Hypotheses of neural code and the information model of the neuron-detector

TL;DR

This paper proposes a new information model for neurons in artificial neural networks based on hypotheses about neural coding, challenging traditional connectionist paradigms and aligning with neuropsychology and neurophysiology.

Contribution

It introduces a novel neuron-detector model and a new paradigm for building and learning neural networks, critiquing existing connectionist approaches.

Findings

The model aligns with neuropsychological and neurophysiological data.

It offers a new perspective on neural coding and network construction.

The approach is validated through theoretical consistency with current neuro sciences.

Abstract

This paper deals with the problem of neural code solving. On the basis of the formulated hypotheses the information model of a neuron-detector is suggested, the detector being one of the basic elements of an artificial neural network (ANN). The paper subjects the connectionist paradigm of ANN building to criticism and suggests a new presentation paradigm for ANN building and neuroelements (NE) learning. The adequacy of the suggested model is proved by the fact that is does not contradict the modern propositions of neuropsychology and neurophysiology.