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.
Highlights
An ubiquitous motif of cortical microcircuits is ensembles of pyramidal cells with lateral inhibition [1,2,3]
We show here that if the experimentally found lateral excitatory connections between pyramidal cells are taken into account, theoretically optimal probabilistic models for the prediction of time-varying spike input patterns emerge through spike-timing-dependent plasticity (STDP)
We have shown that STDP in WTA circuits with lateral excitatory connections implements the capability to represent the statistical structure underlying time-varying input patterns
Summary
An ubiquitous motif of cortical microcircuits is ensembles of pyramidal cells (in layers 2/3 and in layer 5) with lateral inhibition [1,2,3]. We investigate in this article which computational capabilities emerge in WTA circuits if one takes into account the existence of lateral excitatory synaptic connections within such ensembles of pyramidal cells (Fig. 1A). This augmented architecture will be our default notion of a WTA circuit throughout this paper. We show that this network motif endows cortical microcircuits with the capability to encode and process information in a highly dynamic environment. We show in this article that WTA circuits have emergent coding and computing capabilities that are especially suited for this highly dynamic context of cortical microcircuits
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