Topic: Primary visual cortex (V1)

Recent work has shown that spontaneous movements are represented throughout the brain, even in sensory areas such as primary visual cortex (V1). Interference between visual stimuli and movement is minimized in V1 because they are encoded in orthogonal subspaces, but it is not known how these subspaces are formed. Here, we report that Neuropixels recordings in mouse V1 during slow-wave sleep (SWS) reveal internally generated low-dimensional manifold structure that is mostly preserved in the awake state. However, the awake state has more variance in “off-manifold” dimensions, defined as those ex

Eliciting percepts akin to natural vision using brain computer interfaces is the holy grail of vision prosthetics. However, progress has been slowed by our lack of understanding of how the human visual cortex processes and encodes information as well as how external perturbations, such as electrical stimulation via multi-electrode arrays, might perturb the recurrent cortical dynamics. Furthermore, investigating these questions directly remains difficult as we rarely have the opportunity to probe the human cortex in-vivo. From this limitation and thanks to the current exponential increase in

Activity of sensory neurons is driven not only by stimuli but also by the behavior of the animal. For example, in the primary visual cortex (V1) of a mouse, the gain of sensory responses is modulated by the speed of movement [1]. On average, the response strength increases with running speed - this effect is prominent and systematic. Individual neurons can, however, reveal distinctive patterns of gain modulation. This diversity raises questions about how nuanced the impact of behavior on sensory coding is, whether it is limited to coarse gain changes, and what its computational function might

We propose a novel method to predict contextual modulation of local field potential (LFP) gamma synchronization and multi-unit activity (MUA) in a single framework. Convolutional neural networks are state-of-the-art models of the primary visual cortex (V1). Sensory processing in V1 is determined by contextual modulation via feedback and recurrent connections. However, the computational goal and the neural implementation that shape V1 activity are not yet fully understood. We use transfer learning to use an object recognition convolutional neural network (VGG-16) to predict firing rates and gam