Associative recall and formation of stable modes of activity in neural network models.

Abstract

Models of neural networks with recurrent inhibition are studied, as well as one model which also includes recurrent excitation. The models are intended as possible descriptions of the cerebral cortex. Each network model is composed of neuron models called pyramidal cells and stellate cells in accordance with the names of two types of cells in the cortex. Inputs and outputs of the network are connected to the pyramidal cells while feedback is provided by the stellate cells. Connections within the network are random. During a learning phase the pyramidal cell excitatory synapses become facilitated according to a two-conditional facilitation rule. This is the basis of the model's ability for associative learning. The associative retrieval of information can be studied during a subsequent association phase. This has been done by simulation on a digital computer. It was shown that all of the models considered can be designed to perform a so-called decision-making function. This means that if the associating input pattern is similar to several patterns which occurred during learning the model can decide which similarity is greatest by responding with the appropriate associated pattern. The model also including recurrent excitation differs from the simpler models in that it can become stabilized in so-called stable modes of activity which are self-sustaining and remain even after the input has been turned off. Normally, only one stable mode can be active at a time. However, through careful choice of construction parameters it was possible to obtain a model in which a maximum of two stable modes could be activated independently of each other. Physiological and psychological interpretations are discussed and so are the limitations of the models, which are evident in certain situations.