1,987 publications from this institution
These findings reveal a disrupted heart-brain relationship in MDD, notably a link between cardiac dysregulation and reduced insula volume. This highlights the importance of potential body-brain targets for therapeutic intervention in mood disorders.
The aim of this study was to measure the contextual influence of globally coherent motion on visual cortical responses using functional magnetic resonance imaging. Our motivation was to test a prediction from representational theories of perception (i.e. predictive coding) that primary visual responses should be suppressed by top-down influences during coherent motion. We used a sparse stimulus array such that each element could not fall within the same classical receptive field of primary visual cortex neurons (i.e. precluding lateral interactions within V1). This enabled us to attribute differences, in striate cortex responses, to extra-classical receptive field effects mediated by backward connections. In accord with theoretical predictions we were able to demonstrate suppression of striate cortex activations to coherent relative to incoherent motion. These results suggest that suppression of primary visual cortex responses to coherent motion reflect extra-classical effects mediated by backward connections.
The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level.
Positron emission tomography was used to study the relationship between rCBF and symptom profiles in 30 schizophrenic patients. Factor analysis confirmed that the symptoms segregated into three syndromes – psychomotor poverty, disorganisation, and reality distortion – described previously. Analysis of the correlations between syndrome scores and rCBF revealed that each syndrome was associated with a specific pattern of perfusion in paralimbic and association cortex, and in related subcortical nuclei. The study confirmed predictions that psychomotor poverty and disorganisation are associated with altered perfusion at different loci in the pre-frontal cortex, and reality distortion with altered perfusion in the medial temporal lobe. The perfusion patterns suggest that the abnormalities of brain function underlying each of the three syndromes are not confined to single loci, but involve distributed neuronal networks.
In this work, we present a nonlinear principal component analysis (PCA) that identifies underlying sources causing the expression of spatial modes or patterns of activity in neuroimaging time series where these sources can interact to produce second-order modes. This nonlinear PCA uses a neural network architecture that embodies a specific form for the mixing of sources that is based on a second-order approximation to any general nonlinear mixing. The modes obtained have a unique rotation and scaling that does not depend on the biologically implausible constraints adopted by conventional PCA. Interactions among sources render the expression of any mode or brain system sensitive to the expression of others. The example considers interactions among functionally specialized brain systems (using a fMRI study of colour and motion processing).
The neuromodulatory effect of manipulating monoaminergic receptor function was assessed by combining a psychological and a pharmacological activation during repeated positron emission tomographic (PET) scans. The effects of buspirone (a 5-HT1A receptor partial agonist) on changes in regional cerebral blood flow (rCBF) associated with free word recall were examined. A factorial design was used to demonstrate a significant interaction (changes in rCBF brought about by psychological activation which depend on drug administration) in the left parahippocampal region. This interaction was an attenuation of increases in local neuronal activity (rCBF) related to memory function. Buspirone-induced decreases in rCBF, independent of the memory effect, were seen in the left prefrontal and parietal cortices. We suggest that combined psychological and pharmacological activation is a way of measuring direct (main) drug effects and modulatory effects on neurotransmission associated with cognitive functions (interaction).
Predictive processing (PP) approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of 'body-representations', and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory (e.g., of attention or consciousness) must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle (FEP) attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal 'representations' arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories (e.g., predictive processing) by which to guide discovery through the formal modelling of the embodied mind.
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications. The state-of-the-art -- in Bayesian sparsification of deep neural networks -- combines structural shrinkage priors on model weights with an approximate inference scheme based on stochastic variational inference. However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates. In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights. As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model. Our comparative study highlights the advantages of the BMR method relative to established approaches based on hierarchical horseshoe priors over model weights. We illustrate the potential of BMR across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.
