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Motor Cortex Microcircuit Simulation Based on Brain Activity Mapping Open Access (recommended)

Descriptions

Resource type(s)
Journal Article
Keyword
Neuroscience
Neocortex
Rights
Attribution 3.0 United States

Creator
Suter, Benjamin
Chadderdon, George L
Shepherd, Gordon M G
Abstract
The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. Subsequent dynamical analysis confirmed this observation, in addition to revealing network dynamics that cannot be readily predicted through analysis of the wiring diagram alone. Activation thresholds depended on the stimulated layer. Low stimulation produced transient activation, while stronger activation produced sustained oscillations where the threshold for sustained responses varied by layer: 13% in layer 2/3, 54% in layer 5A, 25% in layer 5B, and 17% in layer 6. The frequency and phase of the resulting oscillation also depended on stimulation layer. By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping.
Original Bibliographic Citation
Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW. (2014) Motor Cortex Microcircuit Simulation Based on Brain Activity Mapping. Neural Comput. 26(7):1239-1262.
Related URL
Publisher
Massachusetts Institute of Technology
DigitalHub. Galter Health Sciences Library
Date Created
2013
Original Identifier
(PMID)24708371
Language
English
Subject: MESH
Brain Mapping
Mice
Computer Simulation
Models, Neurological
Motor Cortex
Neural Pathways
Neurons
Periodicity
Synapses
DOI
10.1162/NECO_a_00602
ARK
ark:/c8131/g30016

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