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Computing with Neural Synchrony

Figure 4

Learning in the duration model.

A, In addition to homeostasis, synaptic weights are modified by for every pair of pre and postsynaptic spikes at times tpre and tpost, respectively. B, Presynaptic neurons project to random postsynaptic neurons, with on average 5 synapses per postsynaptic neuron. C, Duration selectivity curves for 5 postsynaptic neurons at the beginning (top) and end (bottom) of the learning period. D, Temporal evolution of the synaptic weights of the neuron corresponding to the blue curves in C. E, Spike latency as a function of stimulus duration for all the presynaptic neurons of the postsynaptic neuron selected in D. Red curves correspond to the two strongest synapses. F, For three postsynaptic neurons (colors as in C), synaptic weights are shown against spike latency of the corresponding presynaptic neurons, at the best duration of the postsynaptic neuron.

Figure 4

doi: https://doi.org/10.1371/journal.pcbi.1002561.g004