Biologically grounded neocortex computational primitives implemented on neuromorphic hardware improve vision transformer performance.

Understanding the computational principles of the brain and translating them into neuromorphic hardware and modern deep learning architectures is critical for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally constrained, biophysically realistic model of neocortical microcircuits in the mouse primary visual cortex (layers 2 to 3) to examine how four major interneuron classes-Parvalbumin, Somatostatin, vasoactive intestinal peptide, and LAMP5-interact within a competitive-cooperative motif to implement soft winner-take-all (sWTA) circuit dynamics. In a conductance-based network grounded in in vitro physiology, we show that this circuit motif selectively amplifies strong inputs while suppressing, without silencing, weaker ones, enabling gain modulation, signal restoration, and context-dependent multistability. Using a gain-matching strategy, we mapped the motif onto IBM's TrueNorth neuromorphic chip, revealing a principled correspondence between cell-type-specific roles and hardware primitives. Sparse coupling of sWTA modules generated persistent up-states and a two-state neural machine approximating working memory. We then embedded the sWTA circuit as a preprocessing filter in a Vision Transformer, which significantly enhanced out-of-distribution generalization across diverse tasks, including zero-shot digit classification, cross-domain transfer between digit datasets, and nighttime semantic segmentation. The sWTA filter boosted accuracy on unseen data by up to ~20% and reduced training compute by directing learning toward salient features, without additional data or architectural changes. By unifying biophysically grounded circuit models, neuromorphic implementation, and state-of-the-art AI architectures, this work outlines a generalizable roadmap for embedding cortical computation into next-generation NeuroAI systems that combine biological principles with practical AI performance gains.