Image2MUA: Generative Modeling of Trial-level Macaque MUA with Area Structured Visual Expert Routing

Understanding how visual stimuli shape neural population activity is a central goal of visual neuroscience. Visual encoding models predict mean responses to visual stimuli accurately, but mean responses alone do not capture response variability across repeated presentations of the same stimulus. Capturing these trial-level responses across cortical areas requires modeling correlated fluctuations while accommodating different visual feature preferences. We introduce IMAGE2MUA, a conditional rectified flow model that learns to generate joint multi-unit activity (MUA) in macaque primary visual cortex (V1), visual area V4, and inferior temporal (IT) cortex from one response per training image. IMAGE2MUA combines joint response generation with visual expert routing, which learns how individual channels draw on different visual representations. Across two macaques, its samples capture both trial to trial variability within individual channels and pooled residual correlations between channels, while their averages outperform the evaluated deterministic encoding baselines in mean response prediction. Learned visual expert preferences differ across cortical areas, even when area labels are not provided during training. Together, these results show that a single model can capture population variability across trials while making its use of visual representations explicit for each channel.