Analysis of Generalized Hebbian Learning Algorithm for Neuromorphic Hardware Using Spinnaker

TL;DR

This paper investigates the application of the Generalized Hebbian Algorithm in SpiNNaker neuromorphic hardware, demonstrating improved classification accuracy and highlighting the potential of biologically inspired learning methods for efficient neural network implementation.

Contribution

It presents the first large-scale implementation of GHA on SpiNNaker hardware, showing its effectiveness in neuromorphic systems.

Findings

Significant accuracy improvements with GHA on SpiNNaker

Validation of biologically plausible learning in hardware

Potential for energy-efficient neuromorphic computing

Abstract

Neuromorphic computing, inspired by biological neural networks, has emerged as a promising approach for solving complex machine learning tasks with greater efficiency and lower power consumption. The integration of biologically plausible learning algorithms, such as the Generalized Hebbian Algorithm (GHA), is key to enhancing the performance of neuromorphic systems. In this paper, we explore the application of GHA in large-scale neuromorphic platforms, specifically SpiNNaker, a hardware designed to simulate large neural networks. Our results demonstrate significant improvements in classification accuracy, showcasing the potential of biologically inspired learning algorithms in advancing the field of neuromorphic computing.