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conference poster not in proceedings
Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks
2014
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 [1], that can simultaneously learn to abstract hidden states from sensory inputs and learn transition probabilities [2] between these states in recurrent connection weights.
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Name
Cosyne14_Corneil.pdf
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openaccess
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4.2 MB
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Adobe PDF
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