Biology subjects

Papale, P.

Publications and source records attributed to Papale, P..

14 recordsLinked to original sources

bioRxiv2026

Cell-type-specific coupling of single-unit spikes and cortical ripples in macaque and human V1

Cortical ripples are brief high-frequency (80-150 Hz) oscillatory bursts in the extracellular potential, associated with memory and attention. So far, it is unclear whether they are brain-state specific and which neuron types participate in their generation. We report cortical ripples in macaque V1 at rest, present with both eyes open and closed. Using massively parallel Utah array recordings, we identify six single-unit classes and describe their relation to ripple-band oscillations. The classes differ in firing-rate modulation, phase-locking strength, and preferred ripple-band phase. One class of putative deep-layer neurons exhibits non-sinusoidal phase locking, explained by a simple thresholding mechanism in a computational model. During ripples, most units fire irregularly with transient regular bursts. The ripple-associated spiking organisation is largely preserved during natural-image viewing and confirmed in V1 of a blind human. Together, these findings suggest cortical ripples arise from a shared circuit conserved across species and conditions.

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bioRxiv2026

A hierarchical computational motif unifies neural dynamics across the ventral visual stream

Neural representations within individual visual cortical areas are dynamic; they evolve over tens to hundreds of milliseconds, even in response to static images. These dynamics have typically been described as area-specific phenomena with unique computational signatures. Here, we show that representational dynamics across the ventral visual stream follow a common motif: representations within each area shift over time along the same complexity axis that organizes hierarchical visual areas. The spatiotemporal signatures of this shift indicate that it is broadly distributed across the neural population, rather than concentrated in specific subpopulations. These shifts are functionally consequential, allowing for the recognition of more complex images over the course of the response. Further, we show evidence in all visual areas of a 30 ms within-area predictivity signal whose properties are consistent with local recurrence, which may be driving the representational shifts. Yet, we show that current state-of-the-art dynamic models, including one with built-in local recurrent processing, fail to recapitulate the measured neural dynamics. Together, these results reveal a common temporal motif across the ventral hierarchy, suggest local recurrence as a possible driver, and provide a concrete dynamic target for future models of the ventral visual stream.

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bioRxiv2024

Deep generative networks reveal the tuning of neurons in IT and predict their influence on visual perception

Finding the tuning of visual neurons has kept neuroscientists busy for decades. One approach to this problem has been to test specific hypotheses on the relevance of a visual property (e.g. orientation or color), build a set of "artificial" stimuli that vary along that property and then record neural responses to those stimuli. Here, we present a complementary, data-driven method to retrieve the tuning properties of visual neurons. Exploiting deep generative networks and electrophysiology in monkeys, we first used a method to reconstruct any stimulus from its evoked neuronal activity in the inferotemporal cortex (IT). Then, by arbitrarily perturbing the response of individual cortical sites in the model, we generated naturalistic and interpretable sequences of images that strongly influence neural activity of that site. This method enables the discovery of previously unknown tuning properties of high-level visual neurons that are easily interpretable, which we tested with carefully controlled stimuli. When we knew which images drove the neurons, we activated the cells with electrical microstimulation and observed a predicable shift of the monkey perception in the direction of the preferred image. By allowing the brain to tell us what it cares about, we are no longer limited by our experimental imagination.

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bioRxiv2024

Experience-dependent predictions of feedforward and contextual information in mouse visual cortex

To navigate complex environments, the visual system must prioritize unexpected inputs or omissions over predictable sensory backgrounds. This saliency assignment is a hallmark of visual processing and depends on comparing visual feedforward inputs with learned contextual surrounds, a process often conceptualized as a difference computation. However, cortical circuitry poses fundamental challenges: visual contextual feedback is predominantly amplifying, and feedback-driven inhibitory neurons necessary for subtractive computations are relatively sparse. How then can complex contextual feedback be selectively compared with feedforward input at the level of individual neurons? Here, we show that divergent plasticity rules within single pyramidal neurons provide a solution to this problem. Using two-photon calcium imaging, we recorded layer 2/3 pyramidal cell responses in mouse primary visual cortex before and after visual familiarization. We dissociated feedforward and contextual signals by comparing responses to stimuli with and without an occluded sector. Repeated feedforward responses selectively weakened through adaptation and surround suppression, whereas contextual responses strengthened and generalized across scenes. These strengthened contextual inputs amplified non-adapted responses to novel images and drove neuronal activity when occlusion of feedforward input prevented surround suppression. This produced a conserved structure of feedforward and contextual responses across mice, monkeys, and humans. Ultimately, these results reveal a cellular mechanism for saliency assignment that does not require feedback-driven inhibition. Pyramidal neurons autonomously learn the generalized statistical structure of familiar contexts, allowing them to selectively amplify improbable inputs or omissions.

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bioRxiv2023

Brain2GAN: Feature-disentangled neural coding of visual perception in the primate brain

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding. Author summaryNeural coding seeks to understand how the brain represents the world by modeling the relationship between stimuli and internal neural representations thereof. This field focuses on predicting brain responses to stimuli (neural encoding) and deciphering information about stimuli from brain activity (neural decoding). Recent advances in generative adversarial networks (GANs; a type of machine learning model) have enabled the creation of photorealistic images. Like the brain, GANs also have internal representations of the images they create, referred to as "latents". More recently, a new type of feature-disentangled "w-latent" of GANs has been developed that more effectively separates different image features (e.g., color; shape; texture). In our study, we presented such GAN-generated pictures to a macaque with cortical implants and found that the underlying w-latents were accurate predictors of high-level brain activity. We then used these w-latents to reconstruct the perceived images with high fidelity. The remarkable similarities between our predictions and the actual targets indicate alignment in how w-latents and neural representations represent the same stimulus, even though GANs have never been optimized on neural data. This implies a general principle of shared encoding of visual phenomena, emphasizing the importance of feature disentanglement in deeper visual areas.

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bioRxiv2023

Visuomotor experience induces functional and structural plasticity of chandelier cells

Detailed characterization of interneuron types in primary visual cortex (V1) has greatly contributed to understanding visual perception, yet the role of chandelier cells (ChCs) in visual processing remains poorly characterized. Using viral tracing we found that V1 ChCs predominantly receive monosynaptic input from local layer 5 pyramidal cells and higher-order cortical regions. Two-photon calcium imaging and convolutional neural network modelling revealed that ChCs are visually responsive but weakly selective for stimulus content. In mice running in a virtual tunnel, ChCs respond strongly to events known to elicit arousal, including locomotion and visuomotor mismatch. Repeated exposure of the mice to the virtual tunnel was accompanied by reduced visual responses of ChCs and structural plasticity of ChC boutons and axon initial segment length. Finally, ChCs only weakly inhibited pyramidal cells. These findings suggest that ChCs provide an arousal-related signal to layer 2/3 pyramidal cells that may modulate their activity and/or gate plasticity of their axon initial segments during behaviorally relevant events.

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bioRxiv2023

Modulatory feedback explain object segmentation by attention

Studies in neuroscience inspired progress in the design of artificial neural networks (ANNs), and, vice versa, ANNs provide new insights into the functioning of brain circuits. So far, the focus has been on how ANNs can help to explain the tuning of neurons at various stages of the visual cortical hierarchy. However, the role of modulatory feedback connections, which play a role in attention and perceptual organization, has not been resolved yet. The present study presents a biologically plausible neural network that performs scene segmentation and can shift attention using modulatory feedback connections from higher to lower brain areas. The model replicates several neurophysiological signatures of recurrent processing. Specifically, figural regions elicit more activity in model units than background regions. The modulation of activity by figure and ground occurs at a delay after the first feedforward response, because it depends on a loop through the higher model areas. Importantly, the figural response enhancement is enhanced by object-based attention, which stays focused on the figural regions and does not spill over to the adjacent background, just as is observed in the visual cortex. Our results indicate how progress in artificial intelligence can be used to garner insight into the recurrent cortical processing for scene segmentation and object-based attention. Author SummaryRecent feedforward networks in artificial intelligence provide unmatched models of tuning of neurons in the visual cortex. However, these feedforward models do not explain the influences of object-based attention and image segmentation on neuronal responses, which rely on feedback interactions between cortical regions that are not included in the feedforward networks. In particular, the role of feedback connections from higher brain regions that modulate neural activity in lower cortical regions has not yet been studied extensively so that we still lack an in silico model of the role of these connections. Here, we present a biologically plausible neural network that successfully performs image segmentation and can shift object-based attention using modulatory feedback connections. The model evolved representations that mirror the properties of neurons in the visual cortex, including orientation tuning, shape-selectivity, surround suppression and a sensitivity to figure-ground organization, while trained only on a segmentation task. The new model provides insight into how the perception of coherent objects can emerge from the interaction between lower and higher visual cortical areas.

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bioRxiv2022

Feedback brings scene information to the representation of occluded image regions in area V1 of monkeys and humans

Neuronal activity in the primary visual cortex (V1) is driven by feedforward input from within the neurons receptive fields (RFs) and modulated by contextual information in regions surrounding the RF. The effect of contextual information on spiking activity occurs rapidly, and is therefore challenging to dissociate from feedforward input. To address this challenge, we recorded the spiking activity of V1 neurons in monkeys viewing either natural scenes or scenes where the information in the RF was occluded, effectively removing the feedforward input. We found that V1 neurons responded rapidly and selectively to occluded scenes. V1 responses elicited by occluded stimuli could be used to decode scene identity and could be predicted from those elicited by non-occluded images, indicating that there is overlap between visually-driven and contextual responses. We used representational similarity analysis to show that the structure of V1 representations of occluded scenes measured with electrophysiology in monkeys correlates strongly with the representations of the same scenes in humans measured with fMRI. Our results reveal that contextual influences alter the spiking activity of V1 in monkeys across large distances on a rapid time scale, carry information about scene identity and resemble those in human V1.

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bioRxiv2022

A visual representation of the hand in the resting somatomotor regions of the human brain

Hands are regularly in sight in everyday life. This visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of sensorimotor control. Spontaneous brain activity, i.e., ongoing activity in the absence of an active task (rest), is correlated among somatomotor regions that are jointly activated during motor tasks1. Moreover, recent studies suggest that spontaneous activity patterns do not only replay at rest task activation patterns, but also maintain a model of the statistical regularities (priors) of the body and environment, which may be used to predict upcoming behavior2-4. Here we test whether spontaneous activity in the human somatomotor cortex is modulated by visual stimuli that display hands vs. non-hand stimuli, and by the use/action they represent. We analyzed activity with fMRI and multivariate pattern analysis to examine the similarity between spontaneous (rest) activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex we observed a stronger (multi-voxel) spatial correlation between resting-state activity and natural hand picture patterns, as compared to other stimuli. A trend analysis showed that task-rest pattern similarity was influenced by inferred visual and motor attributes (i.e., correlation for hand>robot>glove>food). We did not observe any task-rest similarity in the visual cortex. We conclude that somatomotor brain regions code at rest for visual representations of hand stimuli and their inferred use.

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bioRxiv2021

The influence of objecthood on the representation of natural images in the visual cortex

Neurons in early visual cortex are not only sensitive to the image elements in their receptive field but also to the context determining whether the elements are part of an object or background. We here assessed the effect of objecthood in natural images on neuronal activity in early visual cortex, with fMRI in humans and electrophysiology in monkeys. We report that boundaries and interiors of objects elicit more activity than the background. Boundary effects occur remarkably early, implying that visual cortical neurons are tuned to features characterizing object boundaries in natural images. When a new image is presented the influence of the object interiors on neuronal activity occurs during a late phase of neuronal response and earlier when eye movements shift the image representation, implying that object representations are remapped across eye-movements. Our results reveal how object perception shapes the representation of natural images in early visual cortex.

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bioRxiv2019

Shape coding in occipito-temporal cortex relies on object silhouette, curvature and medial-axis

Object recognition relies on different transformations of the retinal input, carried out by the visual system, that range from local contrast to object shape and category. While some of those transformations are thought to occur at specific stages of the visual hierarchy, the features they represent are correlated (e.g., object shape and identity) and selectivity for the same feature overlaps in many brain regions. This may be explained either by collinearity across representations, or may instead reflect the coding of multiple dimensions by the same cortical population. Moreover, orthogonal and shared components may differently impact on distinctive stages of the visual hierarchy. We recorded functional MRI (fMRI) activity while participants passively attended to object images and employed a statistical approach that partitioned orthogonal and shared object representations to reveal their relative impact on brain processing. Orthogonal shape representations (silhouette, curvature and medial-axis) independently explained distinct and overlapping clusters of selectivity in occitotemporal (OTC) and parietal cortex. Moreover, we show that the relevance of shared representations linearly increases moving from posterior to anterior regions. These results indicate that the visual cortex encodes shared relations between different features in a topographic fashion and that object shape is encoded along different dimensions, each representing orthogonal features. New & NoteworthyThere are several possible ways of characterizing the shape of an object. Which shape description better describes our brain responses while we passively perceive objects? Here, we employed three competing shape models to explain brain representations when viewing real objects. We found that object shape is encoded in a multi-dimensional fashion and thus defined by the interaction of multiple features.

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bioRxiv2018

Emotionotopy: Gradients encode emotion dimensions in right temporo-parietal territories

Humans use emotions to decipher complex cascades of internal events. However, which mechanisms link descriptions of affective states to brain activity is unclear, with evidence supporting either local or distributed processing. A biologically favorable alternative is provided by the notion of gradient, which postulates the isomorphism between functional representations of stimulus features and cortical distance. Here, we use fMRI activity evoked by an emotionally charged movie and continuous ratings of the perceived emotion intensity to reveal the topographic organization of affective states. Results show that three orthogonal and spatially overlapping gradients encode the polarity, complexity and intensity of emotional experiences in right temporo-parietal territories. The spatial arrangement of these gradients allows the brain to map a variety of affective states within a single patch of cortex. As this organization resembles how sensory regions represent psychophysical properties (e.g., retinotopy), we propose emotionotopy as a principle of emotion coding.

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bioRxiv2018

Common spatiotemporal processing of visual features shapes object representation

Biological vision relies on representations of the physical world at different levels of complexity. Relevant features span from simple low-level properties, as contrast and spatial frequencies, to object-based attributes, as shape and category. However, how these features are integrated into coherent percepts is still debated. Moreover, these dimensions often share common biases: for instance, stimuli from the same category (e.g., tools) may have similar shapes. Here, using magnetoencephalography, we revealed the temporal dynamics of feature processing in human subjects attending to pictures of items pertaining to different semantic categories. By employing Relative Weights Analysis, we mitigated collinearity between model-based descriptions of stimuli and showed that low-level properties (contrast and spatial frequencies), shape (medial-axis) and category are represented within the same spatial locations early in time: 100-150ms after stimulus onset. This fast and overlapping processing may result from independent parallel computations, with categorical representation emerging later than the onset of low-level feature processing, yet before shape coding. Categorical information is represented both before and after shape also suggesting a role for this feature in the refinement of categorical matching.

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bioRxiv2017

Foreground Enhancement and Background Suppression in Human Early Visual System During Passive Perception of Natural Images

One of the major challenges in visual neuroscience is represented by foreground-background segmentation. Data from nonhuman primates show that segmentation leads to two distinct, but associated processes: the enhancement of neural activity during figure processing (i.e., foreground enhancement) and the suppression of background-related activity (i.e., background suppression). To study foreground-background segmentation in ecological conditions, we introduce a novel method based on parametric modulation of low-level image properties followed by application of simple computational image-processing models. By correlating the outcome of this procedure with human fMRI activity measured during passive viewing of 334 natural images, we reconstruct easily interpretable \"neural images\" from seven visual areas: V1, V2, V3, V3A, V3B, V4 and LOC. Results show evidence of foreground enhancement for all tested regions, while background suppression specifically occurs in V4 and LOC. \"Neural images\" reconstructed from V4 and LOC revealed a preserved spatial resolution of foreground textures, indicating a richer representation of the salient part of natural images, rather than a simplistic model of object shape. Our results indicate that scene segmentation is an automatic process that occurs during natural viewing, even when individuals are not required to perform any particular task.

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