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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PLoS computational biology 2023-07, Vol.19 (7), p.e1011212-e1011212
Hauptverfasser: Schneider, Marius, Bird, Alexander D, Gidon, Albert, Triesch, Jochen, Jedlicka, Peter, Cuntz, Hermann
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page e1011212
container_issue 7
container_start_page e1011212
container_title PLoS computational biology
container_volume 19
creator Schneider, Marius
Bird, Alexander D
Gidon, Albert
Triesch, Jochen
Jedlicka, Peter
Cuntz, Hermann
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
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2851974526</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A759349781</galeid><sourcerecordid>A759349781</sourcerecordid><originalsourceid>FETCH-LOGICAL-c568t-309503d5af18d100d635fc1559e136995d22b4c7248d24a396ab171dfd43f19d3</originalsourceid><addsrcrecordid>eNqVkktv1TAQhSMEoi_-AYJIbMriXvyIk3iFSlVopQqkAmvL1x6nrpw4tR3U_vs6umnVi9ggLzyyv5kzOjpF8RajNaYN_nTjpzBItx7Vxq4xwphg8qLYx4zRVUNZ-_JZvVccxHiDUC55_brYow3lnBC0X5x_sd75zirpSuX70cGdTfelkco6m2SCWKZpsENXelOmaygHmILPuuUog-whQSjjKBUcFa-MdBHeLPdh8fvr2a_T89Xlj28XpyeXK8XqNq0o4gxRzaTBrcYI6Zoyo_KiHDCtOWeakE2lGlK1mlSS8lpucIO10RU1mGt6WLzfzh2dj2IxIQrSMsybipE6E58XYtr0oBUMKUgnxmB7Ge6Fl1bs_gz2WnT-j8DZH9pwnCccLxOCv50gJtHbqMA5OYCfZjFKESGczWIf_kL_vdJ6S3XSgbCD8VlY5aOht8oPYGx-P2kYpxVv2nmDjzsNmUlwlzo5xSgufl79B_t9l622rAo-xgDmyRaMxByrx_XFHCuxxCq3vXtu6VPTY47oAx05yQc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2851974526</pqid></control><display><type>article</type><title>Biological complexity facilitates tuning of the neuronal parameter space</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Public Library of Science (PLoS)</source><creator>Schneider, Marius ; Bird, Alexander D ; Gidon, Albert ; Triesch, Jochen ; Jedlicka, Peter ; Cuntz, Hermann</creator><creatorcontrib>Schneider, Marius ; Bird, Alexander D ; Gidon, Albert ; Triesch, Jochen ; Jedlicka, Peter ; Cuntz, Hermann</creatorcontrib><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.</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. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability.</description><subject>Action Potentials - physiology</subject><subject>Analysis</subject><subject>Automation</subject><subject>Biological complexity</subject><subject>Biological research</subject><subject>Biology and Life Sciences</subject><subject>Biology, Experimental</subject><subject>Complexity</subject><subject>Computational neuroscience</subject><subject>Dendritic branching</subject><subject>Dentate gyrus</subject><subject>Excitability</subject><subject>Genetic algorithms</subject><subject>Granular materials</subject><subject>Granule cells</subject><subject>Ion channels</subject><subject>Ion Channels - physiology</subject><subject>Ions</subject><subject>Mathematical models</subject><subject>Models, Neurological</subject><subject>Morphology</subject><subject>Neurons</subject><subject>Neurons - physiology</subject><subject>Parameters</subject><subject>Perturbation</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Social Sciences</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqVkktv1TAQhSMEoi_-AYJIbMriXvyIk3iFSlVopQqkAmvL1x6nrpw4tR3U_vs6umnVi9ggLzyyv5kzOjpF8RajNaYN_nTjpzBItx7Vxq4xwphg8qLYx4zRVUNZ-_JZvVccxHiDUC55_brYow3lnBC0X5x_sd75zirpSuX70cGdTfelkco6m2SCWKZpsENXelOmaygHmILPuuUog-whQSjjKBUcFa-MdBHeLPdh8fvr2a_T89Xlj28XpyeXK8XqNq0o4gxRzaTBrcYI6Zoyo_KiHDCtOWeakE2lGlK1mlSS8lpucIO10RU1mGt6WLzfzh2dj2IxIQrSMsybipE6E58XYtr0oBUMKUgnxmB7Ge6Fl1bs_gz2WnT-j8DZH9pwnCccLxOCv50gJtHbqMA5OYCfZjFKESGczWIf_kL_vdJ6S3XSgbCD8VlY5aOht8oPYGx-P2kYpxVv2nmDjzsNmUlwlzo5xSgufl79B_t9l622rAo-xgDmyRaMxByrx_XFHCuxxCq3vXtu6VPTY47oAx05yQc</recordid><startdate>20230701</startdate><enddate>20230701</enddate><creator>Schneider, Marius</creator><creator>Bird, Alexander D</creator><creator>Gidon, Albert</creator><creator>Triesch, Jochen</creator><creator>Jedlicka, Peter</creator><creator>Cuntz, Hermann</creator><general>Public Library of Science</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><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></search><sort><creationdate>20230701</creationdate><title>Biological complexity facilitates tuning of the neuronal parameter space</title><author>Schneider, Marius ; Bird, Alexander D ; Gidon, Albert ; Triesch, Jochen ; Jedlicka, Peter ; Cuntz, Hermann</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c568t-309503d5af18d100d635fc1559e136995d22b4c7248d24a396ab171dfd43f19d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Action Potentials - physiology</topic><topic>Analysis</topic><topic>Automation</topic><topic>Biological complexity</topic><topic>Biological research</topic><topic>Biology and Life Sciences</topic><topic>Biology, Experimental</topic><topic>Complexity</topic><topic>Computational neuroscience</topic><topic>Dendritic branching</topic><topic>Dentate gyrus</topic><topic>Excitability</topic><topic>Genetic algorithms</topic><topic>Granular materials</topic><topic>Granule cells</topic><topic>Ion channels</topic><topic>Ion Channels - physiology</topic><topic>Ions</topic><topic>Mathematical models</topic><topic>Models, Neurological</topic><topic>Morphology</topic><topic>Neurons</topic><topic>Neurons - physiology</topic><topic>Parameters</topic><topic>Perturbation</topic><topic>Physical Sciences</topic><topic>Physiological aspects</topic><topic>Social Sciences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><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><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium &amp; Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - 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. 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.</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>
fulltext fulltext
identifier ISSN: 1553-7358
ispartof PLoS computational biology, 2023-07, Vol.19 (7), p.e1011212-e1011212
issn 1553-7358
1553-734X
1553-7358
language eng
recordid cdi_plos_journals_2851974526
source MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Public Library of Science (PLoS)
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T07%3A24%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Biological%20complexity%20facilitates%20tuning%20of%20the%20neuronal%20parameter%20space&rft.jtitle=PLoS%20computational%20biology&rft.au=Schneider,%20Marius&rft.date=2023-07-01&rft.volume=19&rft.issue=7&rft.spage=e1011212&rft.epage=e1011212&rft.pages=e1011212-e1011212&rft.issn=1553-7358&rft.eissn=1553-7358&rft_id=info:doi/10.1371/journal.pcbi.1011212&rft_dat=%3Cgale_plos_%3EA759349781%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2851974526&rft_id=info:pmid/37399220&rft_galeid=A759349781&rfr_iscdi=true