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- W1841486360 abstract "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." @default.
- W1841486360 created "2016-06-24" @default.
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- W1841486360 date "2014-01-01" @default.
- W1841486360 modified "2023-09-24" @default.
- W1841486360 title "Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks" @default.
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- W1841486360 hasPublicationYear "2014" @default.
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