Gerstner, WulframCorneil, Dane SterlingNeftci, EmreIndiveri, GiacomoPfeiffer, Michael2014-03-072014-03-072014-03-072014https://infoscience.epfl.ch/handle/20.500.14299/101445Learning 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.text::conference output::conference poster not in proceedings