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Stimulus presentation can enhance spiking irregularity across subcortical and cortical regions

Fayaz, S ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1371/journal.pcbi.1010256
  3. Publisher: Public Library of Science , 2022
  4. Abstract:
  5. Stimulus presentation is believed to quench neural response variability as measured by fano-factor (FF). However, the relative contributions of within-trial spike irregularity and trial-to-trial rate variability to FF fluctuations have remained elusive. Here, we introduce a principled approach for accurate estimation of spiking irregularity and rate variability in time for doubly stochastic point processes. Consistent with previous evidence, analysis showed stimulus-induced reduction in rate variability across multiple cortical and subcortical areas. However, unlike what was previously thought, spiking irregularity, was not constant in time but could be enhanced due to factors such as bursting abating the quench in the post-stimulus FF. Simulations confirmed plausibility of a time varying spiking irregularity arising from within and between pool correlations of excitatory and inhibitory neural inputs. By accurate parsing of neural variability, our approach reveals previously unnoticed changes in neural response variability and constrains candidate mechanisms that give rise to observed rate variability and spiking irregularity within brain regions. © 2022 Fayaz 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
  6. Keywords:
  7. Brain ; % reductions ; Accurate estimation ; Cortical regions ; Doubly stochastic ; Evidence analysis ; Fano factor ; Neural response ; Relative contribution ; Response variability ; Stochastic point process ; Stochastic systems ; Brain region ; Human ; Nerve potential ; Simulation ; Stochastic model ; action potential ; Biological model ; Markov chain ; Nerve cell ; Physiology ; Action Potentials ; Models, Neurological ; Neurons ; Stochastic Processes
  8. Source: PLoS Computational Biology ; Volume 18, Issue 7 , 2022 ; 1553734X (ISSN)
  9. URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010256