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Updated: Jul 4, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Natural textures classification in area V4 of the macaque monkey
F Arcizet1, C Jouffrais, P Girard
1Faculté de Médecine de Rangueil, Université de Toulouse, CerCo, UPS, CNRS, UMR5549, 31062 Toulouse, France. farcizet@mednet.ucla.edu
Abstract:
Natural texture of an object is an important cue for recognition. In real conditions, the incidence angle of light on natural textures leads to a complex pattern of micro-shading that modifies 3D rendering of surfaces. Little is known about visual processing of material properties. The present work aims to study the coding of natural textures by the neurons of area V4 of the awake macaque monkey. We used patches of natural textures issued from the CURET database and illuminated with two or three different angles with their corresponding controls (scrambled Fourier phase). We recorded the responses of V4 neurons to stimuli flashed in their receptive fields (RFs) while the macaques performed a simple fixation task. We show that a large majority of V4 neurons responded to texture patches with a strong modulation across stimuli. The analysis of those responses indicate that V4 neurons integrate first and second order parameters in the image (mean luminance, SNR, and energy), which may be used to achieve texture clustering in a multidimensional space. This clustering was comparable to that of a pyramid of Gabor filters and was not affected by illumination angles. Altogether, these results suggest that the V4 neuronal population acts as a set of filters able to classify textures independently of illumination angle. We conclude that area V4 contains mechanisms that are sensitive to the aspect of textured surfaces, even in an environment where illumination changes continuously.
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