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 (Erdos-Renyi) networks do not. This finding suggests that network topology may play an important role in determining the nature and magnitude of synchronous neural activity.