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Spike-Based Bayesian-Hebbian Learning of Temporal Sequences
Fig 5
The temporal structure of neural recall dynamics reflects the temporal interval used during training.
(A) Average 
after training as in Fig 4C (reproduced here by the 0 ms line) except now depicting terminal weight profiles for many differently trained networks with IPIs varying between 0 and 2000 ms. (B) CRP curves calculated for networks with representative IPIs = 0, 500, 1000, 1500 and 2000 ms after 1 minute of recall, with colors corresponding to (A). Increasing IPIs flattened the CRP curve, promoting attractor transition distribution evenness. Error bars reflect standard deviations. (C) Average strength of 
taken across entire networks after training for different IPIs, where the number of NMDA synapses in these separate networks was constant. (D) Average dwell times μdwell measured during 1 minute recall periods for entire networks trained with different IPIs. Shaded areas denote standard deviations here and in (E). (E) Average neural firing rates for attractors with dwell times corresponding to those measured in (D).
doi: https://doi.org/10.1371/journal.pcbi.1004954.g005