Computational neuroscience

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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

San Francisco, California in 1989.[15] The first graduate educational program in computational neuroscience was organized as the Computational and Neural Systems Ph.D. program at the California Institute of Technology
in 1985.

The early historical roots of the field

artificial neural networks studies because of its simplicity (see a recent review[18]
).

About 40 years later,

primary visual cortex, the first cortical area to process information coming from the retina, have oriented receptive fields and are organized in columns.[19] 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
.

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

dendrites are also under intense investigation. There is a large body of literature regarding how different currents interact with geometric properties of neurons.[22]

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

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.[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

Hopfield net have been developed to address the properties of associative (also known as "content-addressable") style of memory that occur in biological systems. These attempts are primarily focusing on the formation of medium- and long-term memory, localizing in the hippocampus
.

One of the major problems in neurophysiological memory is how it is maintained and changed through multiple time scales. Unstable

synapses
forget less easily, but they are also harder to consolidate. 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 usually specific. It is not known how information is transmitted through such sparsely connected networks, although specific areas of the brain, such as the visual cortex, are understood in some detail.[34]
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

, 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.[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[

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.[40]

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 (

CRUM
) is another attempt at modeling human cognition through simulated processes like acquired rule-based systems in decision making and the manipulation of visual representations in decision making.

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

psychiatric diseases, and to train scientists and clinicians that wish to apply these models to diagnosis and treatment.[43][44]

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

Computational psychiatry is a new emerging field that brings together experts in machine learning, neuroscience, neurology, psychiatry, psychology to provide an understanding of psychiatric disorders.[47][48][49]

Technology

Neuromorphic computing

A neuromorphic computer/chip is any device that uses physical artificial neurons (made from silicon) to do computations (See:

physical model computer such as this is that it takes the computational load of the processor (in the sense that the structural and some of the functional elements don't have to be programmed since they are in hardware). In recent times,[50] neuromorphic technology has been used to build supercomputers which are used in international neuroscience collaborations. Examples include the Human Brain Project SpiNNaker supercomputer and the BrainScaleS computer.[51]

See also

Notes and references

  1. .
  2. ^ 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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  6. . Retrieved 17 January 2017.
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  16. ^ Lapicque L (1907). "Recherches quantitatives sur l'excitation électrique des nerfs traitée comme une polarisation". J. Physiol. Pathol. Gen. 9: 620–635.
  17. S2CID 17816096
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  24. ^ "Dynamics of Ion Fluxes between Neurons, Astrocytes and the Extracellular Space during Neurotransmission". cyberleninka.ru. Retrieved 2023-03-14.
  25. PMID 25826753
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  27. .
    Review article
  28. ^ Zhaoping L. 2014, The efficient coding principle , chapter 3, of the textbook Understanding vision: theory, models, and data
  29. ^ see visual spational attention https://en.wikipedia.org/wiki/Visual_spatial_attention
  30. ^ 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
  31. S2CID 2777968
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  49. ^ Russell, John (21 March 2016). "Beyond von Neumann, Neuromorphic Computing Steadily Advances".
  50. PMID 24139655.{{cite journal}}: CS1 maint: DOI inactive as of January 2024 (link
    )

Bibliography

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

Conferences

Websites