Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd.

ABSTRACT: Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints - non-negative weights and sparse coding o

SUBMITTER: Layton OW 

PROVIDER: S-EPMC11403719 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature