Cell-type-specific encoding of prediction and reward in cortical microcircuits during novelty detection

Cortical circuits comprise diverse neuron types whose distinct activity patterns suggest specialized computational roles. Recent large-scale recordings reveal striking, cell-type-specific responses to novelty in cortex: excitatory neurons respond strongly to novel and unexpected stimuli, VIP interneurons respond more to novelty and omissions, while SST neurons are suppressed. What computational principles give rise to these dynamics? We introduce a normative model of a canonical cortical micro-circuit that jointly optimizes predictive coding, energy efficiency, and reinforcement learning under realistic connectivity constraints. By mapping algorithmic roles onto specific interneuron subtypes, the model reproduces absolute, contextual, and omission novelty effects observed experimentally. Critically, these emerge without hard-coded novelty detection and reveal a computational role for the VIP-SST disinhibitory motif in balancing representational capacity and metabolic cost. Mechanistic alternatives relying only on adaptation and Hebbian learning capture contextual but not absolute or omission effects. Our framework provides a unifying, falsifiable theory of how diverse cortical cell types implement coding principles underlying expectation and surprise.