J Neurosci. 2025 Nov 5:e1133252025. doi: 10.1523/JNEUROSCI.1133-25.2025. Online ahead of print.
ABSTRACT
Mouse superficial superior colliculus (sSC) has been found to have orientation selective maps, suggesting a fundamentally different selectivity than in primate SC. Moreover, orientation selectivity in mouse sSC appears to change with stimulus properties such as size, shape and spatial frequency, in contradistinction to the computational principle of invariance in primates. To reconcile mouse and primate mechanisms for orientation selectivity, we constructed a computational model of mouse sSC populations with circular-symmetric, center-surround (i.e., not intrinsically orientation selective), stimulus-invariant receptive fields (RFs), classically used to describe monkey lateral geniculate nucleus (LGN) neurons. This model produced population maps similar to those found in mouse sSC, which show strong radial orientation preferences at retinotopic locations along stimulus edges. We show how this selectivity depended critically on spatial frequency tuning of the model units. The model predicted a shift from radial to anti-radial orientation preferences from the same simulated units at high stimulus spatial frequencies, also consistent with measurements from mouse sSC. We found intrinsically oriented RFs were largely unnecessary to explain the imaging data, but could explain a possible small subpopulation of intrinsically orientation selective neurons. We conclude that to study orientation selectivity in mouse sSC and other systems, the problem is not the choice of stimulus. Rather than endless tweaks to find the perfect, unbiased stimulus, image-computable population modeling is the solution. Regardless of the stimulus presented, comparing how well models of intrinsically or non-intrinsically orientation selective units account for empirical data provides definitive evidence for underlying neural selectivity.Significance Statement Measurements of neural population activity from mouse superior colliculus (SC) show patterns of orientation selectivity differing markedly from those observed in primates. Do such measurements necessarily imply different neural mechanisms across species? We developed a modeling framework that explicitly predicts population activity using well-established mechanisms from classic primate single-unit neurophysiology. Notably, this framework was sufficient to explain a diverse array of population measurements in mouse SC. Our results reconcile seemingly contradictory neural phenomena across species and visual areas through a principled approach for making inferences across measurement scales (i.e., single neurons to neural populations), providing a unifying framework for determining shared computational mechanisms broadly throughout the brain.
PMID:41193256 | DOI:10.1523/JNEUROSCI.1133-25.2025