Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8551
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dc.contributor.authorBernáez Timón, Laura-
dc.contributor.authorEkelmans, Pierre-
dc.contributor.authorKraynyukova, Nataliya-
dc.contributor.authorRose, Tobias-
dc.contributor.authorBusse, Laura-
dc.contributor.authorTchumatchenko, Tatjana-
dc.date.accessioned2023-01-25T08:52:07Z-
dc.date.available2023-01-25T08:52:07Z-
dc.date.issued2022-
dc.identifier.urihttps://openscience.ub.uni-mainz.de/handle/20.500.12030/8567-
dc.description.abstractDue to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.en_GB
dc.description.sponsorshipGefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577de
dc.language.isoengde
dc.rightsCC BY-NC*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subject.ddc610 Medizinde_DE
dc.subject.ddc610 Medical sciencesen_GB
dc.titleHow to incorporate biological insights into network models and why it mattersen_GB
dc.typeZeitschriftenaufsatzde
dc.identifier.doihttp://doi.org/10.25358/openscience-8551-
jgu.type.dinitypearticleen_GB
jgu.type.versionPublished versionde
jgu.type.resourceTextde
jgu.organisation.departmentFB 04 Medizinde
jgu.organisation.number2700-
jgu.organisation.nameJohannes Gutenberg-Universität Mainz-
jgu.rights.accessrightsopenAccess-
jgu.journal.titleThe journal of physiologyde
jgu.journal.volumeVersion of Record (VoR)de
jgu.publisher.year2022-
jgu.publisher.nameWiley-Blackwellde
jgu.publisher.placeHoboken, NJde
jgu.publisher.issn1469-7793de
jgu.organisation.placeMainz-
jgu.subject.ddccode610de
jgu.publisher.doi10.1113/JP282755de
jgu.organisation.rorhttps://ror.org/023b0x485-
jgu.subject.dfgLebenswissenschaftende
Appears in collections:DFG-491381577-H

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