Simple Cortex: A Model of Cells in the Sensory Nervous System

TL;DR

This paper introduces Simple Cortex, a biologically inspired neural model that simulates sensory processing, learning, and prediction, bridging neuroscience principles with artificial intelligence applications.

Contribution

It presents a new neural model, Simple Cortex, based on biological sensory principles, with a software implementation demonstrating rapid learning and prediction capabilities.

Findings

Fast observation, learning, and prediction of sensory-motor patterns

Implementation of spike timing and synaptic plasticity in the model

Potential for future improvements in biologically inspired AI

Abstract

Neuroscience research has produced many theories and computational neural models of sensory nervous systems. Notwithstanding many different perspectives towards developing intelligent machines, artificial intelligence has ultimately been influenced by neuroscience. Therefore, this paper provides an introduction to biologically inspired machine intelligence by exploring the basic principles of sensation and perception as well as the structure and behavior of biological sensory nervous systems like the neocortex. Concepts like spike timing, synaptic plasticity, inhibition, neural structure, and neural behavior are applied to a new model, Simple Cortex (SC). A software implementation of SC has been built and demonstrates fast observation, learning, and prediction of spatio-temporal sensory-motor patterns and sequences. Finally, this paper suggests future areas of improvement and growth for Simple Cortex and other related machine intelligence models.