Synchronous neural activity in scale-free network models versus random network models
AUTOR(ES)
Grinstein, Geoffrey
FONTE
National Academy of Sciences
RESUMO
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.
