Controlling Complexity of Cerebral Cortex Simulations-II : Streamlined Microcircuits

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Hokkanen , H , Andalibi , V & Vanni , S 2019 , ' Controlling Complexity of Cerebral Cortex Simulations-II : Streamlined Microcircuits ' , Neural Computation , vol. 31 , no. 6 , pp. 1066-1084 . https://doi.org/10.1162/neco_a_01188

Title: Controlling Complexity of Cerebral Cortex Simulations-II : Streamlined Microcircuits
Author: Hokkanen, Henri; Andalibi, Vafa; Vanni, Simo
Contributor: University of Helsinki, Neurologian yksikkö
University of Helsinki, Clinicum
University of Helsinki, Department of Neurosciences
Date: 2019-06
Language: eng
Number of pages: 19
Belongs to series: Neural Computation
ISSN: 0899-7667
URI: http://hdl.handle.net/10138/304810
Abstract: Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear. We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and GABAB and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (GABAB) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time. Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.
Subject: PYRAMIDAL NEURONS
DYNAMICS
CELLS
INTERNEURONS
NOMENCLATURE
NETWORKS
MODEL
3112 Neurosciences
113 Computer and information sciences
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