Learning probabilistic inference through STDP

D. Pecevski and W. Maass

Abstract:

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be carried out by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity (STDP) in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their ring current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p that generates the examples it receives. This holds even if p contains higher order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore we show, that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference

Reference: D. Pecevski and W. Maass. Learning probabilistic inference through STDP. eNeuro, 2016.