Editing Computational neuroscience (section)

{{short description|Branch of neuroscience}}
'''Computational neuroscience''' (also known as '''theoretical neuroscience''' or '''mathematical neuroscience''') is a branch of&nbsp;[[neuroscience]]&nbsp;which employs [[mathematics]], [[computer science]], theoretical analysis and abstractions of the brain to understand the principles that govern the [[Developmental neuroscience|development]], [[Neuroanatomy|structure]], [[Neurophysiology|physiology]] and [[Cognitive neuroscience|cognitive abilities]] of the [[nervous system]].<ref name=":0">{{Cite book|title=Fundamentals of Computational Neuroscience|url=https://archive.org/details/fundamentalscomp00ttra|url-access=limited|last=Trappenberg|first=Thomas P.|publisher=Oxford University Press Inc.|year=2010|isbn=978-0-19-851582-1|location=United States|pages=[https://archive.org/details/fundamentalscomp00ttra/page/n17 2]}}</ref><ref>{{cite book |chapter=What is computational neuroscience? |author1=Patricia S. Churchland |author2=Christof Koch |author3=Terrence J. Sejnowski |title=Computational Neuroscience |pages=46–55 |editor1=Eric L. Schwartz |year=1993 |publisher=MIT Press  |url=http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7195 |access-date=2009-06-11 |url-status=dead |archive-url=https://web.archive.org/web/20110604124206/http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=7195 |archive-date=2011-06-04 }}</ref><ref>{{cite book |author1=Dayan P. |author-link=Peter Dayan |author2=Abbott, L. F. |author2-link=Larry Abbott |title=Theoretical neuroscience: computational and mathematical modeling of neural systems |publisher=MIT Press |location=Cambridge, Mass |year=2001 |isbn=978-0-262-04199-7 }}</ref><ref>{{ cite book | author1= Gerstner, W. | author2 = Kistler, W. | author3 = Naud, R. | author4 = Paninski, L.| title = Neuronal Dynamics | publisher = Cambridge University Press | location = Cambridge, UK | year = 2014 | isbn = 9781107447615}}</ref>

Computational neuroscience employs computational simulations<ref>{{Cite journal |last1=Fan |first1=Xue |last2=Markram |first2=Henry |date=2019 |title=A Brief History of Simulation Neuroscience |journal=Frontiers in Neuroinformatics |volume=13 |page=32 |doi=10.3389/fninf.2019.00032 |doi-access=free |issn=1662-5196 |pmc=6513977 |pmid=31133838}}</ref> 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.<ref>{{Cite book|last=Thomas|first=Trappenberg|url=https://books.google.com/books?id=4PDsA1EVCx0C|title=Fundamentals of Computational Neuroscience|publisher=OUP Oxford|year=2010|isbn=978-0199568413|location=|pages=2|access-date=17 January 2017}}</ref> The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field.<ref>{{Cite journal|last1=Gutkin|first1=Boris|last2=Pinto|first2=David|last3=Ermentrout|first3=Bard|date=2003-03-01|title=Mathematical neuroscience: from neurons to circuits to systems|journal=Journal of Physiology-Paris|series=Neurogeometry and visual perception|volume=97|issue=2|pages=209–219|doi=10.1016/j.jphysparis.2003.09.005|pmid=14766142|s2cid=10040483|issn=0928-4257}}</ref>

Computational neuroscience focuses on the description of [[Biology|biologically]] plausible [[neuron]]s (and [[Nervous system|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 network]]s, [[artificial intelligence]] and [[computational learning theory]];<ref>{{Cite journal|last1=Kriegeskorte|first1=Nikolaus|last2=Douglas|first2=Pamela K.|date=September 2018|title=Cognitive computational neuroscience|journal=Nature Neuroscience|language=en|volume=21|issue=9|pages=1148–1160|doi=10.1038/s41593-018-0210-5|pmid=30127428|pmc=6706072|arxiv=1807.11819|bibcode=2018arXiv180711819K|issn=1546-1726}}</ref><ref>{{Citation |last=Paolo |first=E. D. |title=Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop | journal=Dynamical Systems Approach to Embodiment and Sociality |s2cid=15349751 }}</ref>
<ref>{{Cite journal|last1=Brooks|first1=R.|last2=Hassabis|first2=D.|last3=Bray|first3=D.|last4=Shashua|first4=A.|date=2012-02-22|title=Turing centenary: Is the brain a good model for machine intelligence?|journal=Nature|language=En|volume=482|issue=7386|pages=462–463|bibcode=2012Natur.482..462.|doi=10.1038/482462a|issn=0028-0836|pmid=22358812|s2cid=205070106|doi-access=free}}</ref> although mutual inspiration exists and sometimes there is no strict limit between fields,<ref>{{Cite book|url=https://books.google.com/books?id=uV9TZzOITMwC&q=%22biological%20plausibility%22&pg=PA17|title=Neural Network Perspectives on Cognition and Adaptive Robotics|last=Browne|first=A.|date=1997-01-01|publisher=CRC Press|isbn=9780750304559|language=en}}</ref><ref>{{Cite journal|last1=Zorzi|first1=Marco|last2=Testolin|first2=Alberto|last3=Stoianov|first3=Ivilin P.|date=2013-08-20|title=Modeling language and cognition with deep unsupervised learning: a tutorial overview|journal=Frontiers in Psychology|volume=4|pages=515|doi=10.3389/fpsyg.2013.00515|issn=1664-1078|pmc=3747356|pmid=23970869|doi-access=free}}</ref><ref>{{Cite journal|last1=Shai|first1=Adam|last2=Larkum|first2=Matthew Evan|date=2017-12-05|title=Branching into brains|journal=eLife|language=en|volume=6|doi=10.7554/eLife.33066|issn=2050-084X|pmc=5716658|pmid=29205152 |doi-access=free }}</ref> 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 [[neural oscillation|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.