On the contributions of retinal direction selectivity to cortical motion processing in mice
Cells preferentially responding to visual motion in a particular direction are said to be direction-selective, and these were first identified in the primary visual cortex. Since then, direction-selective responses have been observed in the retina of several species, including mice, indicating motion analysis begins at the earliest stage of the visual hierarchy. Yet little is known about how retinal direction selectivity contributes to motion processing in the visual cortex. In this talk, I will present our experimental efforts to narrow this gap in our knowledge. To this end, we used genetic approaches to disrupt direction selectivity in the retina and mapped neuronal responses to visual motion in the visual cortex of mice using intrinsic signal optical imaging and two-photon calcium imaging. In essence, our work demonstrates that direction selectivity computed at the level of the retina causally serves to establish specialized motion responses in distinct areas of the mouse visual cortex. This finding thus compels us to revisit our notions of how the brain builds complex visual representations and underscores the importance of the processing performed in the periphery of sensory systems.
Memory for Latent Representations: An Account of Working Memory that Builds on Visual Knowledge for Efficient and Detailed Visual Representations
Visual knowledge obtained from our lifelong experience of the world plays a critical role in our ability to build short-term memories. We propose a mechanistic explanation of how working memory (WM) representations are built from the latent representations of visual knowledge and can then be reconstructed. The proposed model, Memory for Latent Representations (MLR), features a variational autoencoder with an architecture that corresponds broadly to the human visual system and an activation-based binding pool of neurons that binds items’ attributes to tokenized representations. The simulation results revealed that shape information for stimuli that the model was trained on, can be encoded and retrieved efficiently from latents in higher levels of the visual hierarchy. On the other hand, novel patterns that are completely outside the training set can be stored from a single exposure using only latents from early layers of the visual system. Moreover, the representation of a given stimulus can have multiple codes, representing specific visual features such as shape or color, in addition to categorical information. Finally, we validated our model by testing a series of predictions against behavioral results acquired from WM tasks. The model provides a compelling demonstration of visual knowledge yielding the formation of compact visual representation for efficient memory encoding.