Correlation Between Extreme Learning Machine and Entorhinal Hippocampal System

Abstract

In recent years there has been a considerable interest in exploring the nature of learning and memory system among artificial intelligence researchers and neuroscientists about the neural mechanisms, simulation and enhancement. While a number of studies have investigated the artificial neural networks inspired by biological learning and memory systems, for example the extreme learning machine and support vector machine, seldom research exists examining and comparing the recording neural data and these neural networks. Therefore, the purpose of this exploratory qualitative study is to investigate the extreme learning machine proposed by Huang as a novel method to analyze and explain the biological learning process in the entorhinal hippocampal system, which is thought to play an important role in animal learning, memory and spatial navigation. Data collected from multiunit recordings of different rat hippocampal regions in multiple behavioral tasks was used to analyze the relationship between the extreme learning machine and the biological learning. The results demonstrated that there was a correlation between the biological learning and the extreme learning machine which can contribute to a better understanding of biological learning mechanism.