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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Fig 4

Learning sequential attractor states.

(A) Rastergram and firing rates associated with the first out of 50 epochs of training. (B) Average during training emanating from neurons belonging to the first stimulated pattern as in Fig 3E (indicated with red arrow, colors denote target postsynaptic neuron pattern). Contrast the weight trajectories between patterns with Fig 3F. (C) Average after training that depicts an asymmetrical terminal weight profile. As in (B), the red arrow indicates the presynaptic perspective taken from the first stimulated pattern, which is aligned here at index 4. (D) Rastergram snapshot of excitatory neurons during recall. (E) Relative average firing rates based on (D) and sorted by attractor membership as in Fig 3H. The sequence is chronologically ordered according to the trained patterns from (A). (F) Average firing rate of attractors displaying the sequential progression of the network through state space. (G) Resulting recall after training the network by exchanging for showing the reverse traversal of attractor states from (D-F). Firing rates here and in (E) are coded according to the colorbar from Fig 3H.

doi: https://doi.org/10.1371/journal.pcbi.1004954.g004