Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks
Learning to recognize, predict, and generate spatio-temporal patterns and sequences of spikes is a key feature of nervous systems, and essential for solving basic tasks like localization and navigation. How this can be done by a spiking network, however, remains an open question. Here we present a STDP-based framework extending a previous model , that can simultaneously learn to abstract hidden states from sensory inputs and learn transition probabilities  between these states in recurrent connection weights.
Record created on 2014-03-07, modified on 2016-08-09