Extending concepts of mapping of human brain to artificial intelligence and neural networks

Abstract

This paper introduces the concept of mapping of Artificially Intelligent (AI) computational systems. The concept of homunculus from human neurophysiology is extended to AI systems. It is assumed that an AI system behaves similarly to a mini-column or ganglion in the natural animal brain that comprises a layer of afferent (input) neurons, a number of interconnecting processing cells, and a layer of efferent (output) neurons or organs. The objective of the present study was to identify the correlation between the stimulus to each afferent neuron and the corresponding response from each efferent organ when the intelligent system is subjected to certain stimuli. To clarify the general concept, a small three-layered feedforward Neural Network (NN) was used as a simple example and an NNculus was built. Two important applications of this concept lie in the quality control of autonomous robots where an NN or AI culi can be built to evaluate their performance and in investigation of the topographic organization in the internal layers of the mini-columns of the human brain through hardware or numerical simulations using artificial NN.