STDP enhances learning by backpropagation in a spiking neural network

02/21/2021
by   Kotaro Furuya, et al.
0

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly efficient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which differs from the previous method of using STDP as pre-training interpreted as self-organization.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/17/2018

A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule

Spiking neural networks (SNNs) possess energy-efficient potential due to...
research
07/27/2020

Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation

In this work we propose a new supervised learning method for temporally-...
research
06/04/2020

Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks

For the gradient computation across the time domain in Spiking Neural Ne...
research
04/20/2022

Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks

The information of spiking neural networks (SNNs) are propagated between...
research
11/12/2017

BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity

The problem of training spiking neural networks (SNNs) is a necessary pr...
research
02/16/2022

Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones

This paper demonstrates for the first time that a biologically-plausible...
research
11/07/2016

Adversarial Ladder Networks

The use of unsupervised data in addition to supervised data in training ...

Please sign up or login with your details

Forgot password? Click here to reset