Ensemble priming via competitive inhibition: local mechanisms of sensory context storage and deviance detection in the neocortical column

The process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, often referred to as deviance detection (DD), has long been thought to be the product of specialized cell types and/or routing between mesoscopic brain areas. Here, we explore a different theory, whereby DD emerges from local network-level interactions within a neocortical column. We propose that deviance-driven neural dynamics can emerge through interactions between ensembles of neurons that have a fundamental inhibitory motif: competitive inhibition between reciprocally connected ensembles under modulation from feed-forward selective (dis)inhibition. Using this framework, we were able to simulate a variety of phenomena pertaining to the experimentally observed shifts in neural tuning across neurons, time, and stimulus history. Anchoring our approach in a variety of experimentally observed phenomena, we used computation modeling in two types of neural networks of vastly different levels of biophysical detail to test hypotheses on emergent dynamics and explore the robustness of underlying connectivity parameters. With a number of corollary predictions that can be tested in future in vivo studies, we show that ensemble priming via competitive inhibition under modulation from selective (dis)inhibition acts as a local mechanism for sensory context storage and that DD does not require specialized input from other brain areas—a novel theoretical paradigm that resolves previously confounding aspects of sensory encoding and predictive processing in the neocortex.