Topic: leaky integrate and fire neurons

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

Healthy brains exhibit a rich dynamical repertoire with flexible and varied spatiotemporal patterns replays on both microscopic and large scales. Neurodegenerative diseases reduce this functional repertoire. We hypothesize that microscopic dynamics must operate in or near a critical regime for the functional repertoire to be fully explored and for realistic flexible dynamics to emerge. To test this hypothesis, we use a modular Spiking Neuronal Network model, where each group of Leaky Integrate and Fire neurons represents a cortical region. A STDP-based rule is used to learn patterns of activat