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dc.contributor.authorTabak, J
dc.contributor.authorRinzel, J
dc.contributor.authorBertram, R
dc.date.accessioned2016-04-04T15:36:22Z
dc.date.issued2011-04-21
dc.description.abstractBiological systems are characterized by a high number of interacting components. Determining the role of each component is difficult, addressed here in the context of biological oscillations. Rhythmic behavior can result from the interplay of positive feedback that promotes bistability between high and low activity, and slow negative feedback that switches the system between the high and low activity states. Many biological oscillators include two types of negative feedback processes: divisive (decreases the gain of the positive feedback loop) and subtractive (increases the input threshold) that both contribute to slowly move the system between the high- and low-activity states. Can we determine the relative contribution of each type of negative feedback process to the rhythmic activity? Does one dominate? Do they control the active and silent phase equally? To answer these questions we use a neural network model with excitatory coupling, regulated by synaptic depression (divisive) and cellular adaptation (subtractive feedback). We first attempt to apply standard experimental methodologies: either passive observation to correlate the variations of a variable of interest to system behavior, or deletion of a component to establish whether a component is critical for the system. We find that these two strategies can lead to contradictory conclusions, and at best their interpretive power is limited. We instead develop a computational measure of the contribution of a process, by evaluating the sensitivity of the active (high activity) and silent (low activity) phase durations to the time constant of the process. The measure shows that both processes control the active phase, in proportion to their speed and relative weight. However, only the subtractive process plays a major role in setting the duration of the silent phase. This computational method can be used to analyze the role of negative feedback processes in a wide range of biological rhythms.en_GB
dc.description.sponsorshipThis work is supported by National Institutes of Health (NIH) grant DK043200 (JT, RB). JR values the hospitality and resources provided by the Laboratory of Biological Modeling, NIDDK-IR, on the NIH campus. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_GB
dc.identifier.citationPLoS Computational Biology, 2011, Vol. 7 (4): e1001124en_GB
dc.identifier.doi10.1371/journal.pcbi.1001124
dc.identifier.urihttp://hdl.handle.net/10871/20954
dc.language.isoenen_GB
dc.publisherPublic Library of Scienceen_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/21533065en_GB
dc.rightsThis is the final version of the article. Available from PLoS via the DOI in this record.en_GB
dc.subjectAction Potentialsen_GB
dc.subjectAnimalsen_GB
dc.subjectComputational Biologyen_GB
dc.subjectComputer Simulationen_GB
dc.subjectHumansen_GB
dc.subjectModels, Biologicalen_GB
dc.subjectModels, Neurologicalen_GB
dc.subjectModels, Statisticalen_GB
dc.subjectModels, Theoreticalen_GB
dc.subjectNeural Networks (Computer)en_GB
dc.subjectNeuronsen_GB
dc.subjectOscillometryen_GB
dc.subjectSoftwareen_GB
dc.titleQuantifying the relative contributions of divisive and subtractive feedback to rhythm generation.en_GB
dc.typeArticleen_GB
dc.date.available2016-04-04T15:36:22Z
dc.identifier.issn1553-734X
exeter.place-of-publicationUnited States
dc.descriptionPublisheden_GB
dc.descriptionResearch Support, N.I.H., Extramuralen_GB
dc.identifier.journalPLoS Computational Biologyen_GB


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