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Poster B65 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Topographic Deep ANN Models Predict the Perceptual Effects of Direct IT Cortical Interventions
Martin Schrimpf1,2 (), Paul McGrath2, Eshed Margalit3, James DiCarlo2; 1EPFL, 2MIT, 3Stanford
Ever-advancing artificial neural network (ANN) models of the ventral visual stream capture core object recognition behavior and the neural mechanisms underlying it with increasing precision. We here extend this modeling approach to make and test predictions of neural intervention experiments. Specifically, we enable a new prediction regime for topographic deep ANN (TDANN) models of primate visual processing through the development of perturbation modules that translate micro-stimulation, optogenetic suppression, and muscimol suppression into changes in model neural activity which unlocks predicting downstream behavioral effects. Without any fitting, we find that TDANN models generated via co-training with both a spatial correlation loss and a standard categorization task qualitatively predict key behavioral results from several primate IT perturbation experiments. In contrast, TDANN models generated via random topography fail to predict nearly all primate results. Taken together, these findings indicate that current topographic deep ANN models paired with perturbation modules are reasonable guides to predict the qualitative results of direct causal experiments in IT.
Keywords: Primate Vision Topographic Deep Neural Networks Cortical Interventions Causal Perturbations