High-density recording reveals sparse clusters (but not columns) for shape and texture encoding in macaque V4

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Abstract Macaque area V4 includes neurons that exhibit exquisite selectivity for visual form and surface texture, but their functional organization across laminae is unknown. We used high-density Neuropixels probes in two awake monkeys to characterize shape and texture tuning of dozens of neurons simultaneously across layers. We found sporadic clusters of neurons that exhibit similar tuning for shape and texture: ∼20% exhibited similar tuning with their neighbors. Importantly, these clusters were confined to a few layers, seldom ‘columnar’ in structure. This was the case even when neurons were strongly driven, and exhibited robust contrast invariance for shape and texture tuning. We conclude that functional organization in area V4 is not columnar for shape and texture stimulus features and in general organization maybe at a coarse scale (e.g. encoding of 2D vs 3D shape) rather than at a fine scale in terms of similarity in tuning for specific features (as in the orientation columns in V1). We speculate that this may be a direct consequence of the great diversity of inputs integrated by V4 neurons to build variegated tuning manifolds in a high-dimensional space. Significance Statement In primary visual cortex of the macaque monkey, studies have demonstrated columnar functional organization, i.e. shared tuning across layers for stimulus orientation, spatial frequency, ocular dominance, etc. In mid and higher level visual form processing stages, where neurons exhibit high-dimensional tuning, functional organization has been harder to evaluate. Here, leveraging the use of the high-density Neuropixels probes to record simultaneously from dozens of neurons across cortical layers, we demonstrate that functional organization is not columnar for shape and texture tuning in area V4, a midlevel stage critical for form processing. Our results contribute to the debate about the functional significance of cortical columns providing support to the idea that they emerge due to one-to-many representational expansion.

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