1,987 publications from this institution
Abstract This technical report describes the dynamic causal modelling of mitigated epidemiological outcomes during the COVID-9 coronavirus outbreak in 2020. Dynamic causal modelling is a form of complex system modelling, which uses ‘real world’ timeseries to estimate the parameters of an underlying state space model using variational Bayesian procedures. Its key contribution—in an epidemiological setting—is to embed conventional models within a larger model of sociobehavioural responses—in a way that allows for (relatively assumption-free) forecasting. One advantage of using variational Bayes is that one can progressively optimise the model via Bayesian model selection: generally, the most likely models become more expressive as more data becomes available. This report summarises the model (on 6-Nov-20), eight months after the inception of dynamic causal modelling for COVID-19. This model—and its subsequent updates—is used to provide nowcasts and forecasts of latent behavioural and epidemiological variables as an open science resource. The current report describes the underlying model structure and the rationale for the variational procedures that underwrite Bayesian model selection.
Recently, there have been several concerted international efforts - the BRAIN initiative, European Human Brain Project and the Human Connectome Project, to name a few - that hope to revolutionize our understanding of the connected brain. Over the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We will highlight the theoretical advances made in the (dynamic) causal modelling of brain function - that may have escaped the wider audience of this article - and provide a brief overview of recent developments and interesting clinical applications. We hope that this article will engage the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed.
Selective attention to color or motion enhances activity in specialized areas of extrastriate cortex, but mechanisms of attentional modulation remain unclear. By dissociating modulation of visually evoked transient activity from the baseline for a particular attentional set, human functional neuroimaging was used to investigate the physiological basis of such effects. Baseline activity in motion- and color-sensitive areas of extrastriate cortex was enhanced by selective attention to these attributes, even without moving or colored stimuli. Further, visually evoked responses increased along with baseline activity. These results are consistent with the hypothesis that attention modulates sensitivity of neuronal populations to inputs by changing background activity.
This chapter focuses on dynamic coordination and how it is achieved, how it is related to cognitive functions and learning processes, and the role of neural oscillations in different frequency bands for dynamic coordination. It also considers modulation of coordination at the systems level and the relation of the mechanisms underlying coordination of behavior and cognition to neuropsychiatric disorders. In addition, the chapter examines dynamic coordination that arises from self-organization, the reduction in dimensionality associated with dynamic coordination, the theory of Coherent Infomax, Dynamic Pattern Theory, how optimization and synchrony are related to dynamic coordination, neural synchrony during human ontogeny, and how coordination contributes to perceptual and motor development. The chapter concludes with a discussion of the role of different frequency bands in sensory processing, attention and awareness, and motor circuits, together with how the prefrontal cortex modulates dynamic coordination.
Positron Emission Tomography (PET) represents a major advance in neuroimaging techniques enabling for the first time the quantitative acquisition of in. vivo pharmacological information in humans. This review will outline the range of its applications as a pharmacological tool with specific reference to its use in the measurement of neuroreceptors and in pharmacodynamic studies. A new and potentially more exciting application of PET is in pharmacological challenge combined with cognitive activation paradigms. The key issues in the design and implementation of this latter approach are detailed.
In Friston et al. ((2002) Neuroimage 16: 465–483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models. In this paper we present a series of models that exemplify the diversity of problems that can be addressed within this framework. In hierarchical linear observation models, both classical and empirical Bayesian approaches can be framed in terms of covariance component estimation (e.g., variance partitioning). To illustrate the use of the expectation–maximization (EM) algorithm in covariance component estimation we focus first on two important problems in fMRI: nonsphericity induced by (i) serial or temporal correlations among errors and (ii) variance components caused by the hierarchical nature of multisubject studies. In hierarchical observation models, variance components at higher levels can be used as constraints on the parameter estimates of lower levels. This enables the use of parametric empirical Bayesian (PEB) estimators, as distinct from classical maximum likelihood (ML) estimates. We develop this distinction to address: (i) The difference between response estimates based on ML and the conditional means from a Bayesian approach and the implications for estimates of intersubject variability. (ii) The relationship between fixed- and random-effect analyses. (iii) The specificity and sensitivity of Bayesian inference and, finally, (iv) the relative importance of the number of scans and subjects. The forgoing is concerned with within- and between-subject variability in multisubject hierarchical fMRI studies. In the second half of this paper we turn to Bayesian inference at the first (within-voxel) level, using PET data to show how priors can be derived from the (between-voxel) distribution of activations over the brain. This application uses exactly the same ideas and formalism but, in this instance, the second level is provided by observations over voxels as opposed to subjects. The ensuing posterior probability maps (PPMs) have enhanced anatomical precision and greater face validity, in relation to underlying anatomy. Furthermore, in comparison to conventional SPMs they are not confounded by the multiple comparison problem that, in a classical context, dictates high thresholds and low sensitivity. We conclude with some general comments on Bayesian approaches to image analysis and on some unresolved issues.
REVIEW article Front. Comput. Neurosci., 28 May 2013 https://doi.org/10.3389/fncom.2013.00057
We have used positron tomography (PET) to demonstrate that some parts of the motor system exhibit physiological adaptation during the repeated performance of a simple motor task, but others do not. In contrast to the primary sensori-motor cortex, the cerebellum exhibits a decrease in physiological activation (increases in regional blood flow during performance) with practice. A new application of factorial experimental design to PET activation studies was used to make these measurements in four normal males. This design allowed adaptation to be examined by testing for an interaction between regional cerebral blood flow (rCBF) increases brought about by a motor task and the number of trials (time). These findings are interpreted as the neurophysiological correlates of synaptic changes in the cerebellum associated with motor learning in man.
Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-supervised adaptation, such as contrastive formulations, has emerged in recent computational neuroscience and brain-inspired research. Nevertheless, current work on self-supervised learning relies on biologically implausible credit assignment -- in the form of backpropagation of errors -- and feedforward inference, typically a forward-locked pass. Predictive coding, in its mechanistic form, offers a biologically plausible means to sidestep these backprop-specific limitations. However, unsupervised predictive coding rests on learning a generative model of raw pixel input (akin to ``generative AI'' approaches), which entails predicting a potentially high dimensional input; on the other hand, supervised predictive coding, which learns a mapping between inputs to target labels, requires human annotation, and thus incurs the drawbacks of supervised learning. In this work, we present a scheme for self-supervised learning within a neurobiologically plausible framework that appeals to the free energy principle, constructing a new form of predictive coding that we call meta-representational predictive coding (MPC). MPC sidesteps the need for learning a generative model of sensory input (e.g., pixel-level features) by learning to predict representations of sensory input across parallel streams, resulting in an encoder-only learning and inference scheme. This formulation rests on active inference (in the form of sensory glimpsing) to drive the learning of representations, i.e., the representational dynamics are driven by sequences of decisions made by the model to sample informative portions of its sensorium.
Abstract This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function—and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity—accompanied with adaptation of firing thresholds—is a sufficient neuronal substrate to attain Bayes optimal control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.
