Continual Weight Updates and Convolutional Architectures for Equilibrium Propagation

by   Maxence Ernoult, et al.

Equilibrium Propagation (EP) is a biologically inspired alternative algorithm to backpropagation (BP) for training neural networks. It applies to RNNs fed by a static input x that settle to a steady state, such as Hopfield networks. EP is similar to BP in that in the second phase of training, an error signal propagates backwards in the layers of the network, but contrary to BP, the learning rule of EP is spatially local. Nonetheless, EP suffers from two major limitations. On the one hand, due to its formulation in terms of real-time dynamics, EP entails long simulation times, which limits its applicability to practical tasks. On the other hand, the biological plausibility of EP is limited by the fact that its learning rule is not local in time: the synapse update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically. Our work addresses these two issues and aims at widening the spectrum of EP from standard machine learning models to more bio-realistic neural networks. First, we propose a discrete-time formulation of EP which enables to simplify equations, speed up training and extend EP to CNNs. Our CNN model achieves the best performance ever reported on MNIST with EP. Using the same discrete-time formulation, we introduce Continual Equilibrium Propagation (C-EP): the weights of the network are adjusted continually in the second phase of training using local information in space and time. We show that in the limit of slow changes of synaptic strengths and small nudging, C-EP is equivalent to BPTT (Theorem 1). We numerically demonstrate Theorem 1 and C-EP training on MNIST and generalize it to the bio-realistic situation of a neural network with asymmetric connections between neurons.


Equilibrium Propagation with Continual Weight Updates

Equilibrium Propagation (EP) is a learning algorithm that bridges Machin...

Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input

Equilibrium Propagation (EP) is a biologically inspired learning algorit...

Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias

Equilibrium Propagation (EP) is a biologically-inspired algorithm for co...

Spatiotemporal Local Propagation

This paper proposes an in-depth re-thinking of neural computation that p...

Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations

Equilibrium propagation (EP) is an alternative to backpropagation (BP) t...

Generalization of Equilibrium Propagation to Vector Field Dynamics

The biological plausibility of the backpropagation algorithm has long be...

Improving equilibrium propagation without weight symmetry through Jacobian homeostasis

Equilibrium propagation (EP) is a compelling alternative to the backprop...

Please sign up or login with your details

Forgot password? Click here to reset