With lateral inhibition, spike-timing-dependent plasticity (STDP) mimics Bayesian independent component analysis (ICA).

<p>(A) Schematic figure of the model with four sources. (B) Synaptic weight development in input neuron space. Arrows <i>q</i>_<i>A</i> to <i>q</i>_<i>D</i> are response probability vectors of the four sources, and PC1 to PC4 are normalized principal components of the correlation matrix C. Lines represent traces of average synaptic weight from each input group to the output groups that learned corresponding sources during the learning process. (C) Comparison of performance among the ideal observer, Bayesian ICA learning, and STDP learning. (D) LTP/LTD time window of Bayesian ICA learning. (E) Behaviors of log-membrane potential (color lines) in the STDP model, and estimated log-posterior (black lines) in the Bayesian ICA algorithm for the same stimuli. Vertical lines represent timings of external events. Log-membrane potentials are normalized to align the mean and the variance to the corresponding log-posteriors.