping L. 2014, The efficient coding principle , chapter 3, of the textbook Understanding vision: theory, models, and data
- ^ see visual spational attention https://en.wikipedia.org/wiki/Visual_spatial_attention
- ^ 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
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- ^ Russell, John (21 March 2016). "Beyond von Neumann, Neuromorphic Computing Steadily Advances".
- PMID 24139655.)
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Bibliography
- Chklovskii DB (2004). "Synaptic connectivity and neuronal morphology: two sides of the same coin". Neuron. 43 (5): 609–17. S2CID 16217065.
- ISBN 978-0-262-03188-2.
- Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014). Neuronal Dynamics. Cambridge, UK: Cambridge University Press. ISBN 9781107447615.
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- Eliasmith, Chris; Anderson, Charles H. (2003). Neural engineering: Representation, computation, and dynamics in neurobiological systems. Cambridge, Mass: ISBN 978-0-262-05071-5.
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- Schutter, Erik de (2001). Computational neuroscience: realistic modeling for experimentalists. Boca Raton: CRC. ISBN 978-0-8493-2068-2.
- Sejnowski, Terrence J.; Hemmen, J. L. van (2006). 23 problems in systems neuroscience. Oxford [Oxfordshire]: Oxford University Press. ISBN 978-0-19-514822-0.
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See also
Software
- 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.
- NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons.
External links
Journals
- Journal of Mathematical Neuroscience
- Journal of Computational Neuroscience
- Neural Computation
- Cognitive Neurodynamics
- Frontiers in Computational Neuroscience
- PLoS Computational Biology
- Frontiers in Neuroinformatics
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.
- Computational Cognitive Neuroscience - a yearly computational neuroscience meeting with a focus on cognitive phenomena.
- Neural Information Processing Systems (NIPS)– a leading annual conference covering mostly machine learning.
- Cognitive Computational Neuroscience (CCN) – a computational neuroscience meeting focusing on computational models capable of cognitive tasks.
- International Conference on Cognitive Neurodynamics (ICCN) – a yearly conference.
- UK Mathematical Neurosciences Meeting– a yearly conference, focused on mathematical aspects.
- Bernstein Conference on Computational Neuroscience (BCCN)– a yearly computational neuroscience conference ].
- AREADNE Conferences– a biennial meeting that includes theoretical and experimental results.
Websites
- Encyclopedia of Computational Neuroscience, part of Scholarpedia, an online expert curated encyclopedia on computational neuroscience and dynamical systems