This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and ...

The Forward-Forward (FF) technique, which Hinton developed, offers a fresh method for training neural networks, in addition to the studies above focused on distributed backpropagation implementations.

The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and implementation of a back-propagation algorithm using ...

The Forward-Forward algorithm (FF) is comparable in speed to backpropagation but has the advantage that it can be used when the precise details of the forward computation are unknown.

This article proposes a sparsity-driven SNN learning algorithm, namely backpropagation with sparsity regularization (BPSR), aiming to achieve improved spiking and synaptic sparsity. Backpropagation ...

In this paper we describe a backpropagation-less learning approach to train a network of spiking GT neurons by enforcing sparsity constraints on the overall network spiking activity.