Topic: visual hierarchy

Recent experiments demonstrate that the brain transforms visual inputs into representations that follow straighter temporal trajectories than their initial photoreceptor encoding, facilitating prediction by linear extrapolation (Hénaff et al 2019; 2021). Can the brain use this principle to learn visual representations? Here, we develop an objective that quantifies straightening, augment it with a regularizer to prevent collapse to trivial solutions, and use it to train deep feedforward neural networks on video sequences. Decoding of the learned representation with a separately trained readout

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

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 r