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Computational neuroscience

Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.[1][2][3][4]

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 biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory;[8][9] [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.

  1. ^ Trappenberg, Thomas P. (2010). Fundamentals of Computational Neuroscience. United States: Oxford University Press Inc. pp. 2. ISBN 978-0-19-851582-1.
  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.
  3. ^ Dayan P.; Abbott, L. F. (2001). Theoretical neuroscience: computational and mathematical modeling of neural systems. Cambridge, Mass: MIT Press. ISBN 978-0-262-04199-7.
  4. ^ Gerstner, W.; Kistler, W.; Naud, R.; Paninski, L. (2014). Neuronal Dynamics. Cambridge, UK: Cambridge University Press. ISBN 9781107447615.
  5. ^ Fan, Xue; Markram, Henry (2019). "A Brief History of Simulation Neuroscience". Frontiers in Neuroinformatics. 13: 32. doi:10.3389/fninf.2019.00032. ISSN 1662-5196. PMC 6513977. PMID 31133838.
  6. ^ Thomas, Trappenberg (2010). Fundamentals of Computational Neuroscience. OUP Oxford. p. 2. ISBN 978-0199568413. Retrieved 17 January 2017.
  7. ^ Gutkin, Boris; Pinto, David; Ermentrout, Bard (2003-03-01). "Mathematical neuroscience: from neurons to circuits to systems". Journal of Physiology-Paris. Neurogeometry and visual perception. 97 (2): 209–219. doi:10.1016/j.jphysparis.2003.09.005. ISSN 0928-4257. PMID 14766142. S2CID 10040483.
  8. ^ Kriegeskorte, Nikolaus; Douglas, Pamela K. (September 2018). "Cognitive computational neuroscience". Nature Neuroscience. 21 (9): 1148–1160. arXiv:1807.11819. Bibcode:2018arXiv180711819K. doi:10.1038/s41593-018-0210-5. ISSN 1546-1726. PMC 6706072. PMID 30127428.
  9. ^ Paolo, E. D., "Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop", Dynamical Systems Approach to Embodiment and Sociality, S2CID 15349751
  10. ^ Brooks, R.; Hassabis, D.; Bray, D.; Shashua, A. (2012-02-22). "Turing centenary: Is the brain a good model for machine intelligence?". Nature. 482 (7386): 462–463. Bibcode:2012Natur.482..462.. doi:10.1038/482462a. ISSN 0028-0836. PMID 22358812. S2CID 205070106.
  11. ^ Browne, A. (1997-01-01). Neural Network Perspectives on Cognition and Adaptive Robotics. CRC Press. ISBN 9780750304559.
  12. ^ Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P. (2013-08-20). "Modeling language and cognition with deep unsupervised learning: a tutorial overview". Frontiers in Psychology. 4: 515. doi:10.3389/fpsyg.2013.00515. ISSN 1664-1078. PMC 3747356. PMID 23970869.
  13. ^ Shai, Adam; Larkum, Matthew Evan (2017-12-05). "Branching into brains". eLife. 6. doi:10.7554/eLife.33066. ISSN 2050-084X. PMC 5716658. PMID 29205152.

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