Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
Figure 7
Output neurons self-organize via STDP to detect and represent spatio-temporal spike patterns.
A: Sample of the Poisson input spike trains at 20 Hz (only 100 of the 500 input channels are shown). Dashed vertical lines mark time segments of 50 ms length where spatio-temporal spike patterns are embedded into noise. B: Same spike input as in A, but spikes belonging to five repeating spatio-temporal patterns (frozen Poisson spike patterns at 15 Hz) are marked in five different colors. These spike patterns are superimposed by noise (Poisson spike trains at 5 Hz), and interrupted by segments of pure noise of the same statistics (Poisson spike trains at 20 Hz) for intervals of randomly varying time lengths. C, D: Firing probabilities and spike outputs of 6 output neurons (z-neurons in Fig. 1A) for the spike input shown in A, after applying STDP for 200 s to continuous spike trains of the same structure (without any supervision or reward). These 6 output neurons have self-organized so that 5 of them specialize on one of the 5 spatio-temporal patterns. One of the 6 output neurons (firing probability and spikes marked in black) only responds to the noise between these patterns. The spike trains in A represent test inputs, that had never been shown during learning.