Biology subjects

Whyte, C. J.

Publications and source records attributed to Whyte, C. J..

7 recordsLinked to original sources

bioRxiv2025

Continuous Flash Suppression responses in mouse visual cortex: stimulus laterality and anesthesia effects

We investigated whether binocularly conflicting stimuli adapted from primate binocular rivalry studies could induce binocular response suppression in mouse visual cortex. We presented binocularly conflicting stimuli adapted from the paradigm of continuous flash suppression to awake and anesthetized mice and examined neuronal responses in visual areas V1 and LM. Neurons often showed a preference for the left or right eye where a monocular grating was presented, and their responses were modulated by presenting a conflicting flashing Mondrian stimulus to the other eye. The direction of this modulation depended on whether neurons preferentially responded to contralateral or ipsilateral gratings. Responses of cells with a preference for ipsilateral gratings were suppressed during binocular conflict, while contralateral-preferring cells were less suppressed or even enhanced by binocular conflict. Binocular conflict modulation continued to occur under anesthesia, but response recovery after intermittent binocular conflict only occurred in awake mice. Responses to Mondrians were modulated by binocular conflict in a similar fashion as those to grating stimuli, which suggests that mouse binocular conflict processing exhibits a distinctive dependency on stimulus laterality over other stimulus features. Finally, a canonical binocular rivalry model could be successfully fitted to our data but lacked sufficient competitive inhibition and adaptation strength to create oscillatory activity under binocular conflict.

neuroscience↗

bioRxiv2025

A plastic attractor model of flexible rule-based selective attention

A defining feature of human cognition is the ability to select and respond to stimuli flexibly in different circumstances. Here we show that a recently proposed class of small associative neural network, plastic attractors, can perform such flexible cognitive functions through the rapid formation of task-based attractors. We simulated a rule-based selective attention paradigm, in which agents respond to one task-relevant feature of a visual stimulus, while ignoring another, irrelevant feature, and respond to the attended feature according to a predefined stimulus-response mapping rule. The model consists of a broadly tuned prefrontal population with rapidly changing recurrent connections to sensory neurons that compete via lateral inhibition. In this framework, the rules governing the focus of selective attention are not coded explicitly, but arise as an emergent property of temporary associations between stimulus features and motor responses. The model exhibited activation properties that embody cornerstone concepts in current attentional theory including mixed selectivity, adaptive coding and biased competition, and reproduced a number of classic behavioral and neural findings. A causal test of the model using non-invasive brain stimulation concurrent with functional magnetic resonance imaging (TMS-fMRI) in humans showed that network perturbation reproduced neural decoding and behavioural data. When features were task-relevant, they rapidly formed synaptic connections with frontal cortex binding them into an attracting state, which manifest as prioritized representation of attended information, but this state was readily corrupted by brain stimulation. The model shows mechanistically that rapid synaptic changes could explain flexible rule-based control of attention.

animal behavior and cognition↗

bioRxiv2025

A Minimal Physiological Model of Perceptual Suppression and Breakthrough in Visual Rivalry

Visual rivalry paradigms provide a powerful tool for probing the mechanisms of visual awareness and perceptual suppression. While the dynamics and determinants of perceptual switches in visual rivalry have been extensively studied and modeled, recent advances in experimental design - particularly those that quantify the depth and variability of perceptual suppression - have outpaced the development of computational models. Here we extend an existing dynamical model of binocular rivalry to encompass two novel experimental paradigms: a threshold detection variant of binocular rivalry, and tracking continuous flash suppression. Together, these tasks provide complementary measures of the dynamics and magnitude of perceptual suppression. Through numerical simulation we demonstrate that a single mechanism, competitive (hysteretic) inhibition between slowly adapting monocular populations, is sufficient to account for the suppression depth findings across both paradigms. This unified model offers a foundation for the development of a quantitative theory of perceptual suppression in visual rivalry.

neuroscience↗

bioRxiv2025

A data-driven approach to identifying and evaluating connectivity-based neural correlates of consciousness

Identifying the neural correlates of conscious visual perception remains a major challenge in neuroscience, requiring theories that bridge between subjective experience and measurable neural correlates. However, theoretical interpretation of empirical evidence is often post hoc and susceptible to confirmation bias. Building upon the adversarial collaboration mediated by the COGITATE Consortium, we present a generalizable approach for the data-driven identification, evaluation, and theoretical modeling of connectivity-based neural correlates of conscious visual perception. Using the same magnetoencephalography (MEG) dataset and accompanying pre-registered hypotheses from the COGITATE Consortium, we systematically compared 246 functional connectivity (FC) measures between regions predicted to underlie conscious vision by Integrated Information Theory (IIT) and/or Global Neuronal Workspace Theory (GNWT). We identified a family of FC measures based on the barycenter--tracking the center of mass between two signals--as the top-performing stimulus decoding measures that generalize across regions central to predictions of both IIT and GNWT. To interpret these findings within a theoretical framework, we developed neural mass models that recapitulate the neural dynamics hypothesized to underlie conscious perception by each theory. Comparing simulated barycenter values from these models against empirically measured MEG data revealed that both the GNWT-based model, featuring delayed ignition dynamics, and the IIT-based model, which relied on synchronous sensory dynamics, captured the observed connectivity patterns. These results lend tentative support to GNWT, as the presence of ignition dynamics independent of task-demand conditions contradicts the predictions of IIT. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of conscious visual perception in an unbiased and interpretable manner.

neuroscience↗

bioRxiv2023

A Biophysical Model of Visual Rivalry Links Cellular Mechanisms to Signatures of Conscious Perception

Contemporary models of perceptual awareness lack tractable neurobiological constraints. Inspired by recent cellular recordings in a mouse model of tactile threshold detection, we constructed a biophysical model of perceptual awareness that incorporated essential features of thalamocortical anatomy and cellular physiology. Our model reproduced, and mechanistically explains, the key in vivo neural and behavioural signatures of perceptual awareness in the mouse model, as well as the response to a set of causal perturbations. We generalised the same model (with identical parameters) to a more complex task - visual rivalry - and found that the same thalamic-mediated mechanism of perceptual awareness determined perceptual dominance. This led to the generation of a set of novel, and directly testable, electrophysiological predictions. Analyses of the model based on dynamical systems theory show that perceptual awareness in simulations of both threshold detection and visual rivalry arises from the emergent systems-level dynamics of thalamocortical loops.

neuroscience↗

bioRxiv2020

Decoding Predictions and Violations of Object Position and Category in Time-resolved EEG

Classic models of predictive coding propose that sensory systems use information retained from prior experience to predict current sensory input. Any mismatch between predicted and current input (prediction error) is then fed forward up the hierarchy leading to a revision of the prediction. We tested this hypothesis in the domain of object vision using a combination of multivariate pattern analysis and time-resolved electroencephalography. We presented participants with sequences of images that stepped around fixation in a predictable order. On the majority of presentations, the images conformed to a consistent pattern of position order and object category order, however, on a subset of presentations the last image in the sequence violated the established pattern by either violating the predicted category or position of the object. Contrary to classic predictive coding when decoding position and category we found no differences in decoding accuracy between predictable and violation conditions. However, consistent with recent extensions of predictive coding, exploratory analyses showed that a greater proportion of predictions was made to the forthcoming position in the sequence than to either the previous position or the position behind the previous position suggesting that the visual system actively anticipates future input as opposed to just inferring current input.

neuroscience↗

bioRxiv2020

The Predictive Global Neuronal Workspace: A Formal Active Inference Model of Visual Consciousness

The global neuronal workspace (GNW) model has inspired over two decades of hypothesis driven research on the neural basis consciousness. However, recent studies have reported findings that are at odds with empirical predictions of the model. Further, the macro-anatomical focus of current GNW research has limited the specificity of predictions afforded by the model. In this paper we present a neurocomputational model - based on Active Inference - that captures central architectural elements of the GNW and is able to address these limitations. The resulting predictive global workspace casts neuronal dynamics as approximating Bayesian inference, allowing precise, testable predictions at both the behavioural and neural levels of description. We report simulations demonstrating the models ability to reproduce: 1) the electrophysiological and behaviour results observed in previous studies of inattentional blindness; and 2) the previously introduced four-way taxonomy predicted by the GNW, which describes the relationship between consciousness, attention, and sensory signal strength. We then illustrate how our model can reconcile/explain (apparently) conflicting findings, extend the GNW taxonomy to include the influence of prior expectations, and inspire novel paradigms to test associated behavioural and neural predictions.

neuroscience↗