Simulation studies of the CA3 hippocampal subfield modelled as an attractor neural network

Abstract Real neuronal networks in the brain utilize networks of neurons with graded not binary firing rates. A theoretical analysis of the operation of autoassociative networks with neurons with graded firing rates has therefore been developed. The present simulation study was performed in order to investigate the operation of such a network with values for the asymmetric diluted neuronal connectivity typical of some brain regions such as the hippocampus, which are outside the range to which the theoretical analysis strictly applies. We report that, in line with theoretical predictions, the amount of information that can be retrieved is relatively independent of the resolution of the stored patterns (binary, ternary, decimal, or fifty-fold). The implication of this is that if the network stores many graded patterns, which it can, then the retrieval quality of each of the patterns becomes low. The implications of this trade-off between the number of patterns stored and the retrieval quality of each pattern when graded firing rates are stored for understanding the operation of networks in the hippocampus are considered.

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