Speaker
Description
Feature selectivity is a defining property of cortical computation, and across sensory systems it is often organized in space, from topographic maps to gradients in population codes. In mouse primary visual cortex (V1), orientation selectivity has classically been described as “salt and pepper”, yet recent evidence indicates a subtler spatial organization: nearby neurons share more similar tuning than expected by chance, and correlations can persist over longer distances. Whether this structure reflects explicit feature-dependent wiring or emerges from generic anatomical constraints remains unresolved.
Here, we integrate functional imaging with synaptic-resolution connectomics to constrain mechanistic models of layer 2/3 orientation selectivity in mouse V1. Using the MICrONS dataset, we quantified tuning in excitatory neurons and characterized the spatial correlation profile of preferred orientation across short and long ranges. We then developed a cross-validated probabilistic wiring framework that learns population-specific connection probability and synaptic strength from proofread connectomic data, accounts for incomplete reconstruction by treating missing outputs as censored observations, and generalizes these statistics to full circuit realizations. Preferred-orientation difference was tested as an additional predictor, but distance-dependent models alone matched predictive performance and reproduced the apparent like-to-like modulation.
Embedding these anatomical constraints into a spiking network model of layer 2/3 driven by structured layer-4 input, we reproduced the empirical spatial correlation profile of orientation preference without imposing orientation-dependent synaptic rules. In silico ablations revealed dissociable mechanisms: recurrent L2/3 connectivity shaped the spatial arrangement of similarly tuned neurons without determining selectivity itself; structured feedforward projections were required for stimulus-responsive activity; and disrupting the layer 4 orientation map selectively weakened long-range correlations while preserving short-range clustering. These results suggest that micro- and mesoscale functional organization can emerge from the interaction between distance-dependent wiring and structured feedforward input, providing causal predictions for how local anatomy shapes the spatial scale of cortical computation.
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