Learning probabilistic inference through STDP
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.