Topic: primary visual cortex (V1)

There remains significant debate about the role of cortical inhibition for visual selectivity and perception [1,2]. A long-standing view states feedforward excitation dictates neural selectivity for visual features, while cortical inhibition reacts with unselective feedback simply to regulate activity levels. Yet much work shows that cortical inhibition drives distinct computational transformations of visual selectivity in excitatory neurons [3-7]. It has yet to be shown if these effects of inhibition on neural selectivity also drive equivalent transformations of visual perceptual sensitivity.

Animals move their head and eyes as they explore and sample the visual environment. Previous studies have demonstrated neural correlates of head and eye movements in rodent primary visual cortex (V1), but the sources and computational roles of these signals are unclear. We addressed this by combining measurement of head and eye movements with high density neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety o

Distinguishing near and far visual cues is an essential computation that animals must carry out to guide behavior using vision. When animals move, self-motion creates motion parallax — an important but poorly understood source of depth information — whereby the speed of optic flow generated by self-motion depends on the depth of visual cues. This enables animals to estimate depth by comparing visual motion and self-motion speeds. As neurons in the mouse primary visual cortex (V1) are broadly modulated by locomotion, we hypothesized that they may integrate visual- and locomotion-related signals

Decades of work has attempted to explain how the representation of visual information is transformed across stages of the cortical processing stream. In some cases, these transformations have been elucidated by insights into how the receptive field properties of individual neurons differ across areas. But the view provided by single neuron selectivity has struggled to explain the transformations occurring from early to mid-level cortex. An alternative, complementary approach is to study the representational geometry of neuronal populations. Though derived from individual neuronal selectivity,

The primary visual cortex (V1) is fundamental for visual processing. Transcriptomics suggests that the microcircuit in V1 consists of many inhibitory and excitatory cell types1. However, exactly how those diverse cell populations participate in visual cognition in vivo is unknown. To address this question, we recorded from V1 of two anesthetized monkeys using high-density Neuropixel probes across all layers of cortex2. We identified single neuron waveforms using Kilosort2, followed by rigorous manual curation. Our final dataset was composed of 801 negative spiking neurons (from 2529 units, 5 s

During visually guided behaviours, hundreds of milliseconds or more can elapse between stimulus onset and a behavioural response. How spikes occurring at different times and in different brain areas are read out to generate perception and guide behaviour remains elusive. To explore temporal relationships between sensory input, neuronal spiking, and behaviour, we recently adapted reverse correlation approaches for optogenetic inhibition. We delivered white noise pulse trains of optogenetic stimulation to excite ChR2-expressing inhibitory neurons in the primary visual cortex (V1) or superior col

Spatial context plays an important role in visual processing and perception. A rich set of surround effects that involve nonlinear interactions between the center and surround have been found in Primary Visual Cortex (V1). A general summary is that surround suppression is prevalent and the amount of suppression depends on the visual similarity between the center and surround. In recent years, convolutional neural networks (CNNs) have successfully modeled neural behavior across the visual cortices. However, there have been limited studies addressing the multitude of contextual effects that have

Sensations often trigger movements, but one’s movements also generate sensory experience. Vestibular organs monitor head movements and contribute to the brain’s ability to distinguish between self- and externally-generated sensory stimuli. Signals from the vestibular organs are broadcasted throughout the brain, including primary sensory regions such as the primary visual cortex (V1). However, what type of signals relative to head movements reach V1 and what the upstream sources of these signals are, remains poorly understood. Here we show that, in the absence of visual input, V1 neurons encode

The responses of mammalian visual system to the increment (ON) and decrement (OFF) of light are asymmetrical. Previous studies have shown that the OFF responses in early visual systems of higher mammals are dominant. Recently, the OFF dominance had been shown in mouse primary visual cortex (V1), but it is unclear whether the feedforward inputs from dorsal lateral geniculate nucleus (dLGN) to V1 in mouse are OFF dominant. Here, we labeled thalamocortical projecting cells in dLGN with calcium indicator and imaged their axonal bouton activity at different depths in V1. By using locally sparse noi

Perception is not a faithful representation of the sensory world; rather, it is an inference of the most likely explanation given sensory evidence. Illusions arise due to rational mistakes in perceptual inference, exemplifying the dichotomy between faithful representation and inferred representation. We utilized illusory contours to ask how and where perceptual inference is encoded in the mouse visual cortex. In illusory contour perception, the perceived whole is greater than the sum of its parts. As such, we hypothesized that brain regions encoding perceptual inference would show enhanced neu

Orientation selectivity is a key feature of primary visual cortex (V1). Orientation specific receptive fields (OSRFs) appear to arise from specific feedforward connectivity from lateral geniculate nucleus (LGN) to V1 cells. OSRF development has been hypothesized to arise from a competition between ON- and OFF-center LGN cells, if the difference between same-type and opposite-type correlation functions is a non-monotonic, "Mexican hat" function of cell-cell separation. However experiments found this difference decays monotonically. Many models have assumed "Mexican hat" recurrent connectivity (

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

The sequential analysis of information in a coarse-to-fine (CtF) manner is a fundamental processing strategy of the visual system. Previous studies have shown that neurons in the primary visual cortex (V1) of anesthetized animals can process spatial information in a CtF fashion, shifting their spatial frequency (SF) preference from low (coarse) to high (fine) throughout their response to static grating stimuli. However, many central questions regarding CtF processing, such as whether it occurs in awake behaving mice and potential computational advantages it may provide, remain unexplored. Here

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

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behavior. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence primary visual cortex (V1) and higher visual area LM had on each other was surprisingly variable over time. Both feedforward and feedback pathways

Visual input to the brain is highly dynamic during natural behavior due to movements of the eyes, head, and body through complex environments. Previous studies have shown that neurons in mouse primary visual cortex (V1) respond to eye and head movements, but how these signals are integrated with visual processing during free movement is unknown since visual physiology is generally performed under conditions of head-fixation. To address this, we performed the first measurements of visual receptive fields (RFs) from freely moving animals by using single-unit electrophysiology in V1 while simulta

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers 2-3 of the primary visual cortex (V1). We investigate the role of four major cortical interneuron classes in a competitive-cooperative computational primitive and validate these circuit motifs implemented soft winner-take-all (sWTA) computation for gain modulation, signal restorati

Psychedelic compounds act upon the serotonergic pathway to generate altered states of consciousness and disrupt normal sensory processing, creating profound perceptual disturbances including visual hallucinations. While psychedelics have recently garnered much attention for their cognitive and therapeutic effects, how psychedelics disrupt sensory processing to evoke hallucinations is not as well characterized. Furthermore, while initial studies have investigated how psychedelics alter visual responses during head-fixation, nothing is known about how they might alter visual processing during ac

While sensory coding is traditionally thought of as a process independent of behavioral state, modern studies showcase potent modulatory effects of movement on sensory neurons. Intriguingly, these effects are not uniform and can differ between species. In insects and rodents, visual responses increase dramatically when the animal is moving, whereas in primates locomotion has a weak impact on responses in the primary visual cortex (V1). These differences raise questions about the existence and universality of computational principles underlying sensory coding during behavior. We take a normativ

A fundamental goal of visual perception is to generate informative representations that support downstream tasks. Doing so requires disentangling the raw image stream into representations that reflect its underlying causes - i.e., form and motion. Therefore, we propose a generative model containing separate latent variables representing form and motion. The model is related to prior work on sparse coding and slow feature analysis, but goes further by positing multiplicative interactions between latent variables for invariant object representations and for equivariant transformations representi

Brain-wide neuromodulation by behavioral variables, such as locomotion, pupil area, and face motion, have been observed in mice (Stringer et al., 2019; Musall et al., 2019). To study this mechanism at higher spatial resolution, we used two-photon imaging that allows recording of individual neuronal and synaptic bouton activity in mouse primary visual cortex (V1), while the animal's face was simultaneously videotaped. We aim to understand how face motion is related to the population activity of both cortical neurons and their lateral geniculate nucleus (LGN) afferents, during both visual stimul

Hippocampus and primary visual cortex (V1) have been shown to represent self-location in a coordinated manner during navigation. Moreover, during rest, spatially-modulated cells in the hippocampus are known to replay past trajectories. These recapitulations of previous experiences are proposed to spread across cortical areas, constituting a hallmark of system-level consolidation theories and providing clear predictions about how hippocampus and cortex coordinate during sleep. We aimed to test these predictions by answering the following questions: 1) Can replay be detected in V1 during sleep?