Lateral connectivity and contextual interactions in macaque primary visual cortex

Spatial context non-uniformly modulates inter-laminar information flow in the primary visual cortex

Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet, how the spatial organization of objects influences this activity remains poorly understood. We investigate how inter-laminar information flow within the primary visual cortex (V1) is affected by visual stimuli in isolation or with flankers at spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous, layer-specific population recordings, we establish that information propagation between cortical layers occurs along a structurally stable communication subspace. The spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance of feedforward and effective feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences information flow in the sensory hierarchy.

Orientation-dependent contextual modulation of contrast in schizophrenia

INTRODUCTION

Schizophrenia is associated with weakened contextual modulation of visual contrast perception, which is generally predicted by population average neural firing rates in primary visual cortex (V1). We use high field fMRI and a novel task to assess V1-instrinsic and V1-extrinsic mechanisms of atypical contextual modulation in schizophrenia.

METHODS

We examined the BOLD responses of individuals with schizophrenia (SCZ = 34), bipolar disorder (BP = 25), unaffected first-degree relatives of SCZ (SREL = 20), unaffected first-degree relatives of BP (BPREL = 13) and healthy controls (CON = 23). Participants were presented with near- and far-surrounds oriented at 20° and 70° relative to center gratings.

RESULTS

We observed orientation-dependent modulation of V1 BOLD activation to near-surrounds across groups. In particular, the SCZ and CON groups showed significant orientation-dependent contextual modulation (Cohen's dz SCZ = 0.56; CON = 0.63). Surprisingly, the direction of the modulation was opposite of predicted: greater BOLD activation for the condition that was expected to produce suppression.

CONCLUSIONS

Our results differ from previous reports: we observed successful orientation-dependent modulation of V1 activation in SCZ. Furthermore, our results suggest that spatial attention and figure-ground modulation may play an important role in determining the direction and magnitude of orientation-dependent modulation.

What Factors Affect Binocular Summation?

Binocular vision may serve as a good model for research on awareness. Binocular summation (BS) can be defined as the superiority of binocular over monocular visual performance. Early studies of BS found an improvement of a factor of about 1.4 (empirically), leading to models suggesting a quadratic summation of the two monocular inputs (√2). Neural interaction modulates a target's visibility within the same eye or between eyes (facilitation or suppression). Recent results indicated that at a closely flanked stimulus, BS is characterized by instability; it relies on the specific order in which the stimulus condition is displayed. Otherwise, BS is stable. These results were revealed in experiments where the tested eye was open, whereas the other eye was occluded (mono-optic glasses, blocked presentation); thus, the participants were aware of the tested eye. Therefore, in this study, we repeated the same experiments but utilized stereoscopic glasses (intermixed at random presentation) to control the monocular and binocular vision, thus potentially eliminating awareness of the tested condition. The stimuli consisted of a central vertically oriented Gabor target and high-contrast Gabor flankers positioned in two configurations (orthogonal or collinear) with target-flanker separations of either two or three wavelengths (λ), presented at four different presentation times (40, 80, 120, and 200 ms). The results indicate that when utilizing stereoscopic glasses and mixing the testing conditions, the BS is normal, raising the possibility that awareness may be involved.

Dynamics of the perceptive field size in human adults

The receptive field (RF) is the fundamental processing unit of human vision; both masking and crowding depend on its size. The RF has a psychophysical corresponding term, the perceptive field (PF); whereas the RF is measured physiologically, the PF is measured psychophysically (a perceptual response). We investigated how spatial (lateral interactions), temporal (the stimulus presentation time), and the procedure affect the PF size for both monocular and binocular viewing. The stimuli consisted of a central vertically oriented Gabor target and high-contrast Gabor flankers positioned in two configurations (orthogonal or collinear) with target-flanker separations of either 2 or 3 wavelengths (λ). We used two main methods to control the monocular and binocular vision: mono-optic glasses vs. stereo glasses. The presentation order was either mixed or non-mixed for the presentation time and the eye condition. We estimated the PF size for both monocular and binocular viewing at 4 different presentation times (40, 80,120, and 200 ms) with different orders of presentation in each experiment (mono-optic glasses vs. stereo glasses, utilizing the lateral masking paradigm). In each experiment we explored one variable: how changing one parameter would affect the PF size in both monocular and binocular viewing (the temporal duration, the testing order of conditions, and the spatial distance) while keeping the others constant. We found that both the monocular and binocular PF size were dynamic and were significantly affected by the presentation order, leading to reduced lateral suppression under the collinear 2λ condition. Hence, both the monocular and binocular PF size depended on the sequence of the stimulus presentation time and the testing order of the conditions. Furthermore, we found that the binocular PF size was significantly larger than the monocular PF size.

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. Using Neuropixels probes, we measured border ownership modulation within large, local populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that cross-correlations between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, independent of the comparison with CRF stimulation, we observed that the magnitude of border ownership modulation increased with the proportion of information flow from feedback/horizontal layers to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

Transformers and cortical waves: encoders for pulling in context across time

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.

Correcting visual acuity beyond 20/20 improves contour element detection and integration: A cautionary tale for studies of special populations

Contrary to popular lore, optimal visual acuity is typically better than 20/20. Could correcting acuity beyond 20/20 offer any benefit? An affirmative answer could present new confounds in studies of aging, development, psychiatric illness, neurodegenerative disorders, or any other population where refractive error might be more likely. An affirmative answer would also offer a novel explanation of inter-observer variability in visual performance. To address the question, we had individuals perform two well-studied visual tasks, once with 20/20 vision and once with optical correction, so that observers could see one line better on an eye chart. In the contour integration task, observers sought to identify the screen quadrant location of a sparsely defined (integrated) shape embedded in varying quantities of randomly oriented "noise" elements. In the collinear facilitation task, observers sought to detect a low-contrast element flanked by collinear or orthogonal high-contrast elements. In each case, displays were scaled in size to modulate element visibility and spatial frequency (4-12 cycles/deg). We found that improving acuity beyond 20/20 improved contour integration for the high spatial frequency displays. Although improving visual acuity did not affect collinear facilitation, it did improve detection of the central low-contrast target, especially at high spatial frequencies. These results, which were large in magnitude, suggest that optically correcting beyond 20/20 improves the detection and integration of contour elements, especially those that are smaller and of higher spatial frequency. Refractive blur within the normal range may confound special population studies, explain inter-observer differences, and meaningfully impact performance in low-visibility environments.

Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates

A key feature of neurons in the primary visual cortex (V1) of primates is their orientation selectivity. Recent studies using deep neural network models showed that the most exciting input (MEI) for mouse V1 neurons exhibit complex spatial structures that predict non-uniform orientation selectivity across the receptive field (RF), in contrast to the classical Gabor filter model. Using local patches of drifting gratings, we identified heterogeneous orientation tuning in mouse V1 that varied up to 90° across sub-regions of the RF. This heterogeneity correlated with deviations from optimal Gabor filters and was consistent across cortical layers and recording modalities (calcium vs. spikes). In contrast, model-synthesized MEIs for macaque V1 neurons were predominantly Gabor like, consistent with previous studies. These findings suggest that complex spatial feature selectivity emerges earlier in the visual pathway in mice than in primates. This may provide a faster, though less general, method of extracting task-relevant information.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

Lateral inhibition in V1 controls neural & perceptual contrast sensitivity

Lateral inhibition is a central principle for sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. Much work on the role of inhibition in sensory systems has focused on visual cortex; however, the neurons, computations, and mechanisms underlying cortical lateral inhibition remain debated, and its importance for visual perception remains unknown. Here, we tested how lateral inhibition from PV or SST neurons in mouse primary visual cortex (V1) modulates neural and perceptual sensitivity to stimulus contrast. Lateral inhibition from PV neurons reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from SST neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model identified spatially extensive lateral projections from SST neurons as the key factor, and we confirmed this with anatomy and direct subthreshold measurements of a larger spatial footprint for SST versus PV lateral inhibition. Together, these results define cell-type specific computational roles for lateral inhibition in V1, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

Attention spotlight in V1-based cortico-cortical interactions in human visual hierarchy

Attention is often viewed as a mental spotlight, which can be scaled like a zoom lens at specific spatial locations and features a center-surround gradient. Here, we demonstrate a neural signature of attention spotlight in signal transmission along the visual hierarchy. fMRI background connectivity analysis was performed between retinotopic V1 and downstream areas to characterize the spatial distribution of inter-areal interaction under two attentional states. We found that, compared to diffused attention, focal attention sharpened the spatial gradient in the strength of the background connectivity. Dynamic causal modeling analysis further revealed the effect of attention in both the feedback and feedforward connectivity between V1 and extrastriate cortex. In a context which induced a strong effect of crowding, the effect of attention in the background connectivity profile diminished. Our findings reveal a context-dependent attention prioritization in information transmission via modulating the recurrent processing across the early stages in human visual cortex.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Nonuniform and pathway-specific laminar processing of spatial frequencies in the primary visual cortex of primates

The neocortex comprises six cortical layers that play a crucial role in information processing; however, it remains unclear whether laminar processing is consistent across all regions within a single cortex. In this study, we demonstrate diverse laminar response patterns in the primary visual cortex (V1) of three male macaque monkeys when exposed to visual stimuli at different spatial frequencies (SFs). These response patterns can be categorized into two groups. One group exhibit suppressed responses in the output layers for all SFs, while the other type shows amplified responses specifically at high SFs. Further analysis suggests that both magnocellular (M) and parvocellular (P) pathways contribute to the suppressive effect through feedforward mechanisms, whereas amplification is specific to local recurrent mechanisms within the parvocellular pathway. These findings highlight the non-uniform distribution of neural mechanisms involved in laminar processing and emphasize how pathway-specific amplification selectively enhances representations of high-SF information in primate V1.

Modular horizontal network within mouse primary visual cortex

Interactions between feedback connections from higher cortical areas and local horizontal connections within primary visual cortex (V1) were shown to play a role in contextual processing in different behavioral states. Layer 1 (L1) is an important part of the underlying network. This cell-sparse layer is a target of feedback and local inputs, and nexus for contacts onto apical dendrites of projection neurons in the layers below. Importantly, L1 is a site for coupling inputs from the outside world with internal information. To determine whether all of these circuit elements overlap in L1, we labeled the horizontal network within mouse V1 with anterograde and retrograde viral tracers. We found two types of local horizontal connections: short ones that were tangentially limited to the representation of the point image, and long ones which reached beyond the receptive field center, deep into its surround. The long connections were patchy and terminated preferentially in M2 muscarinic acetylcholine receptor-negative (M2-) interpatches. Anterogradely labeled inputs overlapped in M2-interpatches with apical dendrites of retrogradely labeled L2/3 and L5 cells, forming module-selective loops between topographically distant locations. Previous work showed that L1 of M2-interpatches receive inputs from the lateral posterior thalamic nucleus (LP) and from a feedback network from areas of the medial dorsal stream, including the secondary motor cortex. Together, these findings suggest that interactions in M2-interpatches play a role in processing visual inputs produced by object-and self-motion.

Encoding luminance surfaces in the visual cortex of mice and monkeys: difference in responses to edge and center

Luminance and spatial contrast provide information on the surfaces and edges of objects. We investigated neural responses to black and white surfaces in the primary visual cortex (V1) of mice and monkeys. Unlike primates that use their fovea to inspect objects with high acuity, mice lack a fovea and have low visual acuity. It thus remains unclear whether monkeys and mice share similar neural mechanisms to process surfaces. The animals were presented with white or black surfaces and the population responses were measured at high spatial and temporal resolution using voltage-sensitive dye imaging. In mice, the population response to the surface was not edge-dominated with a tendency to center-dominance, whereas in monkeys the response was edge-dominated with a "hole" in the center of the surface. The population response to the surfaces in both species exhibited suppression relative to a grating stimulus. These results reveal the differences in spatial patterns to luminance surfaces in the V1 of mice and monkeys and provide evidence for a shared suppression process relative to grating.

Spike synchrony as a measure of Gestalt structure

The function of spike synchrony is debatable: some researchers view it as a mechanism for binding perceptual features, others - as a byproduct of brain activity. We argue for an alternative computational role: synchrony can estimate the prior probability of incoming stimuli. In V1, this can be achieved by comparing input with previously acquired visual experience, which is encoded in plastic horizontal intracortical connections. V1 connectivity structure can encode the acquired visual experience in the form of its aggregate statistics. Since the aggregate statistics of natural images tend to follow the Gestalt principles, we can assume that V1 is more often exposed to Gestalt-like stimuli, and this is manifested in its connectivity structure. At the same time, the connectivity structure has an impact on spike synchrony in V1. We used a spiking model with V1-like connectivity to demonstrate that spike synchrony reflects the Gestalt structure of the stimulus. We conducted simulation experiments with three Gestalt laws: proximity, similarity, and continuity, and found substantial differences in firing synchrony for stimuli with varying degrees of Gestalt-likeness. This allows us to conclude that spike synchrony indeed reflects the Gestalt structure of the stimulus, which can be interpreted as a mechanism for prior probability estimation.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

Local field potentials, spiking activity, and receptive fields in human visual cortex

The concept of receptive field (RF) is central to sensory neuroscience. Neuronal RF properties have been substantially studied in animals, while those in humans remain nearly unexplored. Here, we measured neuronal RFs with intracranial local field potentials (LFPs) and spiking activity in human visual cortex (V1/V2/V3). We recorded LFPs via macro-contacts and discovered that RF sizes estimated from low-frequency activity (LFA, 0.5-30 Hz) were larger than those estimated from low-gamma activity (LGA, 30-60 Hz) and high-gamma activity (HGA, 60-150 Hz). We then took a rare opportunity to record LFPs and spiking activity via microwires in V1 simultaneously. We found that RF sizes and temporal profiles measured from LGA and HGA closely matched those from spiking activity. In sum, this study reveals that spiking activity of neurons in human visual cortex could be well approximated by LGA and HGA in RF estimation and temporal profile measurement, implying the pivotal functions of LGA and HGA in early visual information processing.

A CODE model bridging crowding in sparse and dense displays

Visual crowding is arguably the strongest limitation imposed on extrafoveal vision, and is a relatively well-understood phenomenon. However, most investigations and theories are based on sparse displays consisting of a target and at most a handful of flanker objects. Recent findings suggest that the laws thought to govern crowding may not hold for densely cluttered displays, and that grouping and nearest neighbour effects may be more important. Here we present a computational model that accounts for crowding effects in both sparse and dense displays. The model is an adaptation and extension of an earlier model that has previously successfully accounted for spatial clustering, numerosity and object-based attention phenomena. Our model combines grouping by proximity and similarity with a nearest neighbour rule, and defines crowding as the extent to which target and flankers fail to segment. We show that when the model is optimized for explaining crowding phenomena in classic, sparse displays, it also does a good job in capturing novel crowding patterns in dense displays, in both existing and new data sets. The model thus ties together different principles governing crowding, specifically Bouma's law, grouping, and nearest neighbour similarity effects.

Modeling the Role of Contour Integration in Visual Inference

Under difficult viewing conditions, the brain's visual system uses a variety of recurrent modulatory mechanisms to augment feedforward processing. One resulting phenomenon is contour integration, which occurs in the primary visual (V1) cortex and strengthens neural responses to edges if they belong to a larger smooth contour. Computational models have contributed to an understanding of the circuit mechanisms of contour integration, but less is known about its role in visual perception. To address this gap, we embedded a biologically grounded model of contour integration in a task-driven artificial neural network and trained it using a gradient-descent variant. We used this model to explore how brain-like contour integration may be optimized for high-level visual objectives as well as its potential roles in perception. When the model was trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, it closely mirrored the brain in terms of behavior, neural responses, and lateral connection patterns. When trained on natural images, the model enhanced weaker contours and distinguished whether two points lay on the same versus different contours. The model learned robust features that generalized well to out-of-training-distribution stimuli. Surprisingly, and in contrast with the synthetic task, a parameter-matched control network without recurrence performed the same as or better than the model on the natural-image tasks. Thus, a contour integration mechanism is not essential to perform these more naturalistic contour-related tasks. Finally, the best performance in all tasks was achieved by a modified contour integration model that did not distinguish between excitatory and inhibitory neurons.

There is a fundamental, unbridgeable gap between DNNs and the visual cortex

Deep neural networks (DNNs) are not just inadequate models of the visual system but are so different in their structure and functionality that they are not even on the same playing field. DNN units have almost nothing in common with neurons, and, unlike visual neurons, they are often fully connected. At best, DNNs can label inputs, while our object perception is both holistic and detail preserving. A feat that no computational system can achieve.

Space, time, and dynamics of binocular interactions

Binocular summation (BS), defined as the superiority of binocular over monocular visual performance, shows that thresholds are about 40% (a factor of 1.4) better in binocular than in monocular viewing. However, it was reported that different amounts of BS exist in a range from 1.4 to 2 values because BS is affected by the spatiotemporal parameters of the stimulus. Lateral interactions can be defined as the neuron's ability to affect the neighboring neurons by either inhibiting or exciting their activity. We investigated the effect of the spatial and temporal domains on binocular interactions and BS under the lateral masking paradigm and how BS would be affected by lateral interactions via a lateral masking experiment. The two temporal alternative forced-choice (2TAFC) method was used. The stimuli consisted of a central vertically oriented Gabor target and high-contrast Gabor flankers positioned in two configurations (orthogonal or collinear) with target-flanker separations of either 2 or 3 wavelengths (λ), presented at 4 different presentation times (40, 80, 120, and 200 ms) using a different order of measurements across the different experiments. Opaque lenses were used to control the monocular and binocular vision. BS is absent at close distances (2λ), depending on the presentation time's order, for the collinear but not for the orthogonal configuration. However, BS exists at more distant flankers (collinear and orthogonal, 3λ). BS is not uniform (1.4); it depends on the stimulus condition, the presentation times, the order, and the method that was used to control the monocular and binocular vision.

An interactive cortical architecture for perceptual organization by accentuation

Accentuation has been proposed as a general principle of perceptual organization. Here, we have developed a neurodynamic architecture to explain how accentuation affects boundary segmentation and shape perception. The model consists of bottom-up and top-down pathways. Bottom-up processing involves a set of feature maps that compute bottom-up salience of surfaces, boundaries, boundary completions, and junctions. Then, a feature-based winner-take-all network selects the most salient locations. Top-down processing includes an object-based attention stage that allows enhanced neural activity to propagate from the most salient locations to all connected locations, and a visual segmentation stage that employs inhibitory connections to segregate boundaries into distinct maps. The model was tested on a series of computer simulations showing how the position of the accent affects boundary segregation in the square-diamond and the pointing illusion. The model was also tested on a variety of texture segregation tasks, showing that its performance was comparable to that of human observers. The model suggests that there is an intermediate stage of visual processing between perceptual grouping and object recognition that helps the visual system choose between competing percepts of the ambiguous stimulus.

Species-specific wiring of cortical circuits for small-world networks in the primary visual cortex

Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key components to organize a "small-world network" optimized for each size of the visual cortex, enabling the cost-efficient integration of visual information. Using computational simulations of a biologically inspired model neural network, we found that sparse LRCs added to networks, combined with dense local connections, compose a small-world network and significantly enhance image classification performance. We confirmed that the performance of the network appeared to be strongly correlated with the small-world coefficient of the model network under various conditions. Our theoretical model demonstrates that the amount of LRCs to build a small-world network depends on each size of cortex and that LRCs are beneficial only when the size of the network exceeds a certain threshold. Our model simulation of various sizes of cortices validates this prediction and provides an explanation of the species-specific existence of LRCs in animal data. Our results provide insight into a biological strategy of the brain to balance functional performance and resource cost.

Waves traveling over a map of visual space can ignite short-term predictions of sensory input

Recent analyses have found waves of neural activity traveling across entire visual cortical areas in awake animals. These traveling waves modulate the excitability of local networks and perceptual sensitivity. The general computational role of these spatiotemporal patterns in the visual system, however, remains unclear. Here, we hypothesize that traveling waves endow the visual system with the capacity to predict complex and naturalistic inputs. We present a network model whose connections can be rapidly and efficiently trained to predict individual natural movies. After training, a few input frames from a movie trigger complex wave patterns that drive accurate predictions many frames into the future solely from the network's connections. When the recurrent connections that drive waves are randomly shuffled, both traveling waves and the ability to predict are eliminated. These results suggest traveling waves may play an essential computational role in the visual system by embedding continuous spatiotemporal structures over spatial maps.

Stimulation-mediated reverse engineering of silent neural networks

Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of simulated silent neuronal networks using stimulation combined with a supervised learning algorithm, which enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell levels with high accuracy. We apply our method on rat cortical recordings fed through a circuit of heterogeneously connected leaky integrate-and-fire neurons firing at typical lognormal distributions and demonstrate improved performance during stimulation for multiple subpopulations. These testable predictions about the number and protocol of the required stimulations are expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.NEW & NOTEWORTHY We introduce a new concept for reverse engineering silent neuronal networks using a supervised learning algorithm combined with stimulation. We quantify the performance of the algorithm and the precision of deriving synaptic weights in inhibitory and excitatory subpopulations. We then show that stimulation enables deciphering connectivity of heterogeneous circuits fed with real electrode array recordings, which could extend in the future to deciphering connectivity in broad biological and artificial neural networks.

Mesoscale functional connectivity in macaque visual areas

Studies of resting-state functional connectivity (rsFC) have provided rich insights into the structures and functions of the human brain. However, most rsFC studies have focused on large-scale brain connectivity. To explore rsFC at a finer scale, we used intrinsic signal optical imaging to image the ongoing activity of the anesthetized macaque visual cortex. Differential signals from functional domains were used to quantify network-specific fluctuations. In 30-60 min resting-state imaging, a series of coherent activation patterns were observed in all three visual areas we examined (V1, V2, and V4). These patterns matched the known functional maps (ocular dominance, orientation, color) obtained in visual stimulation conditions. These functional connectivity (FC) networks fluctuated independently over time and exhibited similar temporal characteristics. Coherent fluctuations, however, were observed from orientation FC networks in different areas and even across two hemispheres. Thus, FC in the macaque visual cortex was fully mapped both on a fine scale and over a long range. Hemodynamic signals can be used to explore mesoscale rsFC in a submillimeter resolution.

Extended perceptive field revealed in humans with binocular fusion disorders

Binocular vision disorders or dysfunctions have considerable impact on daily visual activities such as reading. Heterophoria (phoria) is a latent eye misalignment (with a prevalence of up to 35%) that appears in conditions that disrupt binocular vision and it may affect the quality of binocular fusion. Our recent study, which used lateral masking (LM), suggests that subjects with binocular fusion disorders (horizontal phoria) exhibit an asymmetry and an abnormal pattern of both binocular and monocular lateral interactions, but only for the horizontal meridian (HM). The perceptive field (PF) is the fundamental processing unit of human vision and both masking and crowding depend on its size. An increased PF size is found in amblyopic populations or in young children. We hypothesized that the PF's size would be asymmetric only for the phoric group (larger along the HM). We estimated the PF's size using two different methods (LM with equal-phase and opposite-phase flankers). Phoric subjects exhibited a larger binocular PF size, only for the HM, confirming our hypothesis of an asymmetric PF size. However, the monocular PF size of phoric and control subjects was similar. Phoria affects the PF's size similarly to meridional amblyopia but without being attributed to abnormal refraction. We suggest that these findings could help explain the inter-observer variability found in the masking literature and the reading difficulties often encountered in subjects with high heterophoria. Since perceptual learning can reduce the PF's size, further investigation of training may provide a novel therapy to reduce some symptoms related to heterophoria.

Tilt aftereffect spreads across the visual field

The tilt aftereffect (TAE) is observed when adaptation to a tilted contour alters the perceived tilt of a subsequently presented contour. Thus far, TAE has been treated as a local aftereffect observed only at the location of the adapter. Whether and how TAE spreads to other locations in the visual field has not been systematically studied. Here, we sought an answer to this question by measuring TAE magnitudes at locations including but not limited to the adapter location. The adapter was a tilted grating presented at the same peripheral location throughout an experimental session. In a single trial, participants indicated the perceived tilt of a test grating presented after the adapter at one of fifteen locations in the same visual hemifield as the adapter. We found non-zero TAE magnitudes in all locations tested, showing that the effect spreads across the tested visual hemifield. Next, to establish a link between neuronal activity and behavioral results and to predict the possible neuronal origins of the spread, we built a computational model based on known characteristics of the visual cortex. The simulation results showed that the model could successfully capture the pattern of the behavioral results. Furthermore, the pattern of the optimized receptive field sizes suggests that mid-level visual areas, such as V4, could be critically involved in TAE and its spread across the visual field.

Attention along the cortical hierarchy: Development matters

We build on the existing biased competition view to argue that attention is an emergent property of neural computations within and across hierarchically embedded and structurally connected cortical pathways. Critically then, one must ask, what is attention emergent from? Within this framework, developmental changes in the quality of sensory input and feedforward-feedback information flow shape the emergence and efficiency of attention. Several gradients of developing structural and functional cortical architecture across the caudal-to-rostral axis provide the substrate for attention to emerge. Neural activity within visual areas depends on neuronal density, receptive field size, tuning properties of neurons, and the location of and competition between features and objects in the visual field. These visual cortical properties highlight the information processing bottleneck attention needs to resolve. Recurrent feedforward and feedback connections convey sensory information through a series of steps at each level of the cortical hierarchy, integrating sensory information across the entire extent of the cortical hierarchy and linking sensory processing to higher-order brain regions. Higher-order regions concurrently provide input conveying behavioral context and goals. Thus, attention reflects the output of a series of complex biased competition neural computations that occur within and across hierarchically embedded cortical regions. Cortical development proceeds along the caudal-to-rostral axis, mirroring the flow in sensory information from caudal to rostral regions, and visual processing continues to develop into childhood. Examining both typical and atypical development will offer critical mechanistic insight not otherwise available in the adult stable state. This article is categorized under: Psychology > Attention.

New insights into binocular rivalry from the reconstruction of evolving percepts using model network dynamics

When the two eyes are presented with highly distinct stimuli, the resulting visual percept generally switches every few seconds between the two monocular images in an irregular fashion, giving rise to a phenomenon known as binocular rivalry. While a host of theoretical studies have explored potential mechanisms for binocular rivalry in the context of evoked model dynamics in response to simple stimuli, here we investigate binocular rivalry directly through complex stimulus reconstructions based on the activity of a two-layer neuronal network model with competing downstream pools driven by disparate monocular stimuli composed of image pixels. To estimate the dynamic percept, we derive a linear input-output mapping rooted in the non-linear network dynamics and iteratively apply compressive sensing techniques for signal recovery. Utilizing a dominance metric, we are able to identify when percept alternations occur and use data collected during each dominance period to generate a sequence of percept reconstructions. We show that despite the approximate nature of the input-output mapping and the significant reduction in neurons downstream relative to stimulus pixels, the dominant monocular image is well-encoded in the network dynamics and improvements are garnered when realistic spatial receptive field structure is incorporated into the feedforward connectivity. Our model demonstrates gamma-distributed dominance durations and well obeys Levelt's four laws for how dominance durations change with stimulus strength, agreeing with key recurring experimental observations often used to benchmark rivalry models. In light of evidence that individuals with autism exhibit relatively slow percept switching in binocular rivalry, we corroborate the ubiquitous hypothesis that autism manifests from reduced inhibition in the brain by systematically probing our model alternation rate across choices of inhibition strength. We exhibit sufficient conditions for producing binocular rivalry in the context of natural scene stimuli, opening a clearer window into the dynamic brain computations that vary with the generated percept and a potential path toward further understanding neurological disorders.

Spatiotemporal patterns of population response in the visual cortex under isoflurane: from wakefulness to loss of consciousness

Anesthetic drugs are widely used in medicine and research to mediate loss of consciousness (LOC). Isoflurane is a commonly used anesthetic drug; however, its effects on cortical sensory processing, in particular around LOC, are not well understood. Using voltage-sensitive dye imaging, we measured visually evoked neuronal population response from the visual cortex in awake and anesthetized mice at 3 increasing concentrations of isoflurane, thus controlling the level of anesthesia from wakefulness to deep anesthesia. At low concentration of isoflurane, the effects on neuronal measures were minor relative to the awake condition. These effects augmented with increasing isoflurane concentration, while around LOC point, they showed abrupt and nonlinear changes. At the network level, we found that isoflurane decreased the stimulus-evoked intra-areal spatial spread of local neural activation, previously reported to be mediated by horizontal connections, and also reduced intra-areal synchronization of neuronal population. The synchronization between different visual areas decreased with higher isoflurane levels. Isoflurane reduced the population response amplitude and prolonged their latencies while higher visual areas showed increased vulnerability to isoflurane concentration. Our results uncover the changes in neural activity and synchronization at isoflurane concentrations leading to LOC and suggest reverse hierarchical shutdown of cortical areas.

Binocular fusion disorders impair basic visual processing

In an era of increasing screen consumption, the requirement for binocular vision is demanding, leading to the emergence of syndromes such as the computer vision syndrome (CVS) or visual discomfort reported by virtual reality (VR) users. Heterophoria (phoria) is a latent eye misalignment (with a prevalence up to 35%) that appears in conditions that disrupt binocular vision and may affect the quality of binocular fusion. Collinear facilitation (CF), the mechanism for grouping contour elements, is a process that reveals lateral interactions by improving the visibility of a target by flankers placed collinearly. An abnormal pattern of CF has been observed in strabismic amblyopia. We hypothesize that phoria may affect CF in the horizontal meridian (HM) due to latent eye misalignment and its impact on binocular fusion. Fully corrected participants (phoria group and controls) completed a standard CF experiment for horizontal and vertical meridians during binocular and monocular viewing. Phoric observers exhibited (1) an asymmetry and an abnormal pattern of CF only for the HM, during both monocular and binocular viewing, (2) poor binocular summation between the monocular inputs, and (3) no binocular advantage of the CF. Phoria affects the CF in a way that is reminiscent of meridional amblyopia without being attributed to abnormal refraction. The abnormal pattern of CF in monocular viewing suggests that phoria could be a binocular developmental disorder that affects monocular spatial interactions. We suggest that the results could contribute to explain the visual discomfort experienced with VR users or symptoms when presenting CVS.

The spatial properties of adaptation-induced distance compression

Exposure to a dynamic texture reduces the perceived separation between objects, altering the mapping between physical relations in the environment and their neural representations. Here we investigated the spatial tuning and spatial frame of reference of this aftereffect to understand the stage(s) of processing where adaptation-induced changes occur. In Experiment 1, we measured apparent separation at different positions relative to the adapted area, revealing a strong but tightly tuned compression effect. We next tested the spatial frame of reference of the effect, either by introducing a gaze shift between adaptation and test phase (Experiment 2) or by decoupling the spatial selectivity of adaptation in retinotopic and world-centered coordinates (Experiment 3). Results across the two experiments indicated that both retinotopic and world-centered adaptation effects can occur independently. Spatial attention to the location of the adaptor alone could not account for the world-centered transfer we observed, and retinotopic adaptation did not transfer to world-centered coordinates after a saccade (Experiment 4). Finally, we found that aftereffects in different reference frames have a similar, narrow spatial tuning profile (Experiment 5). Together, our results suggest that the neural representation of local separation resides early in the visual cortex, but it can also be modulated by activity in higher visual areas.

Cascaded normalizations for spatial integration in the primary visual cortex of primates

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.

Traces of semantization - from episodic to semantic memory in a spiking cortical network model

Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or" semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.Significance StatementRemembering single episodes is a fundamental attribute of cognition. Difficulties recollecting contextual information is a key sign of episodic memory loss or semantization. Behavioral studies demonstrate that semantization of episodic memory can occur rapidly, yet the neural mechanisms underlying this effect are insufficiently investigated. In line with recent behavioral findings, we show that multiple stimulus exposures in different contexts may advance item-context decoupling. We suggest a Bayesian-Hebbian synaptic plasticity hypothesis of memory semantization and further show that a transient modulation of plasticity during salient events may disrupt the decontextualization process by strengthening memory traces, and thus, enhancing preferential retention. The proposed cortical network-of-networks model thus bridges micro and mesoscale synaptic effects with network dynamics and behavior.

Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex

The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.

Towards a more general understanding of the algorithmic utility of recurrent connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such "tag propagation" algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

A multi-scale neurodynamic implementation of incremental grouping

Incremental grouping is a process entailing serial binding of distal image elements into a unified object representation. At the neural level, incremental grouping involves propagation of the enhanced firing rate among feature-tuned neurons in the early visual cortex. Here, we developed a multi-resolution neural model of incremental grouping. In the model, propagation of the enhanced firing rate is achieved by computing the activity difference between two sets of units: attentional or A-units, whose firing rate is modulated by their horizontal collaterals, and non-attentional or N-units that receive only feedforward input. The activity difference is computed on dendrites that act as independent computational subunits. The proposed model employs multiple spatial scales to account for a variable speed of incremental grouping. In addition, the model incorporates the L-junction detection network that enables incremental grouping over L-junctions. Computer simulations show that the timing of attentional modulations in the model is comparable with neurophysiological measurements in monkey primary visual cortex.

Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here, we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Results of this process depend on interaction between stimulus dimensions. Comparison of modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.

Revisiting horizontal connectivity rules in V1: from like-to-like towards like-to-all

Horizontal connections in the primary visual cortex of carnivores, ungulates and primates organize on a near-regular lattice. Given the similar length scale for the regularity found in cortical orientation maps, the currently accepted theoretical standpoint is that these maps are underpinned by a like-to-like connectivity rule: horizontal axons connect preferentially to neurons with similar preferred orientation. However, there is reason to doubt the rule's explanatory power, since a growing number of quantitative studies show that the like-to-like connectivity preference and bias mostly observed at short-range scale, are highly variable on a neuron-to-neuron level and depend on the origin of the presynaptic neuron. Despite the wide availability of published data, the accepted model of visual processing has never been revised. Here, we review three lines of independent evidence supporting a much-needed revision of the like-to-like connectivity rule, ranging from anatomy to population functional measures, computational models and to theoretical approaches. We advocate an alternative, distance-dependent connectivity rule that is consistent with new structural and functional evidence: from like-to-like bias at short horizontal distance to like-to-all at long horizontal distance. This generic rule accounts for the observed high heterogeneity in interactions between the orientation and retinotopic domains, that we argue is necessary to process non-trivial stimuli in a task-dependent manner.

Methods for inferring neural circuit interactions and neuromodulation from local field potential and electroencephalogram measures

Electrical recordings of neural mass activity, such as local field potentials (LFPs) and electroencephalograms (EEGs), have been instrumental in studying brain function. However, these aggregate signals lack cellular resolution and thus are not easy to be interpreted directly in terms of parameters of neural microcircuits. Developing tools for a reliable estimation of key neural parameters from these signals, such as the interaction between excitation and inhibition or the level of neuromodulation, is important for both neuroscientific and clinical applications. Over the years, we have developed tools based on neural network modeling and computational analysis of empirical data to estimate neural parameters from aggregate neural signals. This review article gives an overview of the main computational tools that we have developed and employed to invert LFPs and EEGs in terms of circuit-level neural phenomena, and outlines future challenges and directions for future research.

Cytochrome oxidase "blobs": a call for more anatomy

An ordered relation of structure and function has been a cornerstone in thinking about brain organization. Like the brain itself, however, this is not straightforward and is confounded both by functional intricacy and structural plasticity (many routes to a given outcome). As a striking case of putative structure-function correlation, this mini-review focuses on the relatively well-characterized pattern of cytochrome oxidase (CO) blobs (aka "patches" or "puffs") in the supragranular layers of macaque monkey visual cortex. The pattern is without doubt visually compelling, and the semi-dichotomous array of CO+ blobs and CO- interblobs is consistent with multiple studies reporting compartment-specific preferential connectivity and distinctive physiological response properties. Nevertheless, as briefly reviewed here, the finer anatomical organization of this system is surprisingly under-investigated, and the relation to functional aspects, therefore, unclear. Microcircuitry, cell type, and three-dimensional spatiotemporal level investigations of the CO+ CO- pattern are needed and may open new views to structure-function organization of visual cortex, and to phylogenetic and ontogenetic comparisons across nonhuman primates (NHP), and between NHP and humans.

Modulation of Spike Count Correlations Between Macaque Primary Visual Cortex Neurons by Difficulty of Attentional Task

Studies have shown that spatial attention remarkably affects the trial-to-trial response variability shared between neurons. Difficulty in the attentional task adjusts how much concentration we maintain on what is currently important and what is filtered as irrelevant sensory information. However, how task difficulty mediates the interactions between neurons with separated receptive fields (RFs) that are attended to or attended away is still not clear. We examined spike count correlations between single-unit activities recorded simultaneously in the primary visual cortex (V1) while monkeys performed a spatial attention task with two levels of difficulty. Moreover, the RFs of the two neurons recorded were non-overlapping to allow us to study fluctuations in the correlated responses between competing visual inputs when the focus of attention was allocated to the RF of one neuron. While increasing difficulty in the spatial attention task, spike count correlations were either decreased to become negative between neuronal pairs, implying competition among them, with one neuron (or none) exhibiting attentional enhancement of firing rate, or increased to become positive, suggesting inter-neuronal cooperation, with one of the pair showing attentional suppression of spiking responses. Besides, the modulation of spike count correlations by task difficulty was independent of the attended locations. These findings provide evidence that task difficulty affects the functional interactions between different neuronal pools in V1 when selective attention resolves the spatial competition.

Visual exposure enhances stimulus encoding and persistence in primary cortex

Experience shapes sensory responses, already at the earliest stages of cortical processing. We provide evidence that, in the primary visual cortex of anesthetized cats, brief repetitive exposure to a set of simple, abstract stimuli expands the range and decreases the variability of neuronal responses that persist after stimulus offset. These refinements increase the stimulus-specific clustering of neuronal population responses and result in a more efficient encoding of both stimulus identity and stimulus structure, thus potentially benefiting simple readouts in higher cortical areas. Similar results can be achieved via local plasticity mechanisms in recurrent networks, through self-organized refinements of internal dynamics that do not require changes in firing amplitudes.

The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in postexposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.

Spared perilesional V1 activity underlies training-induced recovery of luminance detection sensitivity in cortically-blind patients

Damage to the primary visual cortex (V1) causes homonymous visual-field loss long considered intractable. Multiple studies now show that perceptual training can restore visual functions in chronic cortically-induced blindness (CB). A popular hypothesis is that training can harness residual visual functions by recruiting intact extrageniculostriate pathways. Training may also induce plastic changes within spared regions of the damaged V1. Here, we link changes in luminance detection sensitivity with retinotopic fMRI activity before and after visual discrimination training in eleven patients with chronic, stroke-induced CB. We show that spared V1 activity representing perimetrically-blind locations prior to training predicts the amount of training-induced recovery of luminance detection sensitivity. Additionally, training results in an enlargement of population receptive fields in perilesional V1, which increases blind-field coverage and may support further recovery with subsequent training. These findings uncover fundamental changes in perilesional V1 cortex underlying training-induced restoration of conscious luminance detection sensitivity in CB.

In humans, stroke damage to V1 causes large visual field defects. Spared V1 activity prior to training predicts the amount of training-induced recovery in luminance detection sensitivity. Moreover, visual training changes population receptive field properties within residual V1 circuits.

Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

Studies of sensory-evoked neuronal responses often focus on mean spike rates, with fluctuations treated as internally-generated noise. However, fluctuations of spontaneous activity, often organized as traveling waves, shape stimulus-evoked responses and perceptual sensitivity. The mechanisms underlying these waves are unknown. Further, it is unclear whether waves are consistent with the low rate and weakly correlated “asynchronous-irregular” dynamics observed in cortical recordings. Here, we describe a large-scale computational model with topographically-organized connectivity and conduction delays relevant to biological scales. We find that spontaneous traveling waves are a general property of these networks. The traveling waves that occur in the model are sparse, with only a small fraction of neurons participating in any individual wave. Consequently, they do not induce measurable spike correlations and remain consistent with locally asynchronous irregular states. Further, by modulating local network state, they can shape responses to incoming inputs as observed in vivo.

Spontaneous traveling cortical waves shape neural responses. Using a large-scale computational model, the authors show that transmission delays shape locally asynchronous spiking dynamics into traveling waves without inducing correlations and boost responses to external input, as observed in vivo.

Of maps and grids

Neuroscience has made remarkable advances in accounting for how the brain performs its various functions. Consciousness, too, is usually approached in functional terms: the goal is to understand how the brain represents information, accesses that information, and acts on it. While useful for prediction, this functional, information-processing approach leaves out the subjective structure of experience: it does not account for how experience feels. Here, we consider a simple model of how a "grid-like" network meant to resemble posterior cortical areas can represent spatial information and act on it to perform a simple "fixation" function. Using standard neuroscience tools, we show how the model represents topographically the retinal position of a stimulus and triggers eye muscles to fixate or follow it. Encoding, decoding, and tuning functions of model units illustrate the working of the model in a way that fully explains what the model does. However, these functional properties have nothing to say about the fact that a human fixating a stimulus would also "see" it-experience it at a location in space. Using the tools of Integrated Information Theory, we then show how the subjective properties of experienced space-its extendedness-can be accounted for in objective, neuroscientific terms by the "cause-effect structure" specified by the grid-like cortical area. By contrast, a "map-like" network without lateral connections, meant to resemble a pretectal circuit, is functionally equivalent to the grid-like system with respect to representation, action, and fixation but cannot account for the phenomenal properties of space.