A Physiological Neural Network as an Autoassociative Memory
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We consider a neural network model in which the single neurons are chosen to resemble closely known physiological properties. The neurons are assumed to be linked by synapses which change their strength according to Hebbian rules  on a short time scale (100ms) . Each nerve cell receives input from a primary set of receptors, which offer learning and test patterns without changing their own properties. The activity of the neurons is interpreted as the output of the network (see Fig.1). The backward bended arrows in Fig.1 indicate the feed-back due to the effect of the neuron activity on the synaptic strengths Sik between neuron k and i in the neural network.
KeywordsCell Potential Synaptic Strength Excitatory Synapse Postsynaptic Cell Hebbian Rule
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