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Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Figure 6

Emergent discrimination of handwritten digits through STDP.

A: Examples of digits from the MNIST dataset. The third and fourth row contain test examples that had not been shown during learning via STDP. B: Spike train encoding of the first 5 samples in the third row of A. Colors illustrate the different classes of digits. C, D: Spike trains produced by the output neurons before and after learning with STDP for 500 s. Colored spikes indicate that the class of the input and the class for which the neuron is mostly selective (based on human classification of its generative model shown in F) agree, otherwise spikes are black. E: Temporal evolution of the self-organization process of the 100 output neurons (for the complex version of STDP-curve shown in Fig. 1B), measured by the conditional entropy of digit labels under the learned models at different time points. F: Internal models generated by STDP for the 100 output neurons after 500 s. The network had not received any information about the number of different digits that exist and the colors for different ways of writing the first 5 digits were assigned by the human supervisor. On the basis of this assignment the test samples in row 3 of panel A had been recognized correctly.

doi: https://doi.org/10.1371/journal.pcbi.1003037.g006