Topic: Backpropagation through time

Transformative advances in deep learning, such as deep reinforcement learning, usually rely on gradient-based learning methods such as backpropagation through time (BPTT) as a core learning algorithm. However, BPTT is not argued to be biologically plausible, since it requires to a propagate gradients backwards in time and across neurons. Here, we propose e-prop, a novel gradient-based learning method with local and online weight update rules for recurrent neural networks, and in particular recurrent spiking neural networks (RSNNs). As a result, e-prop has the potential to provide a substantial

Artificial recurrent neural networks (RNNs) are difficult to train due to their tendency towards instability, and common training algorithms that tame such networks are not biologically plausible, i.e., back-propagation through time (BPTT). Node perturbation learning in combination with local Hebbian weight updates has been shown to approximate BPTT [1] and, thus, could achieve similar performance while being biologically plausible. However, where these perturbations could arise from and how they are utilized within neurons remains obscure. For years, the neuroscientific community has known ab

Task-trained recurrent spiking neural networks (RSNNs) can provide insights into how the brain performs spike-based computations, especially those involved in temporal tasks. Training RSNNs with backpropagation through time (BPTT) faces the challenge of non-differentiable spiking functions, requiring an approximation gradient through each time state of the network. Evolutionary Algorithms (EAs) offer an alternative to BPTT by generating random populations of models and selecting those with the best performance to provide a broader initial search space and the ability to optimize non-differenti