Post-natal development of high-level visual representation in primates

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View-invariant object recognition is a complex cognitive task that is critical to everyday functioning. A key neural correlate of high-level object recognition is inferior temporal (IT) cortex, a brain area present in both humans and non-human primates. Recent advances in visual systems neuroscience have begun to uncover how images are encoded in the adult IT object representation, however the learning rules by which high level visual areas (especially IT) develop remain mysterious, with both the magnitude and qualitative nature of developmental changes remaining almost completely unknown — in part because, over the last thirty years, there have been practically no studies of spiking neural responses in the higher ventral cortical areas of developing primates. There is thus a significant gap in our understanding of how visual development proceeds. This exploratory proposal aims to characterize how representation in higher primate visual cortex changes during development. We first aim (Aim 1) to implant chronic electrode arrays to record hundreds of IT neuronal sites in response to thousands of image stimuli in awake behaving juvenile macaques. These data will comprise a snapshot of the developing primate visual representation, and will be particularly powerful because we have already extensively measured adult monkey IT using the same stimuli and methods. By comparing juvenile and adult neuronal responses at both single site and population levels, we will obtain a unprecedentedly large-scale and detailed picture of the neural correlates of high-level visual development (Aim 2). Aims 1 and 2 are exploratory, but potentially transformative – they will result in publicly available neuronal IT development benchmarks against which any proposed model of high level visual development can be rigorously tested, and will spur the development of those models in our lab and others. In that context, we will also seek (Aim 3) to improve known semi- and un-supervised learning rules from the computer vision and computational neuroscience literature, and to compare them to both recent high-performing (but biologically implausible) supervised models as well to the rich developmental measurements obtained in Aims 1 and 2. Establishing experimental and surgical procedures for juvenile array recordings will create the future opportunity to observe changes in high level neural visual representations while experience is manipulated in early development, and will enable experiments in other sensory, motor, or decision making domains. If successful, the proposed work will yield a deeper understanding of the principles underlying visual cortex development, understanding which will in turn be helpful for treating neurodevelopmental disorders that implicate cortical circuits, including amblyopia and autism.