Top-down perceptual inference shaping the activity of early visual cortex
Deep discriminative
models have recently provided remarkable insights into hierarchical processing in the brain by predicting neural activity along the visual ventral pathway. However, these models are at odds with biological systems both at the computational and architectural
levels: on the computational level, deep discriminative models rely on supervised learning, which necessitates exhaustive labeling of experiences; and on architectural level, these models are fundamentally feed-forward in processing incoming stimuli, in contrast
with the ventral pathway that is characterized by extensive top-down connectivity. Here, we address these issues by developing a hierarchical deep generative model of natural images and show that it can predict an extensive set of experimental results in the
primary and secondary visual cortices (V1 and V2, respectively). Our analysis shows that sensitivity of V2 neurons to subtle changes of high-level statistics of images is a consequence of learning a hierarchical representation of natural images. Further, we
show that top-down influences are natural ingredients of hierarchical generative models, and a range of experimental phenomena concerning the mean responses and noise correlation structure of V1 responses are a consequence of inference in this generative model.
Time: 17:00, October 25, 2023.
Location: CEU, 1051 Bp. Nádor u. 15, FT. room 408. (restricted entry, guest cards will be available at the
reception) and Zoom (Meeting ID:
913 1139 5317
Passcode: 290844)
Should you have any inquiries about the series, please contact
Mihály
Bányai.