STDP installs in winner-take-all circuits an online approximation to
hidden Markov model learning
D. Kappel, B. Nessler, and W. Maass
In order to cross a street without being run over, we need to be able to
extract very fast hidden causes of dynamically changing multi-modal sensory
stimuli, and to predict their future evolution. We show here that a generic
cortical microcircuit motif, pyramidal cells with lateral excitation and
inhibition, provides the basis for this diffcult but all-important
information processing capability. This capability emerges in the presence of
noise automatically through effects of STDP on connections between pyramidal
cells in Winner-Take-All circuits with lateral excitation. In fact, one can
show that these motifs endow cortical microcircuits with functional
properties of a hidden Markov model, a generic model for solving such tasks
through probabilistic inference. Whereas in engineering applications this
model is adapted to specific tasks through offline learning, we show here
that a major portion of the functionality of hidden Markov models arises
already from online applications of STDP, without any supervision or rewards.
We demonstrate the emergent computing capabilities of the model through
several computer simulations. The full power of hidden Markov model learning
can be attained through reward-gated STDP. This is due to the fact that these
mechanisms enable a rejection sampling approximation to theoretically optimal
learning. We investigate the possible performance gain that can be achieved
with this more accurate learning method for an artificial grammar task.
Reference: D. Kappel, B. Nessler, and W. Maass.
STDP installs in winner-take-all circuits an online approximation to hidden
Markov model learning.
PLOS Computational Biology, 10(3):e1003511, 2014.