The GAIN Model: A Nature-Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model

TL;DR

The paper introduces GAIN, a biologically plausible neural network framework using a grid structure and Izhikevich model adaptation to enhance neural dynamics and efficiency.

Contribution

It presents a novel grid-based neural network model inspired by biological neurons, integrating the Izhikevich model for improved dynamics and computational efficiency.

Findings

Enhanced biological plausibility through grid structure

Improved neural dynamics and accuracy

Efficient large-scale neural simulations

Abstract

While many neural networks focus on layers to process information, the GAIN model uses a grid-based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and improve their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient.