Deviance detection via competitive inhibition between local neocortical ensembles.

The process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, typically referred to as deviance detection (DD), has traditionally been thought to be the product of specialized cell types and/or routing from distinct brain areas. Here, we explore a different theory, whereby DD emerges intrinsically from local network-level interactions driven by a deviant increase or decrease in exogenous input to a neocortical column. We propose that deviance-driven neural dynamics are generated by ensembles of excitatory and inhibitory neurons that have a fundamental inhibitory connectivity motif: competitive inhibition between reciprocally connected neural representations under modulation from feed-forward selective (dis)inhibition. Implementing this motif in two computational models with different levels of biophysical abstraction, we were able to simulate a variety of phenomena pertaining to the experimentally observed shifts in neural tuning during DD across neurons, time, and stimulus history. We further tested hypotheses related to our theory and examined the robustness of emergent phenomena consistent with prior experimental observations. Our results show that ensemble priming via competitive inhibition under modulation from selective (dis)inhibition can serve as a local mechanism for encoding short-term stimulus memory, enabling deviance-driven shifts in stimulus representation. This work establishes a theoretical paradigm that resolves previously confounding aspects of predictive sensory processing in Neocortex, and we provide a number of corollary predictions that can be tested in future in vivo studies.