Observer-participant models of neural processing

A model is proposed in which the neuron serves as an information channel. Channel distortion occurs through the channel since the mapping from input Boolean codes to output codes are many-to-one in that neuron outputs consist of just two distinguished states. Within the described model, the neuron performs a decision-making function. Decisions are made regarding the validity of a question passively posed by the neuron. This question becomes defined through learning hence learning is viewed as the process of determining an appropriate question based on supplied input ensembles. An application of the Shannon information measures of entropy and mutual information taken together in the context of the proposed model lead to the Hopfield neuron model with conditionalized Hebbian learning rules. Neural decisions are shown to be based on a sigmoidal transfer characteristic or, in the limit as computational temperature tends to zero, a maximum likelihood decision rule. The described work is contrasted with the information-theoretic approach of Linsker.