title: STDP installs in Winner-Take-All circuits an online approximation to hidden Markov model learning.
abstract: 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 difficult 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.
authors.affiliation: Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
authors.forename: David
authors.initials: D
authors.lastname: Kappel
authors.affiliation: Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Nessler
authors.affiliation: Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Maass
mesh_terms: D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D019706:Excitatory Postsynaptic Potentials; D006801:Humans; D007802:Language; D007858:Learning; D008390:Markov Chains; D015233:Models, Statistical; D009415:Nerve Net; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology; D011336:Probability; D017966:Pyramidal Cells; D012201:Reward; D013569:Synapses / Q000502:physiology