Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
Figure 5
Emergence of orientation selective cells for visual input consisting of oriented bars with random orientations.
A Examples of -pixel input images with oriented bars and additional background noise. B Internal models (weight vectors of output neurons
) that are learned through STDP after the presentation of
input images (each encoded by spike trains for 50 ms, as in Fig. 3). C, D Plot of the most active neuron for
images of bars with orientations from
to
in
steps. Colors correspond to the colors of
neurons in B. Before training (C), the
output neurons fire without any apparent pattern. After training (D) they specialize on different orientations and cover the range of possible angles approximately uniformly. E: Spike train encoding of the 10 samples in A. F,G: Spike trains produced by the
output neurons in response to these samples before and after learning with STDP for 200 s. Colors of the spikes indicate the identity of the output neuron, according to the color code in B.