Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. (arXiv:2110.13911v1 [q-bio.NC])

Category-selectivity in the brain describes the observation that certain
spatially localized areas of the cerebral cortex tend to respond robustly and
selectively to stimuli from specific limited categories. One of the most well
known examples of category-selectivity is the Fusiform Face Area (FFA), an area
of the inferior temporal cortex in primates which responds preferentially to
images of faces when compared with objects or other generic stimuli. In this
work, we leverage the newly introduced Topographic Variational Autoencoder to
model of the emergence of such localized category-selectivity in an
unsupervised manner. Experimentally, we demonstrate our model yields spatially
dense neural clusters selective to faces, bodies, and places through visualized
maps of Cohen’s d metric. We compare our model with related supervised
approaches, namely the TDANN, and discuss both theoretical and empirical
similarities. Finally, we show preliminary results suggesting that our model
yields a nested spatial hierarchy of increasingly abstract categories,
analogous to observations from the human ventral temporal cortex.



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