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Inhibitory networks orchestrate the self-organization of computational function in cortical microcircuit motifs through STDP

Interneurons have diverse morphological and physiological characteristics that potentially contribute to the emergence of powerful computational properties of cortical networks. We investigate the functional role of inhibitory subnetworks in the arguably most common network motif of cortical microcircuits: ensembles of pyramidal cells (PCs) with lateral inhibition, commonly referred to as Winner-Take-All networks. Recent theoretical work has shown that spike-timing-dependent plasticity installs in this network motif an important and ubiquitously useful self-organization process: The emergence of sparse codes and Bayesian inference for repeatedly occurring high-dimensional input patterns. However, this link has so far only been established for strongly simplified models with a symbolic implementation of lateral inhibition, rather than through the interaction of PCs with known types of interneurons. We close this gap in this article, and show that the interaction of PCs with two types of inhibitory networks, that reflect salient properties of somatic-targeting neurons (e.g. basket cells) and dendritic-targeting neurons (e.g. Martinotti cells), provides a good approximation to the theoretically optimal lateral inhibition needed for the self-organization of these network motifs. We provide a step towards unraveling the functional roles of interacting networks of excitatory and inhibitory neurons from the perspective of emergent neural computation.