Modeling brain dynamics using computational neurogenetic approach

Abstract

The paper introduces a novel computational approach to brain dynamics modeling that integrates dynamic gene-protein regulatory networks with a neural network model. Interaction of genes and proteins in neurons affects the dynamics of the whole neural network. Through tuning the gene-protein interaction network and the initial gene/protein expression values, different states of the neural network dynamics can be achieved. A generic computational neurogenetic model is introduced that implements this approach. It is illustrated by means of a simple neurogenetic model of a spiking neural network of the generation of local field potential. Our approach allows for investigation of how deleted or mutated genes can alter the dynamics of a model neural network. We conclude with the proposal how to extend this approach to model cognitive neurodynamics.

Tackling the social cognition paradox through multi-scale approaches

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Robot coverage control by evolved neuromodulation

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Coupling-induced synchronization in multicellular circadian oscillators of mammals

“…Mathematical modeling can provide insights into potential mechanisms that lead to coordinated cellular oscillations (Benuskova and Kasabov 2008;Jiao and Wang 2006;Liu et al 2010;Matsuura et al 2008;Wang et al 2011a, b). So far there have been a large number of studies on the cellular oscillators' dynamics and synchronization which induce the circadian rhythm generated by the SCN (Aton et al 2005;Maywood et al 2006;Antle et al 2003Antle et al , 2007Bush and Siegelman 2006;Rougemont and Naef 2006;Indic et al 2007;To et al 2007;Yamaguchi et al 2003;Liu and Reppert 2000).…”