Institute for Theoretical Computer Science, Graz University of TechnologyThe principles by which spiking neurons contribute to the astounding computational
power of generic cortical microcircuits, and how spike-timing-dependent
plasticity (STDP) of synaptic weights could generate and maintain this computational
function, are unknown. We show here that STDP, in conjunction with
a stochastic soft winner-take-all (WTA) circuit, induces spiking neurons to generate
through their synaptic weights implicit internal models for subclasses (or
causes) of the high-dimensional spike patterns of hundreds of pre-synaptic neurons.
Hence these neurons will fire after learning whenever the current input best
matches their internal model. The resulting computational function of soft WTA
circuits, a common network motif of cortical microcircuits, could therefore be
a drastic dimensionality reduction of information streams, together with the autonomous
creation of internal models for the probability distributions of their input
patterns. We show that the autonomous generation and maintenance of this
computational function can be explained on the basis of rigorous mathematical
principles. In particular, we show that STDP is able to approximate a stochastic
online Expectation-Maximization (EM) algorithm for modeling the input data. A
corresponding result is shown for Hebbian learning in artificial neural networks.
