Topic: Recurrent Spiking Neural Networks

The brain encodes information in the spiking dynamics of recurrently connected inhibitory (I) and excitatory (E) neurons. Recurrent Spiking Neural Networks (RSNNs) have the potential to illuminate mechanisms of neurobiological computation. While inhibition and excitation underlie cortical dynamics, the roles of individual E and I neurons, as well as distinct I neuron subclasses, remain unclear. To elucidate these roles, we created biologically plausible RSNNs with leaky integrate and fire neurons and trained the networks on variations of a temporal task. We find that distinct interneuron subcl

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