Synchronous neural activity in scale-free network models versus random network models.
Synchronous firing peaks at levels greatly exceeding background activity have recently been reported in neocortical tissue. A small subset of neurons is dominant in a large fraction of the peaks. To investigate whether this striking behavior can emerge from a simple model, we constructed and studied a model neural network that uses a modified Hopfield-type dynamical rule. We find that networks having a power-law ("scale-free") node degree distribution readily generate extremely large synchronous firing peaks dominated by a small subset of nodes, whereas random (Erdös-Rényi) networks do not. This finding suggests that network topology may play an important role in determining the nature and magnitude of synchronous neural activity.
Grinstein
Geoffrey
G
IBM Thomas J. Watson Research Center, 1101 Kitchawan Road & Route 134, PO Box 218, Yorktown Heights, NY 10598, USA.
Linsker
Ralph
R
D003198:Computer Simulation; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology*