Spatio-Temporal Pattern Selectivity from Homeostatic Hebbian Plasticity

It is an open question to what extent neural coding and computation are based on precise patterns of spikes. Theoretically individual neurons can serve as detectors for given spatio-temporal spike patterns, however, this requires supervised adjustment of their input synapses. It is not known if existing activity dependent synaptic plasticity mechanisms can lead to unsupervised emergence of spatio-temporal pattern selectivity. Here, a combination of realistic mechanisms is demonstrated to self-organize the synaptic input e?cacies such that neurons become detectors of patterns repeating in the input. The proposed combination of learning mechanisms yields a balance of excitation and inhibition similar to observations in cortex, robustness of detection against perturbations and noise, and persistence of memory against ongoing plasticity. It enables groups of neurons to incrementally learn sets of noisy patterns thereby faithfully representing their 'which' and 'when' in sequences. These results suggest that computations based on spatio-temporal spike patterns might emerge without any supervision from the synaptic plasticity mechanisms existing in the brain.