Abstract

Here we develop and investigate a computational model of a network of cortical neurons on the base of biophysically well constrained and tested two-compartmental neurons developed by Pinsky and Rinzel [Pinsky and Rinzel, 1994]. To study associative memory we connect a pool of cells by a structured connectivity matrix. The connection weights are shaped by simple Hebbian coincidence learning using a set of spatially sparse patterns. We study the neuronal activity processes following an external stimulation of a stored memory. In two series of simulation experiments we explore the effect of different classes of external input, tonic and flashed stimulation: With tonic stimulation the addressed memory is attractor of the network dynamics. The memory is displayed rhythmically, coded by phase locked bursts or regular spikes. The participating neurons have rhythmic activity in the gamma-frequency range (30-80 Hz). If the input is switched from one memory to another, the network act...

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