Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9094
Main Title: Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion
Author(s): Roy, Dipanjan
Jirsa, Viktor
Type: Article
Language Code: en
Abstract: Computational models at different space-time scales allow us to understand the fundamental mechanisms that govern neural processes and relate uniquely these processes to neuroscience data. In this work, we propose a novel neurocomputational unit (a mesoscopic model which tell us about the interaction between local cortical nodes in a large scale neural mass model) of bursters that qualitatively captures the complex dynamics exhibited by a full network of parabolic bursting neurons. We observe that the temporal dynamics and fluctuation of mean synaptic action term exhibits a high degree of correlation with the spike/burst activity of our population. With heterogeneity in the applied drive and mean synaptic coupling derived from fast excitatory synapse approximations we observe long term behavior in our population dynamics such as partial oscillations, incoherence, and synchrony. In order to understand the origin of multistability at the population level as a function of mean synaptic coupling and heterogeneity in the firing rate threshold we employ a simple generative model for parabolic bursting recently proposed by Ghosh et al. (2009). Further, we use here a mean coupling formulated for fast spiking neurons for our analysis of generic model. Stability analysis of this mean field network allow us to identify all the relevant network states found in the detailed biophysical model. We derive here analytically several boundary solutions, a result which holds for any number of spikes per burst. These findings illustrate the role of oscillations occurring at slow time scales (bursts) on the global behavior of the network.
URI: https://depositonce.tu-berlin.de/handle/11303/10106
http://dx.doi.org/10.14279/depositonce-9094
Issue Date: 26-Mar-2013
Date Available: 11-Oct-2019
DDC Class: 600 Technik, Medizin, angewandte Wissenschaften
Subject(s): multispikes
self-organization
transients
firing rate
parabolic burst
network synchrony
generative model
oscillations
Sponsor/Funder: EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleS
License: https://creativecommons.org/licenses/by/3.0/
Journal Title: Frontiers in Computational Neuroscience
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 7
Article Number: 20
Publisher DOI: 10.3389/fncom.2013.00020
EISSN: 1662-5188
Appears in Collections:FG Neuronale Informationsverarbeitung » Publications



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