Visual hierarchy

…influence neuronal activity in the visual cortex[1,2]. However, the mechanisms behind these influences and their consequent effect on sensory encoding remain to be determined. Here, we address this issue by analyzing simultaneous recordings of spiking activity and local field potentials (LFPs) from visual and subcortical regions of (N = 25) awake mice viewing a naturalistic movie. Applying a hidden Markov model (HMM) to cortical LFPs, we consistently identified three stable states in each mouse: a high-frequency state (dominated by gam

…in inter-areal correlations along the hierarchy. A second proposal is an improvement in information capacit

The functional visual hierarchy has been widely studied in primates but was only recently revealed in mice. It is thus unclear whether different areas along the mouse visual hierarchy also represent distinct levels of visual abstraction. To address this question, we carefully designed a visual stimulus set consisting of natural images of diverse objects, synthetic textures, and corresponding noise from a range of families. We acquired and analyzed a novel electrophysiology dataset with ~13,000 neurons recorded across six visual cortical regions along the visual hierarchy in awake mice using Ne

The size of a neuron’s receptive field increases along the visual hierarchy. Neurons in higher-order visual areas integrate information through a canonical mechanism called normalization, where neurons respond sublinearly to multiple stimuli in the receptive field (Carandini & Heeger, 2012). Neurons in visual cortex exhibit highly heterogeneous degrees of normalization, which can be measured by normalization index, defined as the sum of a neuron’s firing rate to each one of two stimuli divided by its firing rate when both stimuli are presented. Interestingly, it has been demonstrated that

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