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.
IBM Thomas J. Watson Research Center, 1101 Kitchawan Road & Route 134, PO Box 218, Yorktown Heights, NY 10598, USA.
Geoffrey
G
Grinstein
Ralph
R
Linsker
D003198:Computer Simulation; D016571:Neural Networks, Computer; D013269:Stochastic Processes; D009435:Synaptic Transmission / Q000502:physiology*