Biological complexity facilitates tuning of the neuronal parameter space
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are al...
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Veröffentlicht in: | PLoS computational biology 2023-07, Vol.19 (7), p.e1011212-e1011212 |
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description | The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at ~6% vs. ~1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability. |
doi_str_mv | 10.1371/journal.pcbi.1011212 |
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However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at ~6% vs. ~1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1011212</identifier><identifier>PMID: 37399220</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Action Potentials - physiology ; Analysis ; Automation ; Biological complexity ; Biological research ; Biology and Life Sciences ; Biology, Experimental ; Complexity ; Computational neuroscience ; Dendritic branching ; Dentate gyrus ; Excitability ; Genetic algorithms ; Granular materials ; Granule cells ; Ion channels ; Ion Channels - physiology ; Ions ; Mathematical models ; Models, Neurological ; Morphology ; Neurons ; Neurons - physiology ; Parameters ; Perturbation ; Physical Sciences ; Physiological aspects ; Social Sciences</subject><ispartof>PLoS computational biology, 2023-07, Vol.19 (7), p.e1011212-e1011212</ispartof><rights>Copyright: © 2023 Schneider et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Schneider et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Schneider et al 2023 Schneider et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c568t-309503d5af18d100d635fc1559e136995d22b4c7248d24a396ab171dfd43f19d3</citedby><cites>FETCH-LOGICAL-c568t-309503d5af18d100d635fc1559e136995d22b4c7248d24a396ab171dfd43f19d3</cites><orcidid>0000-0003-1610-0201 ; 0000-0002-8405-4131 ; 0000-0001-5445-0507</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353791/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353791/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37399220$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schneider, Marius</creatorcontrib><creatorcontrib>Bird, Alexander D</creatorcontrib><creatorcontrib>Gidon, Albert</creatorcontrib><creatorcontrib>Triesch, Jochen</creatorcontrib><creatorcontrib>Jedlicka, Peter</creatorcontrib><creatorcontrib>Cuntz, Hermann</creatorcontrib><title>Biological complexity facilitates tuning of the neuronal parameter space</title><title>PLoS computational biology</title><addtitle>PLoS Comput Biol</addtitle><description>The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at ~6% vs. ~1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schneider, Marius</au><au>Bird, Alexander D</au><au>Gidon, Albert</au><au>Triesch, Jochen</au><au>Jedlicka, Peter</au><au>Cuntz, Hermann</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Biological complexity facilitates tuning of the neuronal parameter space</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2023-07-01</date><risdate>2023</risdate><volume>19</volume><issue>7</issue><spage>e1011212</spage><epage>e1011212</epage><pages>e1011212-e1011212</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. 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We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37399220</pmid><doi>10.1371/journal.pcbi.1011212</doi><tpages>e1011212</tpages><orcidid>https://orcid.org/0000-0003-1610-0201</orcidid><orcidid>https://orcid.org/0000-0002-8405-4131</orcidid><orcidid>https://orcid.org/0000-0001-5445-0507</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Action Potentials - physiology Analysis Automation Biological complexity Biological research Biology and Life Sciences Biology, Experimental Complexity Computational neuroscience Dendritic branching Dentate gyrus Excitability Genetic algorithms Granular materials Granule cells Ion channels Ion Channels - physiology Ions Mathematical models Models, Neurological Morphology Neurons Neurons - physiology Parameters Perturbation Physical Sciences Physiological aspects Social Sciences |
title | Biological complexity facilitates tuning of the neuronal parameter space |
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