Perceptive Functions and Memory in Neural Network Model

Abstract

This paper provides the complete illustration about the observation of new group of distributive memory that is termed as R-nets. These networks are in sparse connection and are very much similar to the Hebbian network. A neural network model of associative memory in a small region of the human brain unconventionally depends, on dis-inhibitation of links between excitation neurons instead of long-term potential of excitation projections. Neural network model may have beneficial advantages over traditional neural network models both in sense of information storage capability and biological plausibility. The distributive memory class called R-nets mainly makes the use of simple common binary neurons and make the links between the excitation neurons and inhibition neurons. This paper is also aimed to show the implementation of associative memory that is capable to store sequential patterns in networks along with the higher perceptive or cognitive functions.