Cortical Variability and Challenges for Modeling Approaches.

Balaguer-Ballester, E., 2017. Cortical Variability and Challenges for Modeling Approaches. Frontiers in Systems Neuroscience, 11 (15), pp. 1-5.

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DOI: 10.3389/fnsys.2017.00015

Abstract

The functional role of the observed neuronal variability (the disparity in neural responses across multiple instances of the same experiment) is again receiving close attention in Computational and Systems Neuroscience (e.g., Durstewitz et al., 2010; Moreno-Bote et al., 2011; Oram, 2011; Beck et al., 2012; Churchland and Abbott, 2012; Brunton et al., 2013; Masquelier, 2013; Mattia et al., 2013; Balaguer-Ballester et al., 2014; Renart and Machens, 2014; Bujan et al., 2015; Lin et al., 2015; Pachitariu et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016; McDonnell et al., 2016). Special consideration is currently given to understanding how spiking (Bujan et al., 2015; Deneve and Machens, 2016; Doiron et al., 2016; Hartmann et al., 2016; Landau et al., 2016) and phenomenological (Goris et al., 2014; Lin et al., 2015; Mochol et al., 2015; Arandia-Romero et al., 2016; Doiron et al., 2016) models account for the wide range of classical and new phenomena associated with trial-to-trial uncorrelated activity. Specifically, it has often been proposed that a network state characterized by largely asynchronous spike times whilst maintaining slow oscillations in the firing-rates, may represent the default spontaneous cortical mode (e.g., Sanchez-Vives and Mattia, 2014; Deneve and Machens, 2016; Sancristobal et al., 2016); and similar states could also underlie observed stimulus-driven variability in rate (Litwin Kumar and Doiron, 2012; Deneve and Machens, 2016; Hartmann et al., 2016). However, the way in which such a computationally advantageous network state for neural coding is achieved can differ substantially between modeling approaches; this challenge will be the focus of this manuscript.

Item Type:Article
ISSN:1662-5137
Additional Information:This document is protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.
Uncontrolled Keywords:metastability; excitation-inhibition balance; cortical variability; neuronal variability; recurrent networks; trial to trial variability
Group:Faculty of Science & Technology
ID Code:28620
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:10 Apr 2017 09:50
Last Modified:10 Apr 2017 09:50

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