ePoster · Computational Neuroscience
Evolutionary algorithms support recurrent plasticity in spiking neural network models of neocortical task learning
Ivyer Qu, Huaze Liu, Jiayue Li, Yuqing Zhu · Bernstein Conference 2024
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