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In this work we show a study about which processes are related to chaotic and synchronized neural states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). Chaotic neural ensembles are excellent transmission and convolution systems. At the same time, synchronized cells (that can be understood as ordered states of the brain) are associated to more complex non-linear...
This paper presents a novel paradigm for a spiking neural network to forecast temporal sequences. The key to the approach is a new model of a spiking neuron that can make multi-step predictions, using learnable temporal delays at both dendrites and axons. This model is able to learn the temporal structure of space-time events, adaptable to multiple scales, with the neurons able to function asynchronously...
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