Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks
Abstract
A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications. This paper studies the generalizability properties of the STDP learning processes using the Hausdorff dimension of the trajectories of the learning algorithm. The paper analyzes the effects of STDP learning models and associated hyper-parameters on the generalizability properties of an SNN. The analysis is used to develop a Bayesian optimization approach to optimize the hyper-parameters for an STDP model for improving the generalizability properties of an SNN.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 29, 2021
- Source ID
- 10.3389/fnins.2021.695357
Entities
People
- Biswadeep Chakraborty
- Saibal Mukhopadhyay
Organizations
- Army Research Office