Cerebral cortex inspired representation of neural field network

TL;DR

This paper proposes a cerebral cortex-inspired neural network model that represents memory formation and learning using neural fields and NURBS graphical tools, aiming to enhance real-time intelligent systems.

Contribution

It introduces a novel hypothesis modeling memory creation in the cerebral cortex as neural fields connected in a network, utilizing NURBS for representing these networks.

Findings

Neural fields can model memory and learning in the cerebral cortex.

NURBS effectively represent neural network connections as cubic equations.

Lower-dimensional data patterns improve real-time learning efficiency.

Abstract

Evolution and its intelligence element present thrill and challenges in its exploration. Yet, how species have memory, retrieve them and maintain continuity are the fundamental questions. Most of the phenomenon can only be hypothesised by researchers and validating them through experiments is a big challenge. Taking brain as an ideal intelligent machine and modelling it opens new dimensions for computational algorithm. This paper presents a hypothesis to resemble memory creation in cerebral cortex. The regions of cerebral cortex are implicit to be specific for specific function and constitute neural field that is single dimension and have vector form. The neural field throughout cortex connects with each other to form a network. These networks associate with survival instincts, emotions and rewards to constitute a memory of the exposed environment or say learning. Graphical tool NURBS with multidimensional control points are implicitly used in representing these networks as a set of cubic equations. Learning through data is a primary block of intelligent system, the paper attempts to convert the data in lower dimension patterns rather than existing absolute form for real time intelligent systems.