Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity

Abstract

In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks. As a model for learning in the brain, however, deep learning has long been regarded as implausible, since it relies in its basic form on a non-local plasticity rule. To overcome this problem, energy-based models with local contrastive Hebbian learning were proposed and tested on a classification task with networks of rate neurons. We extended this work by implementing and testing such a model with networks of leaky integrate-and-fire neurons. Preliminary results indicate that it is possible to learn a non-linear regression task with hidden layers, spiking neurons and a local synaptic plasticity rule.

References in corpus (2)

• Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
• Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation

Cited by in corpus (4)

• Generalization of Equilibrium Propagation to Vector Field Dynamics
• Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation
• SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks
• Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks