Cortical Areas V1 Research Articles

Local stimulation of pyramidal neurons in deep cortical layers of anesthetized rats enhances cortical visual information processing

In the primary visual cortex area V1 activation of inhibitory interneurons, which provide negative feedback for excitatory pyramidal neurons, can improve visual response reliability and orientation selectivity. Moreover, optogenetic activation of one class of interneurons, parvalbumin (PV) positive cells, reduces the receptive field (RF) width. These data suggest that in V1 the negative feedback improves visual information processing. However, according to information theory, noise can limit information content in a signal, and to the best of our knowledge, in V1 signal-to-noise ratio (SNR) has never been estimated following either pyramidal or inhibitory neuron activation. Therefore, we optogenetically activated pyramidal or PV neurons in the deep layers of cortical area V1 and measured the SNR and RF area in nearby pyramidal neurons. Activation of pyramidal or PV neurons increased the SNR by 267% and 318%, respectively, and reduced the RF area to 60.1% and 77.5%, respectively, of that of the control. A simple integrate-and-fire neuron model demonstrated that an improved SNR and a reduced RF area can increase the amount of information encoded by neurons. We conclude that in V1 activation of pyramidal neurons improves visual information processing since the location of the visual stimulus can be pinpointed more accurately (via a reduced RF area), and more information is encoded by neurons (due to increased SNR).

Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance.

Recent visual experience heavily influences our visual perception, but how neuronal activity is reshaped to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while two male rhesus macaque monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in the mid-level visual cortex.

Recent visual experience reshapes V4 neuronal activity and improves perceptual performance.

Recent visual experience heavily influences our visual perception, but how this is mediated by the reshaping of neuronal activity to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in mid-level visual cortex.

Multiple objects evoke fluctuating responses in several regions of the visual pathway.

How neural representations preserve information about multiple stimuli is mysterious. Because tuning of individual neurons is coarse (e.g., visual receptive field diameters can exceed perceptual resolution), the populations of neurons potentially responsive to each individual stimulus can overlap, raising the question of how information about each item might be segregated and preserved in the population. We recently reported evidence for a potential solution to this problem: when two stimuli were present, some neurons in the macaque visual cortical areas V1 and V4 exhibited fluctuating firing patterns, as if they responded to only one individual stimulus at a time (Jun et al., 2022). However, whether such an information encoding strategy is ubiquitous in the visual pathway and thus could constitute a general phenomenon remains unknown. Here, we provide new evidence that such fluctuating activity is also evoked by multiple stimuli in visual areas responsible for processing visual motion (middle temporal visual area, MT), and faces (middle fundus and anterolateral face patches in inferotemporal cortex - areas MF and AL), thus extending the scope of circumstances in which fluctuating activity is observed. Furthermore, consistent with our previous results in the early visual area V1, MT exhibits fluctuations between the representations of two stimuli when these form distinguishable objects but not when they fuse into one perceived object, suggesting that fluctuating activity patterns may underlie visual object formation. Taken together, these findings point toward an updated model of how the brain preserves sensory information about multiple stimuli for subsequent processing and behavioral action.

Alice in wonderland syndrome – A medical enigma

Alice in Wonderland syndrome (AIWS) is a disorder in which patients report distorted visual perception and an impaired sense of time. It was first described in 1955 by the British psychiatrist John Todd, who associated these symptoms with disorders such as migraine and epilepsy. Before Todd’s observations, many symptoms of this disorder had made appearances in literature describing hysteria. Similar symptoms were observed in soldiers with occipital wounds after World Wars I and II. Patients with AIWS experience a plethora of symptoms such as, “kinetopsia” – Stationary objects appear to be moving, “total microsomatognosia” – Body parts appear unusually smaller. “Depersonalization” – Feeling of strangeness in perceiving one’s body image is common in AIWS. Often, this is accompanied by “derealisation” – Patients start perceiving their surroundings and the people around them as unreal. However, the patient is aware that this state of consciousness is not normal- which is what differentiates it from the state of psychosis. Other symptoms include: Dyschronometria, dysmorphops, macropsia, micropsia, kinetopsia, prosopometamorphopsia, and plagiopsia. In migraine patients, the prevalence rate of these symptoms increases to about 15%. Most symptoms of AIWS are ascribed to neurons of the central nervous system and cell columns that respond to specific sensory inputs (Ex- like visionary inputs of cortical areas V1–V5). Area V4 of the extrastriata visual cortex, particularly responds to the colors, whereas area V5 responds to movement. V4 and V5 also respond to shape and depth, but the bilateral loss of function of V4 results in achromatopsia (the inability to visualize color), and bilateral loss of V5 results in akinetopsia (the inability to visualize motion). Anatomically speaking, a critical region for developing this syndrome is the junction of three major areas, i.e., temporooccipital, parieto-occipital, and temporo-parietal junctions, known as the temporoparietal-occipital carrefour, point where our visual and somatosensory perceptions of the world are integrated to develop an external and internal representation of ourselves. AIWS remains a poorly understood and usually misdiagnosed syndrome. It is mostly associated with Epstein–Barr virus infection in children. Most cases of AIWS are expected to have a full remission of symptoms; sometimes, this may occur spontaneously with/without proper treatment. However, in cases with an associated chronic disease, like migraines and epilepsy, symptoms tend to occur in accordance with the active phases of these diseases. The number of fascinating disorders that surround and confound the medical community has no limit. The journey into the human brain is about as daunting and baffling as Alice’s journey into Wonderland. Efforts to understand the functioning and physiology of this extraordinary organ will keep both doctors and scientists on their toes for many years to come.

Decreased coherence in the model of the dorsal visual pathway associated with Alzheimer’s disease

Decreased coherence in electroencephalogram (EEG) has been reported in Alzheimer’s disease (AD) experimentally, which could be considered as a typical electrophysiological characteristic in AD. This work aimed to investigate the effect of AD on coherence in the dorsal visual pathway by the technique of neurocomputation. Firstly, according to the hierarchical organization of the cerebral cortex and the information flows of the dorsal visual pathway, a more physiologically plausible neural mass model including cortical areas v1, v2, and v5 was established in the dorsal visual pathway. The three interconnected cortical areas were connected by ascending and descending projections. Next, the pathological condition of loss of long synaptic projections in AD was simulated by reducing the parameters of long synaptic projections in the model. Then, the loss of long synaptic projections on coherence among different visual cortex areas was explored by means of power spectral analysis and coherence function. The results demonstrate that the coherence between these interconnected cortical areas showed an obvious decline with the gradual decrease of long synaptic projections, i.e. decrease in descending projections from area v2 to v1 and v5 to v2 and ascending projection from area v2 to v5. Hopefully, the results of this study could provide theoretical guidance for understanding the dynamical mechanism of AD.

Dynamic attention signalling in V4: Relation to fast-spiking/non-fast-spiking cell class and population coupling.

The computational role of a neuron during attention depends on its firing properties, neurotransmitter expression and functional connectivity. Neurons in the visual cortical area V4 are reliably engaged by selective attention but exhibit diversity in the effect of attention on firing rates and correlated variability. It remains unclear what specific neuronal properties shape these attention effects. In this study, we quantitatively characterised the distribution of attention modulation of firing rates across populations of V4 neurons. Neurons exhibited a continuum of time-varying attention effects. At one end of the continuum, neurons' spontaneous firing rates were slightly depressed with attention (compared to when unattended), whereas their stimulus responses were enhanced with attention. The other end of the continuum showed the converse pattern: attention depressed stimulus responses but increased spontaneous activity. We tested whether the particular pattern of time-varying attention effects that a neuron exhibited was related to the shape of their actions potentials (so-called 'fast-spiking' [FS] neurons have been linked to inhibition) and the strength of their coupling to the overall population. We found an interdependence among neural attention effects, neuron type and population coupling. In particular, we found neurons for which attention enhanced spontaneous activity but suppressed stimulus responses were less likely to be fast-spiking (more likely to be non-fast-spiking) and tended to have stronger population coupling, compared to neurons with other types of attention effects. These results add important information to our understanding of visual attention circuits at the cellular level.

Suppressing feedback signals to visual cortex abolishes attentional modulation.

Attention improves perception by enhancing the neural encoding of sensory information. A long-standing hypothesis is that cortical feedback projections carry top-down signals to influence sensory coding. However, this hypothesis has never been tested to establish causal links. We used viral tools to label feedback connections from cortical area V4 targeting early visual cortex (area V1). While monkeys performed a visual-spatial attention task, inactivating feedback axonal terminals in V1 without altering local intracortical and feedforward inputs reduced the response gain of single cells and impaired the accuracy of neural populations for encoding external stimuli. These effects are primarily manifested in the superficial layers of V1 and propagate to downstream area V4. Attention enhances sensory coding across visual cortex by specifically altering the strength of corticocortical feedback in a layer-dependent manner.

Adaptive processing and perceptual learning in visual cortical areas V1 and V4

Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.

Dynamic task-belief is an integral part of decision-making

Natural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys' task-belief and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not in artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant, but not the believed-irrelevant, feature. Perception also impacts belief updating; noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.

Cortical circuits for top-down control of perceptual grouping

A fundamental characteristic of human visual perception is the ability to group together disparate elements in a scene and treat them as a single unit. The mechanisms by which humans create such groupings remain unknown, but grouping seems to play an important role in a wide variety of visual phenomena, and a good understanding of these mechanisms might provide guidance for how to improve machine vision algorithms. Here, we build on a proposal that some groupings are the result of connections in cortical area V2 that join disparate elements, thereby allowing them to be selected and segmented together. In previous instantiations of this proposal, connection formation was based on the anatomy (e.g., extent) of receptive fields, which made connection formation obligatory when the stimulus conditions stimulate the corresponding receptive fields. We now propose dynamic circuits that provide greater flexibility in the formation of connections and that allow for top-down control of perceptual grouping. With computer simulations we explain how the circuits work and show how they can account for a wide variety of Gestalt principles of perceptual grouping, texture segmentation tasks, amodal illusory contours, and ratings of perceived groupings. We propose that human observers use such top-down control to implement task-dependent connection strategies that encourage particular groupings of stimulus elements in order to promote performance on various visual tasks.

Laminar microcircuitry of visual cortex producing attention-associated electric fields

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.

Spatial and color hallucinations in a mathematical model of primary visual cortex

We study a simplified model of the representation of colors in the primate primary cortical visual area V1. The model is described by an initial value problem related to a Hammerstein equation. The solutions to this problem represent the variation of the activity of populations of neurons in V1 as a function of space and color. The two space variables describe the spatial extent of the cortex while the two color variables describe the hue and the saturation represented at every location in the cortex. We prove the well-posedness of the initial value problem. We focus on its stationary, i.e. independent of time, and periodic in space solutions. We show that the model equation is equivariant with respect to the direct product G of the group of the Euclidean transformations of the planar lattice determined by the spatial periodicity and the group of color transformations, isomorphic to O(2), and study the equivariant bifurcations of its stationary solutions when some parameters in the model vary. Their variations may be caused by the consumption of drugs and the bifurcated solutions may represent visual hallucinations in space and color. Some of the bifurcated solutions can be determined by applying the Equivariant Branching Lemma (EBL) by determining the axial subgroups of G . These define bifurcated solutions which are invariant under the action of the corresponding axial subgroup. We compute analytically these solutions and illustrate them as color images. Using advanced methods of numerical bifurcation analysis we then explore the persistence and stability of these solutions when varying some parameters in the model. We conjecture that we can rely on the EBL to predict the existence of patterns that survive in large parameter domains but not to predict their stability. On our way we discover the existence of spatially localized stable patterns through the phenomenon of "snaking".

Estimating null and potent modes of feedforward communication in a computational model of cortical activity

Communication across anatomical areas of the brain is key to both sensory and motor processes. Dimensionality reduction approaches have shown that the covariation of activity across cortical areas follows well-delimited patterns. Some of these patterns fall within the "potent space" of neural interactions and generate downstream responses; other patterns fall within the "null space" and prevent the feedforward propagation of synaptic inputs. Despite growing evidence for the role of null space activity in visual processing as well as preparatory motor control, a mechanistic understanding of its neural origins is lacking. Here, we developed a mean-rate model that allowed for the systematic control of feedforward propagation by potent and null modes of interaction. In this model, altering the number of null modes led to no systematic changes in firing rates, pairwise correlations, or mean synaptic strengths across areas, making it difficult to characterize feedforward communication with common measures of functional connectivity. A novel measure termed the null ratio captured the proportion of null modes relayed from one area to another. Applied to simultaneous recordings of primate cortical areas V1 and V2 during image viewing, the null ratio revealed that feedforward interactions have a broad null space that may reflect properties of visual stimuli.

Perceptual Texture Dimensions Modulate Neuronal Response Dynamics in Visual Cortical Area V4.

Texture is an important visual attribute for surface pattern discrimination and therefore object segmentation, but the neural bases of texture perception are largely unknown. Previously, we demonstrated that the responses of V4 neurons to naturalistic texture patches are sensitive to four key features of human texture perception: coarseness, directionality, regularity, and contrast. To begin to understand how distinct texture perception emerges from the dynamics of neuronal responses, in 2 macaque monkeys (1 male, 1 female), we investigated the relative contribution of the four texture attributes to V4 responses in terms of the strength and timing of response modulation. We found that the different feature dimensions are associated with different temporal dynamics. Specifically, the response modulation associated with directionality and regularity was significantly delayed relative to that associated with coarseness and contrast, suggesting that the latter are fundamentally simpler feature dimensions. The population of texture-selective neurons could be grouped into multiple clusters based on the combination of feature dimensions encoded, and those subpopulations displayed distinct temporal dynamics characterized by the weighted combinations of multiple features. Finally, we applied a population decoding approach to demonstrate that texture category information can be obtained from short temporal windows across time. These results demonstrate that the representation of different perceptually relevant texture features emerge over time in the responses of V4 neurons. The observed temporal organization provides a framework to interpret how the processing of surface features unfolds in early and midlevel cortical stages, and could ultimately inform the interpretation of perceptual texture dynamics.SIGNIFICANCE STATEMENT To delineate how neuronal responses underlie our ability to perceive visual textures, we related four key perceptual dimensions (coarseness, directionality, regularity, and contrast) of naturalistic textures to the strength and timing of modulation of neuronal responses in area V4, an intermediate stage in the form-processing, ventral visual pathway. Our results provide the first characterization of V4 temporal dynamics for texture encoding along perceptually defined axes.

High-order interactions explain the collective behavior of cortical populations in executive but not sensory areas

One influential view in neuroscience is that pairwise cell interactions explain the firing patterns of large populations. Despite its prevalence, this view originates from studies in the retina and visual cortex of anesthetized animals. Whether pairwise interactions predict the firing patterns of neurons across multiple brain areas in behaving animals remains unknown. Here, we performed multi-area electrical recordings to find that 2nd-order interactions explain a high fraction of entropy of the population response in macaque cortical areas V1 and V4. Surprisingly, despite the brain-state modulation of neuronal responses, the model based on pairwise interactions captured ∼90% of the spiking activity structure during wakefulness and sleep. However, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from the prefrontal cortex. Thus, while simple pairwise interactions explain the collective behavior of visual cortical networks across brain states, explaining the population dynamics in downstream areas involves higher-order interactions.

A Stable Population Code for Attention in Prefrontal Cortex Leads a Dynamic Attention Code in Visual Cortex.

Attention often requires maintaining a stable mental state over time while simultaneously improving perceptual sensitivity. These requirements place conflicting demands on neural populations, as sensitivity implies a robust response to perturbation by incoming stimuli, which is antithetical to stability. Functional specialization of cortical areas provides one potential mechanism to resolve this conflict. We reasoned that attention signals in executive control areas might be highly stable over time, reflecting maintenance of the cognitive state, thereby freeing up sensory areas to be more sensitive to sensory input (i.e., unstable), which would be reflected by more dynamic attention signals in those areas. To test these predictions, we simultaneously recorded neural populations in prefrontal cortex (PFC) and visual cortical area V4 in rhesus macaque monkeys performing an endogenous spatial selective attention task. Using a decoding approach, we found that the neural code for attention states in PFC was substantially more stable over time compared with the attention code in V4 on a moment-by-moment basis, in line with our guiding thesis. Moreover, attention signals in PFC predicted the future attention state of V4 better than vice versa, consistent with a top-down role for PFC in attention. These results suggest a functional specialization of attention mechanisms across cortical areas with a division of labor. PFC signals the cognitive state and maintains this state stably over time, whereas V4 responds to sensory input in a manner dynamically modulated by that cognitive state.SIGNIFICANCE STATEMENT Attention requires maintaining a stable mental state while simultaneously improving perceptual sensitivity. We hypothesized that these two demands (stability and sensitivity) are distributed between prefrontal and visual cortical areas, respectively. Specifically, we predicted attention signals in visual cortex would be less stable than in prefrontal cortex, and furthermore prefrontal cortical signals would predict attention signals in visual cortex in line with the hypothesized role of prefrontal cortex in top-down executive control. Our results are consistent with suggestions deriving from previous work using separate recordings in the two brain areas in different animals performing different tasks and represent the first direct evidence in support of this hypothesis with simultaneous multiarea recordings within individual animals.

Clustered functional domains for curves and corners in cortical area V4.

The ventral visual pathway is crucially involved in integrating low-level visual features into complex representations for objects and scenes. At an intermediate stage of the ventral visual pathway, V4 plays a crucial role in supporting this transformation. Many V4 neurons are selective for shape segments like curves and corners; however, it remains unclear whether these neurons are organized into clustered functional domains, a structural motif common across other visual cortices. Using two-photon calcium imaging in awake macaques, we confirmed and localized cortical domains selective for curves or corners in V4. Single-cell resolution imaging confirmed that curve- or corner-selective neurons were spatially clustered into such domains. When tested with hexagonal-segment stimuli, we find that stimulus smoothness is the cardinal difference between curve and corner selectivity in V4. Combining cortical population responses with single-neuron analysis, our results reveal that curves and corners are encoded by neurons clustered into functional domains in V4. This functionally specific population architecture bridges the gap between the early and late cortices of the ventral pathway and may serve to facilitate complex object recognition.

Multimodal Neuroimaging Study of Visual Plasticity in Schizophrenia.

Schizophrenia is a severe mental illness with visual learning and memory deficits, and reduced long term potentiation (LTP) may underlie these impairments. Recent human fMRI and EEG studies have assessed visual plasticity that was induced with high frequency visual stimulation, which is thought to mimic an LTP-like phenomenon. This study investigated the differences in visual plasticity in participants with schizophrenia and healthy controls. An fMRI visual plasticity paradigm was implemented, and proton magnetic resonance spectroscopy data were acquired to determine whether baseline resting levels of glutamatergic and GABA metabolites were related to visual plasticity response. Adults with schizophrenia did not demonstrate visual plasticity after family-wise error correction; whereas, the healthy control group did. There was a significant regional difference in visual plasticity in the left visual cortical area V2 when assessing group differences, and baseline GABA levels were associated with this specific ROI in the SZ group only. Overall, this study suggests that visual plasticity is altered in schizophrenia and related to basal GABA levels.

FP-Nets for Blind Image Quality Assessment

Feature-Product networks (FP-nets) are a novel deep-network architecture inspired by principles of biological vision. These networks contain the so-called FP-blocks that learn two different filters for each input feature map, the outputs of which are then multiplied. Such an architecture is inspired by models of end-stopped neurons, which are common in cortical areas V1 and especially in V2. The authors here use FP-nets on three image quality assessment (IQA) benchmarks for blind IQA. They show that by using FP-nets, they can obtain networks that deliver state-of-the-art performance while being significantly more compact than competing models. A further improvement that they obtain is due to a simple attention mechanism. The good results that they report may be related to the fact that they employ bio-inspired design principles.