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Title of Journal: J Comput Neurosci

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Abbravation: Journal of Computational Neuroscience

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Springer US

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10.1016/0010-406x(68)90663-4

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1573-6873

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Energybased stochastic control of neural mass mod

Authors: Roberto C Sotero Amir Shmuel
Publish Date: 2011/11/01
Volume: 32, Issue: 3, Pages: 563-576
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Abstract

Several studies posit energy as a constraint on the coding and processing of information in the brain due to the high cost of resting and evoked cortical activity This suggestion has been addressed theoretically with models of a single neuron and two coupled neurons Neural mass models NMMs address meanfield based modeling of the activity and interactions between populations of neurons rather than a few neurons NMMs have been widely employed for studying the generation of EEG rhythms and more recently as frameworks for integrated models of neurophysiology and functional MRI fMRI responses To date the consequences of energy constraints on the activity and interactions of ensembles of neurons have not been addressed Here we aim to study the impact of constraining energy consumption during the restingstate on NMM parameters To this end we first linearized the model then used stochastic control theory by introducing a quadratic cost function which transforms the NMM into a stochastic linear quadratic regulator LQR Solving the LQR problem introduces a regime in which the NMM parameters specifically the effective connectivities between neuronal populations must vary with time This is in contrast to current NMMs which assume a constant parameter set for a given condition or task We further simulated energyconstrained stochastic control of a specific NMM the Wilson and Cowan model of two coupled neuronal populations one of which is excitatory and the other inhibitory These simulations demonstrate that with varying weights of the energycost function the NMM parameters show different timevarying behavior We conclude that constraining NMMs according to energy consumption may create more realistic models We further propose to employ linear NMMs with timevarying parameters as an alternative to traditional nonlinear NMMs with constant parametersWe thank Debra Dawson and Laura Betcherman for their comments on an earlier version of the manuscript and for English editing Supported by Industry Canada / MNI Center of excellence in commercialization and research postdoctoral fellowship and grant awarded to RCS and AS respectively and by CIHR grant MOP102599 and Human Frontier Science grant RGY0080/2008 awarded to AS

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