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Moreover, the kinetics of ion channels, maximal conductances, and<jats:italic>Ca<\/jats:italic><jats:sup>2+<\/jats:sup>buffering each have independent temperature sensitivities, suggesting that the balance of biological parameters can be disturbed by even modest temperature changes. In stomatogastric ganglia of the crab<jats:italic>Cancer borealis<\/jats:italic>, the duty cycle of the bursting pacemaker kernel is highly robust between 7 and 23\u00b0C (Rinberg et al., 2013). We examined how this might be achieved in a detailed conductance-based model in which exponential temperature sensitivities were given by<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>parameters. We assessed the temperature robustness of this model across 125,000 random sets of<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>parameters. To examine how robustness might be achieved across a variable population of animals, we repeated this analysis across six sets of maximal conductance parameters that produced similar activity at 11\u00b0C. Many permissible combinations of maximal conductance and<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>parameters were found over broad regions of parameter space and relatively few correlations among<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>s were observed across successful parameter sets. A significant portion of<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>sets worked for at least 3 of the 6 maximal conductance sets (\u223c11.1%). Nonetheless, no<jats:italic>Q<\/jats:italic><jats:sub>10<\/jats:sub>set produced robust function across all six maximal conductance sets, suggesting that maximal conductance parameters critically contribute to temperature robustness. Overall, these results provide insight into principles of temperature robustness in neuronal oscillators.<\/jats:p>","DOI":"10.1523\/jneurosci.0280-14.2014","type":"journal-article","created":{"date-parts":[[2014,4,2]],"date-time":"2014-04-02T17:22:04Z","timestamp":1396459324000},"page":"4963-4975","source":"Crossref","is-referenced-by-count":50,"title":["Many Parameter Sets in a Multicompartment Model Oscillator Are Robust to Temperature Perturbations"],"prefix":"10.1523","volume":"34","author":[{"given":"Jonathan S.","family":"Caplan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex H.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eve","family":"Marder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2014,4,2]]},"reference":[{"key":"2023041304101052000_34.14.4963.1","doi-asserted-by":"publisher","DOI":"10.1126\/science.275.5297.221"},{"key":"2023041304101052000_34.14.4963.2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.0020094"},{"key":"2023041304101052000_34.14.4963.3","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.5297-11.2012"},{"key":"2023041304101052000_34.14.4963.4","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.3347-06.2006"},{"key":"2023041304101052000_34.14.4963.5","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.6435-09.2010"},{"key":"2023041304101052000_34.14.4963.6","doi-asserted-by":"publisher","DOI":"10.1007\/s00359-002-0279-9"},{"key":"2023041304101052000_34.14.4963.7","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-7793.2001.t01-1-00083.x"},{"key":"2023041304101052000_34.14.4963.8","doi-asserted-by":"publisher","DOI":"10.1038\/35036035"},{"key":"2023041304101052000_34.14.4963.9","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.5080-05.2006"},{"key":"2023041304101052000_34.14.4963.10","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-2236(97)01120-X"},{"key":"2023041304101052000_34.14.4963.11","doi-asserted-by":"publisher","DOI":"10.1007\/s00359-006-0162-1"},{"key":"2023041304101052000_34.14.4963.12","unstructured":"Caplan JS (2013) Temperature scaling in pyloric networks: A computational study of a small neural network oscillator and the effects of ion channel temperature dependencies on network performance. 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