MODELING HEBBIAN CELL ASSEMBLIES COMPRISED OF CORTICAL-NEURONS

A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network is comprised of realistically modelled pyramidal-type cells and inhibitory fast-spiking interneurons and its connectivity is adopted from a trained recurrent artificial neural network. After-activity, pattern completion and competition between cell assemblies is readily produced. If, instead of pyramidal cells, motor neuron type cells are used, network behaviour changes drastically. For instance, spike synchronization can be observed but after-activity is hard to produce. Our results support the biological feasibility of Hebb's cell assembly theory. The analogy between this theory and recurrent artificial neural network models is discussed.