Encoding of Complexity, Shape, and Curvature by Macaque Infero-Temporal Neurons

We recorded responses of macaque infero-temporal (IT) neurons to a stimulus set of Fourier boundary descriptor shapes wherein complexity, general shape, and curvature were systematically varied. We analyzed the response patterns of the neurons to the different stimuli using multidimensional scaling. The resulting neural shape space differed in important ways from the physical, image-based shape space. We found a particular sensitivity for the presence of curved versus straight contours that existed only for the simple but not for the medium and highly complex shapes. Also, IT neurons could linearly separate the simple and the complex shapes within a low-dimensional neural shape space, but no distinction was found between the medium and high levels of complexity. None of these effects could be derived from physical image metrics, either directly or by comparing the neural data with similarities yielded by two models of low-level visual processing (one using wavelet-based filters and one that models position and size invariant object selectivity through four hierarchically organized neural layers). This study highlights the relevance of complexity to IT neural encoding, both as a neurally independently represented shape property and through its influence on curvature detection.