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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Steerable autoencoders model saccadic remapping and visual stability by linking identity to location in visual cortex

Tim V. Nguyen¹, Patrick Cavanagh², Yalda Mohsenzadeh¹; ¹University of Western Ontario, ²York University, Glendon College, Dartmouth College

Presenter: Tim V. Nguyen

Whenever the eyes move, the locations of attended targets must be updated to keep track of them despite their shift on the retina, a process called remapping. Here we show that each attended target’s identity can be connected to the locations of its features in early visual cortex through a steerable autoencoder model. The model is trained to appropriately shift the feature activity to the target’s new location with each saccade. It takes an input pattern, here from early retinotopic cortex, passes it through several layers of encoding to generate a high-level, low-dimensional representation that corresponds to the object areas of visual cortex. The model receives and integrates the upcoming saccadic movement information in this layer. This then returns through decoding layers to project the target’s feature activity to its expected post-saccadic location in early visual cortex. In other words, the autoencoder predicts upcoming target locations following eye movements by using distributed information about eye position (gain fields). This process may be part of the visual system’s architecture, linking target properties across changes in retinal position as the eyes move.

Topic Area: Computational Models of Vision & Visual Cortex