ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2411.11575
59
0

Analysis of Generalized Hebbian Learning Algorithm for Neuromorphic Hardware Using Spinnaker

18 November 2024
Shivani Sharma
Darshika G. Perera
ArXivPDFHTML
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.

View on arXiv
Comments on this paper