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Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Fig 6

Learning a complex sequence.

(A) Target sequence (top). The amplitude shows the rate of the Poisson input to the supervisor neurons and is normalized between 0 and 10 kHz. Rate of read-out neurons for one sample reactivation after learning 6 seconds (bottom). 45 read-out neurons encode the different frequencies in the song. Neuron i encodes a frequency interval of [684 + 171i, 855 + 171i]Hz. (B) The read-out weight matrix after learning 6 seconds. (C) Sequence replays showing the spike trains of both the recurrent network neurons (top, excitatory neurons in red and inhibitory neurons in blue), and the read-out neurons (bottom).

Fig 6

doi: https://doi.org/10.1371/journal.pcbi.1007606.g006