ORCID Profile
0000-0001-7984-8909
Current Organisation
University College London
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Signal Processing | Neurocognitive Patterns and Neural Networks | Human Biophysics | Cognitive Science |
Mental Health | Expanding Knowledge in the Biological Sciences | Expanding Knowledge in the Mathematical Sciences
Publisher: Elsevier BV
Date: 03-2009
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2016
Publisher: American Psychiatric Association Publishing
Date: 10-11-2020
Publisher: Cold Spring Harbor Laboratory
Date: 04-01-2021
DOI: 10.1101/2021.01.03.20248972
Abstract: This report describes a dynamic causal model that could be used to address questions about the rollout and efficacy of vaccines in the United Kingdom. For ex le, is suppression of community transmission a realistic aspiration? And, if not, what kind of endemic equilibrium might be achieved? What percentage of the population needs to be vaccinated? And over what timescale? It focuses on the synergies among (i) vaccination , (ii) the supported isolation of contacts of confirmed cases and (iii) restrictions on contact rates (i.e., lockdown and social distancing). To model these mitigations, we used a dynamic causal model that embeds an epidemiological model into agent-based behavioural model. The model structure and parameters were optimised to best explain responses—to the first and subsequent waves—enabling predictions over the forthcoming year under counterfactual scenarios. Illustrative analyses suggest that the full potential of vaccination is realised by increasing the efficacy of contact tracing: for ex le, under idealised (best case) assumptions—of an effective vaccine and efficient isolation of infected pre-symptomatic cases— suppression of community transmission would require 50% herd immunity by vaccinating 22% by the end of 2021 i.e., 15 million people or about 50,000 per day. With no change in the isolation of contacts, 36% would require vaccination, i.e., 25 million people. These figures should not be read as estimates of the actual number of people requiring vaccination however, they illustrate the potential of this kind of model to quantify interactions among public health interventions. We anticipate using this model in a few months—to estimate the average effectiveness of vaccines when more data become available.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Springer Science and Business Media LLC
Date: 23-04-2008
Publisher: Elsevier BV
Date: 05-2013
DOI: 10.1016/J.NEUROIMAGE.2012.12.076
Abstract: We demonstrate the capacity of dynamic causal modeling to characterize the nonlinear coupling among cortical sources that underlie time-frequency modulations in MEG data. Our experimental task involved the mental rotation of hand drawings that ten subjects used to decide if it was a right or left hand. Reaction times were shorter when the stimuli were presented with a small rotation angle (fast responses) compared to a large rotation angle (slow responses). The grand-averaged data showed that in both cases performance was accompanied by a marked increase in gamma activity in occipital areas and a concomitant decrease in alpha and beta power in occipital and motor regions. Modeling directed (cross) frequency interactions between the two regions revealed that after the stimulus induced a gamma increase and beta decrease in occipital regions, interactions with the motor area served to attenuate these modulations. The difference between fast and slow behavioral responses was manifest as an altered coupling strength in both forward and backward connections, which led to a less pronounced attenuation for more difficult (slow reaction time) trials. This was mediated by a (backwards) beta to gamma coupling from motor till occipital sources, whereas other interactions were mainly within the same frequency. Results are consistent with the theory of predictive coding and suggest that during motor imagery, the influence of motor areas on activity in occipital cortex co-determines performance. Our study illustrates the benefit of modeling experimental responses in terms of a generative model that can disentangle the contributions of intra-areal vis-à-vis inter-areal connections to time-frequency modulations during task performance.
Publisher: MIT Press - Journals
Date: 2021
DOI: 10.1162/NETN_A_00175
Abstract: At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions—and analyses—of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Frontiers Media SA
Date: 08-02-2022
DOI: 10.3389/FNINS.2022.802396
Abstract: Humans are highly proficient in learning about the environments in which they operate. They form flexible spatial representations of their surroundings that can be leveraged with ease during spatial foraging and navigation. To capture these abilities, we present a deep Active Inference model of goal-directed behavior, and the accompanying belief updating. Active Inference rests upon optimizing Bayesian beliefs to maximize model evidence or marginal likelihood. Bayesian beliefs are probability distributions over the causes of observable outcomes. These causes include an agent’s actions, which enables one to treat planning as inference. We use simulations of a geocaching task to elucidate the belief updating—that underwrites spatial foraging—and the associated behavioral and neurophysiological responses. In a geocaching task, the aim is to find hidden objects in the environment using spatial coordinates. Here, synthetic agents learn about the environment via inference and learning (e.g., learning about the likelihoods of outcomes given latent states) to reach a target location, and then forage locally to discover the hidden object that offers clues for the next location.
Publisher: Elsevier BV
Date: 06-2007
DOI: 10.1016/J.NEUROIMAGE.2007.02.046
Abstract: Dynamic causal modelling is an approach to characterising evoked responses as measured by magneto/electroencephalography (M/EEG). A dynamic causal model (DCM) is a spatiotemporal, generative network model for event-related fields/responses (ERP/ERF) data. Using Bayesian model inversion, one can compute the posterior distributions of the DCM's physiological parameters and its marginal likelihood for model comparison. Model comparison can be used to test mechanistic hypotheses about how electrophysiological data were generated. In this work, we look at the relative importance of changes in intrinsic (within source) and extrinsic (between sources) connections in generating mismatch responses. In short, we introduce the modulation of intrinsic connectivity to the DCM framework. This is useful for testing hypotheses about adaptation of neuronal responses to local influences, in relation to influences that are mediated by long-range extrinsic connections (forward, backward, and lateral) from other sources. We illustrate this extension using synthetic data and empirical data from an oddball ERP experiment.
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 07-2023
Publisher: Elsevier BV
Date: 08-2008
Publisher: Springer Science and Business Media LLC
Date: 04-1991
DOI: 10.1007/BF00231461
Publisher: Oxford University Press (OUP)
Date: 2019
DOI: 10.1093/NC/NIZ012
Abstract: Perceptual awareness depends upon the way in which we engage with our sensorium. This notion is central to active inference, a theoretical framework that treats perception and action as inferential processes. This variational perspective on cognition formalizes the notion of perception as hypothesis testing and treats actions as experiments that are designed (in part) to gather evidence for or against alternative hypotheses. The common treatment of perception and action affords a useful interpretation of certain perceptual phenomena whose active component is often not acknowledged. In this article, we start by considering Troxler fading – the dissipation of a peripheral percept during maintenance of fixation, and its recovery during free (saccadic) exploration. This offers an important ex le of the failure to maintain a percept without actively interrogating a visual scene. We argue that this may be understood in terms of the accumulation of uncertainty about a hypothesized stimulus when free exploration is disrupted by experimental instructions or pathology. Once we take this view, we can generalize the idea of using bodily (oculomotor) action to resolve uncertainty to include the use of mental (attentional) actions for the same purpose. This affords a useful way to think about binocular rivalry paradigms, in which perceptual changes need not be associated with an overt movement.
Publisher: Cambridge University Press (CUP)
Date: 10-2001
DOI: 10.1017/S0140525X01250092
Abstract: Tsuda examines the potential contribution of nonlinear dynamical systems, with many degrees of freedom, to understanding brain function. We offer suggestions concerning symmetry and transients to strengthen the physiological motivation and theoretical consistency of this novel research direction: Symmetry plays a fundamental role, theoretically and in relation to real brains. We also highlight a distinction between chaotic “transience” and “itineracy.”
Publisher: Elsevier BV
Date: 03-2018
Publisher: Wiley
Date: 09-04-2009
DOI: 10.1002/HBM.20775
Publisher: American Association for the Advancement of Science (AAAS)
Date: 13-05-2011
Abstract: Discerning the neural correlates of (un)consciousness sheds light on the mechanisms underlying vegetative states.
Publisher: Frontiers Media SA
Date: 20-07-2017
Publisher: Elsevier BV
Date: 02-2015
Publisher: BMJ
Date: 10-1994
Abstract: The localised PET cerebral correlates of the painful experience in the normal human brain have previously been demonstrated. This study examined whether these responses are different in patients with chronic atypical facial pain. The regional cerebral responses to non-painful and painful thermal stimuli in six female patients with atypical facial pain and six matched female controls were studied by taking serial measurements of regional blood flow by PET. Both groups displayed highly significant differences in responses to painful heat compared with non-painful heat in the thalamus, anterior cingulate cortex (area 24), lentiform nucleus, insula, and prefrontal cortex. These structures are closely related to the "medial pain system". The atypical facial pain group had increased blood flow in the anterior cingulate cortex and decreased blood flow in the prefrontal cortex. These findings show the importance of the anterior cingulate cortex and the reciprocal (possibly inhibitory) connections with the prefrontal cortex in the processing of pain in patients with this disorder. A hypothesis is proposed to explain the mechanisms of cognitive and pharmacological manipulation of these pain processes.
Publisher: Elsevier BV
Date: 09-2003
DOI: 10.1016/S1053-8119(03)00332-X
Abstract: It has been proposed that schizophrenia arises through a disturbance of coupling between large-scale cortical systems. This "disconnection hypothesis" is tested by applying a measure of dynamical interdependence to scalp EEG data. EEG data were collected from 40 subjects with a first episode of schizophrenia and 40 matched healthy controls. An algorithm for the detection of dynamical interdependence was applied to six pairs of bipolar electrodes in each subject. The topographic organization of the interdependence was calculated and served as the principle measure of cortical integration. The rate of occurrence of dynamical interdependence did not statistically differ between subject groups at any of the sites. However, the topography across the scalp was significantly different between the two groups. Specifically, nonlinear interdependence tended to occur in larger concurrent "clusters" across the scalp in schizophrenia than in the healthy subjects. This disturbance was reflected most strongly in left intrahemispheric coupling and did not differ significantly according to symptomatology. Medication dose and subject arousal were not observed to be confounding factors. The study of dynamical interdependence in scalp EEG data does not support a straightforward interpretation of the disconnection hypothesis-that there is a decrease in the strength of functional coupling between adjacent cortical regions. Rather, it suggests a dysregulation in the organization of dynamical interactions across supraregional brain systems.
Publisher: Elsevier BV
Date: 05-2013
Publisher: Ubiquity Press, Ltd.
Date: 2021
DOI: 10.5334/CPSY.70
Publisher: Elsevier BV
Date: 07-2014
Publisher: Society for Neuroscience
Date: 03-1991
DOI: 10.1523/JNEUROSCI.11-03-00641.1991
Abstract: We have used positron emission tomography (PET), which measures regional cerebral blood flow (rCBF), to demonstrate directly the specialization of function in the normal human visual cortex. A novel technique, statistical parametric mapping, was used to detect foci of significant change in cerebral blood flow within the prestriate cortex, in order to localize those parts involved in the perception of color and visual motion. For color, we stimulated the subjects with a multicolored abstract display containing no recognizable objects (Land color Mondrian) and contrasted the resulting blood flow maps with those obtained when subjects viewed an identical display consisting of equiluminous shades of gray. The comparison identified a unique area (area V4) located in the lingual and fusiform gyri of the prestriate cortex. For motion, blood flow maps when subjects viewed moving or stationary black and white random-square patterns were contrasted. The comparison identified a unique area located in the region of the temporo-parieto-occipital junction (area V5). We thus provide direct evidence to show that, just as in the macaque monkey, different areas of the human prestriate visual cortex are specialized for different attributes of vision. The striate cortex (V1) and the contiguous visual area (V2), which in the monkey brain feed both the homologous areas, were active in all 4 conditions. This pattern of activity allowed us to use an extension of the approach to assess the functional relationship between the 3 areas during color and motion stimulation. This is based on an hypothesis-led analysis of the covariance structure of the blood flow maps and promises to be a powerful tool for inferring anatomical pathways in the normal human brain.(ABSTRACT TRUNCATED AT 250 WORDS)
Publisher: Frontiers Media SA
Date: 04-04-2022
DOI: 10.3389/FPSYT.2022.763380
Abstract: This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.BPSC.2017.01.010
Abstract: Biological phenomena arise through interactions between an organism's intrinsic dynamics and stochastic forces-random fluctuations due to external inputs, thermal energy, or other exogenous influences. Dynamic processes in the brain derive from neurophysiology and anatomical connectivity stochastic effects arise through sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random effects can be studied with stochastic dynamic models (SDMs). This article, Part I of a two-part series, offers a primer of SDMs and their application to large-scale neural systems in health and disease. The companion article, Part II, reviews the application of SDMs to brain disorders. SDMs generate a distribution of dynamic states, which (we argue) represent ideal candidates for modeling how the brain represents states of the world. When augmented with variational methods for model inversion, SDMs represent a powerful means of inferring neuronal dynamics from functional neuroimaging data in health and disease. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Elsevier BV
Date: 07-2007
Publisher: Elsevier BV
Date: 03-2016
Publisher: Cold Spring Harbor Laboratory
Date: 18-04-2018
DOI: 10.1101/303933
Abstract: Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1 min of electroencephalogram (EEG) recordings during rest, ided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (between-subject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the saliency network comprised both subject-specific components and a common component. For the default mode network, connectivity trajectories only showed a common component. For both networks, connections to higher order regions appear to monotonically increase during the one minute period. These results not only establish the predictive validity of dynamic connectivity estimates – in virtue of detecting systematic changes over subjects – they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.
Publisher: American Medical Association (AMA)
Date: 04-2015
DOI: 10.1001/JAMAPSYCHIATRY.2014.2490
Abstract: Patients with melancholia report a distinct and intrusive dysphoric state during internally generated thought. Melancholia has long been considered to have a strong biological component, but evidence for its specific neurobiological origins is limited. The distinct neurocognitive, psychomotor, and mood disturbances observed in melancholia do, however, suggest aberrant coordination of frontal-subcortical circuitry, which may best be captured through analysis of complex brain networks. To investigate the effective connectivity between spontaneous (resting-state) brain networks in melancholia, focusing on networks underlying attention and interoception. We performed a cross-sectional, observational, resting-state functional magnetic resonance imaging study of 16 participants with melancholia, 16 with nonmelancholic depression, and 16 in iduals serving as controls at a hospital-based research institute between August 30, 2010, and June 27, 2012. We identified 5 canonical resting-state networks (default mode, executive control, left and right frontoparietal attention, and bilateral anterior insula) and inferred spontaneous interactions among these networks using dynamic causal modeling. Graph theoretic measures of brain connectivity, namely, in-degree and out-degree of each network and edge connectivity, between regions composed our principal between-group contrasts. Melancholia was characterized by a pervasive disconnection involving anterior insula and attentional networks compared with participants in the control (Mann-Whitney, 189.00 z = 2.38 P = .02) and nonmelancholic depressive (Mann-Whitney, 203.00 z = 2.93 P = .004) groups. Decreased effective connectivity between the right frontoparietal and insula networks was present in participants with melancholic depression compared with those with nonmelancholic depression (χ2 = 8.13 P = .004). Reduced effective connectivity between the insula and executive networks was found in in iduals with melancholia compared with healthy controls (χ2 = 8.96 P = .003). We observed reduced effective connectivity in resting-state functional magnetic resonance imaging between key networks involved in attention and interoception in melancholia. We propose that these abnormalities underlie the impoverished variety and affective quality of internally generated thought in this disorder.
Publisher: Springer Science and Business Media LLC
Date: 21-05-2019
Publisher: F1000 Research Ltd
Date: 30-09-2020
DOI: 10.12688/WELLCOMEOPENRES.16253.2
Abstract: This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
Publisher: Elsevier BV
Date: 07-2017
DOI: 10.1016/J.NEUROIMAGE.2017.02.090
Abstract: The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from in idual fMRI data.
Publisher: MDPI AG
Date: 14-05-2021
DOI: 10.3390/E23050609
Abstract: Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation and specialization are released from their autopoietic homeostasis. This induces the construction of cancer niches and maintains their survival. In this paper, we characterise cancer niche construction as a direct consequence of interactions between clusters of cancer and healthy cells. Explicitly, we evaluate these higher-order interactions between niches of cancer and healthy cells using Kikuchi approximations to the free energy. Kikuchi’s free energy is measured in terms of changes to the sum of energies of baseline clusters of cells (or nodes) minus the energies of overcounted cluster intersections (and interactions of interactions, etc.). We posit that these changes in energy node clusters correspond to a long-term reduction in the complexity of the system conducive to cancer niche survival. We validate this formulation through numerical simulations of apoptosis, local cancer growth, and metastasis, and highlight its implications for a computational understanding of the etiopathology of cancer.
Publisher: F1000 Research Ltd
Date: 09-2020
DOI: 10.12688/WELLCOMEOPENRES.16253.1
Abstract: This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
Publisher: Frontiers Media SA
Date: 04-05-2016
Publisher: Elsevier BV
Date: 04-2017
DOI: 10.1016/J.BPSC.2016.12.009
Abstract: Brain activity derives from intrinsic dynamics (due to neurophysiology and anatomical connectivity) in concert with stochastic effects that arise from sensory fluctuations, brainstem discharges, and random microscopic states such as thermal noise. The dynamic evolution of systems composed of both dynamic and random fluctuations can be studied with stochastic dynamic models (SDMs). This article, Part II of a two-part series, reviews applications of SDMs to large-scale neural systems in health and disease. Stochastic models have already elucidated a number of pathophysiological phenomena, such as epilepsy and hypoxic ischemic encephalopathy, although their use in biological psychiatry remains rather nascent. Emerging research in this field includes phenomenological models of mood fluctuations in bipolar disorder and biophysical models of functional imaging data in psychotic and affective disorders. Together with deeper theoretical considerations, this work suggests that SDMs will play a unique and influential role in computational psychiatry, unifying empirical observations with models of perception and behavior.
Publisher: Cambridge University Press (CUP)
Date: 30-05-2019
DOI: 10.1017/S0140525X19001213
Abstract: The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as “shared expectations,” the “selective patterning of attention and behaviour,” “cultural evolution,” “cultural inheritance,” and “implicit learning” are the main candidates to underpin a unifying account of cognition and the acquisition of culture however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process “thinking through other minds” (TTOM) – in effect, the process of inferring other agents’ expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.
Publisher: Public Library of Science (PLoS)
Date: 07-11-2013
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier
Date: 2007
Publisher: Elsevier BV
Date: 09-2008
DOI: 10.1016/J.COGNITION.2008.05.010
Abstract: Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom-up, driving signal for that stimulus is explained away while, crucially, the bottom-up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.
Publisher: MDPI AG
Date: 13-08-2020
DOI: 10.3390/E22080889
Abstract: The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference and (2) if so, to assess which philosophical stance—in relation to the ontological and epistemological status of representations—is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account—an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the ‘aboutness’ or intentionality of cognitive systems our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Public Library of Science (PLoS)
Date: 21-02-2013
Publisher: Cold Spring Harbor Laboratory
Date: 08-01-2021
DOI: 10.1101/2021.01.07.21249389
Abstract: Diminished synaptic gain – the sensitivity of postsynaptic responses to neural inputs – may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. Participants with schizophrenia (Scz, n=108), their relatives (n=57), and controls (n=107) underwent three electroencephalography paradigms – resting, mismatch negativity, and 40 Hz auditory steady-state response – and resting functional magnetic resonance imaging. Dynamic causal modelling was used to quantify synaptic connectivity in cortical microcircuits. Across all four paradigms, characteristic Scz data features were best explained by models with greater self-inhibition (decreased synaptic gain), in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in Scz, in three paradigms. Thus, psychotic symptoms of Scz may result from a downregulation of inhibitory interneurons that may compensate for diminished postsynaptic gain in pyramidal cells.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 02-12-2011
Abstract: King et al . raise some technical issues about our recent study showing impaired top-down processes in the vegetative state. We welcome the opportunity to provide more details about our methods and results and to resolve their concerns. We substantiate our interpretation of the results and provide a point-by-point response to the issues raised.
Publisher: F1000 Research Ltd
Date: 08-02-2021
DOI: 10.12688/WELLCOMEOPENRES.15986.2
Abstract: We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.
Publisher: Elsevier BV
Date: 11-1992
DOI: 10.1016/0304-3940(92)90072-F
Abstract: The relationship between activity within the human auditory cortices and the presentation rate of heard words was investigated by measuring changes in regional cerebral blood flow with positron emission tomography. We demonstrate that in the primary auditory cortices and middle regions of the superior temporal gyri there is a linear relationship between the rate of presentation of heard words and blood flow response. In contrast, the blood flow response in an area of the left posterior superior temporal gyrus (Wernicke's area) is primarily dependent on the occurrence of words irrespective of their rate of presentation. The primary auditory cortices are associated with the early processing of complex acoustic signals whereas Wernicke's area is associated with the comprehension of heard words. This study demonstrates for the first time that time dependent sensory signals (heard words) detected in the primary auditory cortices are transformed into a time invariant output which is channelled to a functionally specialised region--Wernicke's area. Wernicke's area is therefore distinguished from other areas of the auditory cortex by direct observation of signal transformation rather than by association with a specific behavioural task.
Publisher: F1000 Research Ltd
Date: 15-03-2021
DOI: 10.12688/WELLCOMEOPENRES.15986.3
Abstract: We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several instantiations of this (epidemic) model to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.
Publisher: Elsevier BV
Date: 2016
Publisher: Elsevier BV
Date: 06-2003
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 21-02-2019
DOI: 10.1007/S11229-019-02115-X
Abstract: We present a multiscale integrationist interpretation of the boundaries of cognitive systems, using the Markov blanket formalism of the variational free energy principle. This interpretation is intended as a corrective for the philosophical debate over internalist and externalist interpretations of cognitive boundaries we stake out a compromise position. We first survey key principles of new radical (extended, enactive, embodied) views of cognition. We then describe an internalist interpretation premised on the Markov blanket formalism. Having reviewed these accounts, we develop our positive multiscale account. We argue that the statistical seclusion of internal from external states of the system—entailed by the existence of a Markov boundary—can coexist happily with the multiscale integration of the system through its dynamics. Our approach does not privilege any given boundary (whether it be that of the brain, body, or world), nor does it argue that all boundaries are equally prescient. We argue that the relevant boundaries of cognition depend on the level being characterised and the explanatory interests that guide investigation. We approach the issue of how and where to draw the boundaries of cognitive systems through a multiscale ontology of cognitive systems, which offers a multidisciplinary research heuristic for cognitive science.
Publisher: Elsevier BV
Date: 06-2019
Publisher: F1000 Research Ltd
Date: 26-05-2020
DOI: 10.12688/WELLCOMEOPENRES.15986.1
Abstract: We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity—and the exchange of people between regions—and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 12-2019
Publisher: Center for Open Science
Date: 19-09-2021
Abstract: Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organisation and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonist of 5HT2A receptors, located at the apex of the cortical hierarchy may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half life, suggesting that psychedelics may have long-term effects on neural plasticity – that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a surrender of ego resistance that increases the repertoire of perceptual hypotheses, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world—and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging.
Publisher: Informa UK Limited
Date: 29-09-2023
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 10-2013
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.JNEUMETH.2018.05.002
Abstract: There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM). In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain). Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity - as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates suggesting the 7T data are more informative - and furnish more efficient estimates. In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization. In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.
Publisher: The Royal Society
Date: 04-2018
Abstract: In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields.
Publisher: MIT Press - Journals
Date: 10-2017
DOI: 10.1162/NETN_A_00015
Abstract: This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.
Publisher: Society for Neuroscience
Date: 29-07-2009
DOI: 10.1523/JNEUROSCI.1126-09.2009
Abstract: Marginal utility theory prescribes the relationship between the objective property of the magnitude of rewards and their subjective value. Despite its pervasive influence, however, there is remarkably little direct empirical evidence for such a theory of value, let alone of its neurobiological basis. We show that human preferences in an intertemporal choice task are best described by a model that integrates marginally diminishing utility with temporal discounting. Using functional magnetic resonance imaging, we show that activity in the dorsal striatum encodes both the marginal utility of rewards, over and above that which can be described by their magnitude alone, and the discounting associated with increasing time. In addition, our data show that dorsal striatum may be involved in integrating subjective valuation systems inherent to time and magnitude, thereby providing an overall metric of value used to guide choice behavior. Furthermore, during choice, we show that anterior cingulate activity correlates with the degree of difficulty associated with dissonance between value and time. Our data support an integrative architecture for decision making, revealing the neural representation of distinct subcomponents of value that may contribute to impulsivity and decisiveness.
Publisher: Elsevier BV
Date: 12-2018
Publisher: Oxford University PressNew York
Date: 30-04-2010
DOI: 10.1093/ACPROF:OSO/9780195372731.003.0015
Abstract: Developments in M/EEG analysis allows for models that are sophisticated enough to capture the full richness of the data. This chapter focuses on dynamic causal modeling (DCM) for M/EEG, which entails the inversion of informed spatiotemporal models of observed responses. The idea is to model condition-specific responses over channels and peristimulus time with the same model, where the differences among conditions are explained by changes in only a few key parameters. The face and predictive validity of DCM have been established, which makes it a potentially useful tool for group studies.
Publisher: Elsevier BV
Date: 03-2018
Publisher: Cold Spring Harbor Laboratory
Date: 03-12-2021
DOI: 10.1101/2021.12.02.471005
Abstract: Integrating neurons into digital systems to leverage their innate intelligence may enable performance infeasible with silicon alone, along with providing insight into the cellular origin of intelligence. We developed DishBrain , a system which exhibits natural intelligence by harnessing the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins, are integrated with in silico computing via high-density multielectrode array. Through electrophysiological stimulation and recording, cultures were embedded in a simulated game-world, mimicking the arcade game ‘Pong’. Applying a previously untestable theory of active inference via the Free Energy Principle, we found that learning was apparent within five minutes of real-time gameplay, not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organise in a goal-directed manner in response to sparse sensory information about the consequences of their actions.
Publisher: Proceedings of the National Academy of Sciences
Date: 28-01-2019
Abstract: Psychedelics exert unique effects on human consciousness. The thalamic filter model suggests that core effects of psychedelics may result from gating deficits, based on a disintegration of information processing within cortico–striato–thalamo-cortical (CSTC) feedback loops. To test this hypothesis, we characterized changes in directed (effective) connectivity between selected CTSC regions after acute administration of lysergic acid diethylamide (LSD), and after pretreatment with Ketanserin (a selective serotonin 2A receptor antagonist) plus LSD in a double-blind, randomized, placebo-controlled, cross-over study in 25 healthy participants. We used spectral dynamic causal modeling (DCM) for resting-state fMRI data. Fully connected DCM models were specified for each treatment condition to investigate the connectivity between the following areas: thalamus, ventral striatum, posterior cingulate cortex, and temporal cortex. Our results confirm major predictions proposed in the CSTC model and provide evidence that LSD alters effective connectivity within CSTC pathways that have been implicated in the gating of sensory and sensorimotor information to the cortex. In particular, LSD increased effective connectivity from the thalamus to the posterior cingulate cortex in a way that depended on serotonin 2A receptor activation, and decreased effective connectivity from the ventral striatum to the thalamus independently of serotonin 2A receptor activation. Together, these results advance our mechanistic understanding of the action of psychedelics in health and disease. This is important for the development of new pharmacological therapeutics and also increases our understanding of the mechanisms underlying the potential clinical efficacy of psychedelics.
Publisher: Elsevier BV
Date: 08-2008
Publisher: Wiley
Date: 28-03-2014
Publisher: Proceedings of the National Academy of Sciences
Date: 26-12-2007
Abstract: Neuronal responses to stimuli, measured electrophysiologically, unfold over several hundred milliseconds. Typically, they show characteristic waveforms with early and late components. It is thought that early or exogenous components reflect a perturbation of neuronal dynamics by sensory input bottom-up processing. Conversely, later, endogenous components have been ascribed to recurrent dynamics among hierarchically disposed cortical processing levels, top-down effects. Here, we show that evoked brain responses are generated by recurrent dynamics in cortical networks, and late components of event-related responses are mediated by backward connections. This evidence is furnished by dynamic causal modeling of mismatch responses, elicited in an oddball paradigm. We used the evidence for models with and without backward connections to assess their likelihood as a function of peristimulus time and show that backward connections are necessary to explain late components. Furthermore, we were able to quantify the contribution of backward connections to evoked responses and to source activity, again as a function of peristimulus time. These results link a generic feature of brain responses to changes in the sensorium and a key architectural component of functional anatomy namely, backward connections are necessary for recurrent interactions among levels of cortical hierarchies. This is the theoretical cornerstone of most modern theories of perceptual inference and learning.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2019
DOI: 10.1038/S41598-019-38801-6
Abstract: Neuroimaging studies in social neuroscience have largely relied on functional connectivity (FC) methods to characterize the functional integration between different brain regions. However, these methods have limited utility in social-cognitive studies that aim to understand the directed information flow among brain areas that underlies complex psychological processes. In this study we combined functional and effective connectivity approaches to characterize the functional integration within the Default Mode Network (DMN) and its role in self-perceived empathy. Forty-two participants underwent a resting state fMRI scan and completed a questionnaire of dyadic empathy. Independent Component Analysis (ICA) showed that higher empathy scores were associated with an increased contribution of the medial prefrontal cortex (mPFC) to the DMN spatial mode. Dynamic causal modelling (DCM) combined with Canonical Variance Analysis (CVA) revealed that this association was mediated indirectly by the posterior cingulate cortex (PCC) via the right inferior parietal lobule (IPL). More specifically, in participants with higher scores in empathy, the PCC had a greater effect on bilateral IPL and the right IPL had a greater influence on mPFC. These results highlight the importance of using analytic approaches that address directed and hierarchical connectivity within networks, when studying complex psychological phenomena, such as empathy.
Publisher: Cold Spring Harbor Laboratory
Date: 11-01-2021
DOI: 10.1101/2021.01.10.21249520
Abstract: This report considers three mechanisms that might underlie the course of the secondary peak of coronavirus infections in the United Kingdom. It considers: (i) fluctuations in transmission strength (ii) seasonal fluctuations in contact rates and (iii) fluctuations in testing. Using dynamic causal modelling, we evaluated the contribution of all combinations of these three mechanisms using Bayesian model comparison. We found overwhelming evidence for the combination of all mechanisms, when explaining 16 types of data. Quantitatively, there was clear evidence for an increase in transmission strength of 57% over the past months (e.g., due to viral mutation), in the context of increased contact rates (e.g., rebound from national lockdowns) and increased test rates (e.g., due to the inclusion of lateral flow tests). Models with fluctuating transmission strength outperformed models with fluctuating contact rates. However, the best model included all three mechanisms suggesting that the resurgence during the second peak can be explained by an increase in effective contact rate that is the product of a rebound of contact rates following a national lockdown and increased transmission risk due to viral mutation.
Publisher: Public Library of Science (PLoS)
Date: 23-02-2022
DOI: 10.1371/JOURNAL.PCBI.1009807
Abstract: Estimating the changes of epidemiological parameters, such as instantaneous reproduction number, R t , is important for understanding the transmission dynamics of infectious diseases. Current estimates of time-varying epidemiological parameters often face problems such as lagging observations, averaging inference, and improper quantification of uncertainties. To address these problems, we propose a Bayesian data assimilation framework for time-varying parameter estimation. Specifically, this framework is applied to estimate the instantaneous reproduction number R t during emerging epidemics, resulting in the state-of-the-art ‘DARt’ system. With DARt, time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and R t the drawback of averaging is overcome by instantaneously updating upon new observations and developing a model selection mechanism that captures abrupt changes the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt and demonstrate its power in describing the transmission dynamics of COVID-19. The proposed approach provides a promising solution for making accurate and timely estimation for transmission dynamics based on reported data.
Publisher: Elsevier BV
Date: 2017
Publisher: Frontiers Media SA
Date: 31-03-2017
Publisher: Elsevier BV
Date: 12-2019
Publisher: Cambridge University Press (CUP)
Date: 2020
DOI: 10.1017/S0140525X20000011
Abstract: The target article “Thinking Through Other Minds” (TTOM) offered an account of the distinctively human capacity to acquire cultural knowledge, norms, and practices. To this end, we leveraged recent ideas from theoretical neurobiology to understand the human mind in social and cultural contexts. Our aim was both synthetic – building an integrative model adequate to account for key features of cultural learning and adaptation and prescriptive – showing how the tools developed to explain brain dynamics can be applied to the emergence of social and cultural ecologies of mind. In this reply to commentators, we address key issues, including: (1) refining the concept of culture to show how TTOM and the free-energy principle (FEP) can capture essential elements of human adaptation and functioning (2) addressing cognition as an embodied, enactive, affective process involving cultural affordances (3) clarifying the significance of the FEP formalism related to entropy minimization, Bayesian inference, Markov blankets, and enactivist views (4) developing empirical tests and applications of the TTOM model (5) incorporating cultural ersity and context at the level of intra-cultural variation, in idual differences, and the transition to digital niches and (6) considering some implications for psychiatry. The commentators’ critiques and suggestions point to useful refinements and applications of the model. In ongoing collaborations, we are exploring how to augment the theory with affective valence, take into account in idual differences and historicity, and apply the model to specific domains including epistemic bias.
Publisher: Oxford University Press (OUP)
Date: 08-2011
Publisher: Elsevier BV
Date: 10-2009
Publisher: Springer Science and Business Media LLC
Date: 20-07-2022
DOI: 10.1038/S41598-022-16799-8
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.
Publisher: Society for Neuroscience
Date: 02-04-2014
DOI: 10.1523/JNEUROSCI.3765-13.2014
Abstract: Detecting the location of salient sounds in the environment rests on the brain's ability to use differences in sounds arriving at both ears. Functional neuroimaging studies in humans indicate that the left and right auditory hemispaces are coded asymmetrically, with a rightward attentional bias that reflects spatial attention in vision. Neuropsychological observations in patients with spatial neglect have led to the formulation of two competing models: the orientation bias and right-hemisphere dominance models. The orientation bias model posits a symmetrical mapping between one side of the sensorium and the contralateral hemisphere, with mutual inhibition of the ipsilateral hemisphere. The right-hemisphere dominance model introduces a functional asymmetry in the brain's coding of space: the left hemisphere represents the right side, whereas the right hemisphere represents both sides of the sensorium. We used Dynamic Causal Modeling of effective connectivity and Bayesian model comparison to adjudicate between these alternative network architectures, based on human electroencephalographic data acquired during an auditory location oddball paradigm. Our results support a hemispheric asymmetry in a frontoparietal network that conforms to the right-hemisphere dominance model. We show that, within this frontoparietal network, forward connectivity increases selectively in the hemisphere contralateral to the side of sensory stimulation. We interpret this finding in light of hierarchical predictive coding as a selective increase in attentional gain, which is mediated by feedforward connections that carry precision-weighted prediction errors during perceptual inference. This finding supports the disconnection hypothesis of unilateral neglect and has implications for theories of its etiology.
Publisher: Oxford University Press (OUP)
Date: 17-11-2017
Publisher: Cold Spring Harbor Laboratory
Date: 02-03-2018
DOI: 10.1101/273565
Abstract: Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most research applying spectral DCM has focused on group-averaged connectivity within large-scale intrinsic brain networks however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, behavioural, and physical states. Determining the sources of fluctuations in effective connectivity may yield greater understanding of brain processes and inform clinical applications about potential confounds. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We further investigated how standard procedures for data processing and signal extraction affect this consistency. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our s le consisted of 20 subjects with 653 resting state fMRI sessions in total. These data allowed to quantify the robustness of connectivity estimates for each subject, and to draw conclusions beyond specific data features. We found that subjects contributing to all datasets showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and reliability of connectivity for the majority of subjects. Bayesian model reduction increased reliability (within-subjects) and stability (between-subjects) of connectivity patterns.
Publisher: Wiley
Date: 11-1993
DOI: 10.1113/JPHYSIOL.1993.SP019913
Abstract: 1. The primary objective of this study was to examine in man, how induced changes in global cerebral blood flow (gCBF) affected a regional cerebral blood flow (rCBF) increase resulting from a neural activation task (opening of eyes). A secondary objective was to quantify how such induced changes in gCBF were distributed between representative regions of either predominantly grey matter or white matter. 2. Positron emission tomography with intravenous infusion of H2(15)O, was used to measure gCBF in six normal males. Concomitant measures of rCBF were obtained in three different regions of interest (ROI): a representative area of predominantly grey matter, a representative area of predominantly white matter and an area of visual cortex. 3. Cerebral blood flow was altered by establishing steady-state changes in PCO2 at a near constant ventilation of approximately 30 l min-1. The mean PET,CO2 (+/- S.D.) levels (mmHg) that resulted were: low, 21.8 +/- 1.8 normal, 39.8 +/- 1.0, and high, 54.8 +/- 1.2. The normal and high levels were obtained by adding appropriate amounts of CO2 to the inspirate. The corresponding mean gCBF levels across all six subjects with eyes closed were: low, 24.2 +/- 4.6 normal, 37.2 +/- 3.9 and high, 66.8 +/- 7.6 ml min-1 dl-1. 4. Blood flow in grey matter (insular cortex) and white matter (centrum semiovale) at normal levels of PCO2 averaged 56.8 +/- 10.1 and 20.3 +/- 3.4 ml min dl-1 respectively. As PCO2 rose, the increase in rCBF to grey matter was approximately three times greater than that to white matter. 5. An activation state of eyes open in a brightly lit room was compared to a baseline state of eyes closed in a darkened room at the three levels of PCO2 (and hence at three levels of gCBF). Over the whole gCBF range a significant (P = 0.028) effect of increasing rCBF in the visual cortex ROI was found in response to opening the eyes the effect of this activation on rCBF was not significantly dependent (P = 0.34) on the PCO2 (and hence gCBF) level. The effect of the activation on the rCBF was apparently 'additive' to the rise of rCBF associated with PCO2-related gCBF increase. 6. The results confirm the need to normalize for changes in gCBF during studies of rCBF in response to an activation protocol. They also provide support for the use of an 'additive' model to achieve such normalization provided that other cortical areas behave in a similar manner to that of the visual cortex.
Publisher: Elsevier BV
Date: 03-2017
DOI: 10.1016/J.TICS.2017.01.005
Abstract: Major depression is a debilitating condition characterised by erse neurocognitive and behavioural deficits. Nevertheless, our species-typical capacity for depressed mood implies that it serves an adaptive function. Here we apply an interdisciplinary theory of brain function to explain depressed mood and its clinical manifestations. Combining insights from the free-energy principle (FEP) with evolutionary theorising in psychology, we argue that depression reflects an adaptive response to perceived threats of aversive social outcomes (e.g., exclusion) that minimises the likelihood of surprising interpersonal exchanges (i.e., those with unpredictable outcomes). We suggest that psychopathology typically arises from ineffectual attempts to alleviate interpersonal difficulties and/or hyper-reactive neurobiological responses to social stress (i.e., uncertainty), which often stems from early experience that social uncertainty is difficult to resolve.
Publisher: Springer Science and Business Media LLC
Date: 28-01-2021
DOI: 10.1007/S11229-020-02986-5
Abstract: When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist : that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed—a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference—behavioral modeling frameworks that descend, but are distinct, from OMCT—and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference.
Publisher: Center for Open Science
Date: 09-07-2020
Abstract: The ubiquity and importance of narratives in human adaptation has been recognized by many scholars. Research has identified several functions of narratives that are conducive to in iduals’ well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the social and cognitive functions of narratives in terms of the framework of active inference. Active inference depicts the fundamental tendency of living organisms to adapt by creating, updating, and maintaining inferences about their environment. We review the literature on the functions of narratives in identity, event segmentation, episodic memory, future projection, storytelling practices, and enculturation. We then re-cast these functions of narratives in terms of active inference, outlining a parsimonious model that can guide future developments in narrative theory, research, and clinical applications.
Publisher: F1000 Research Ltd
Date: 15-02-2021
DOI: 10.12688/WELLCOMEOPENRES.16004.2
Abstract: By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For ex le, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
Publisher: Elsevier BV
Date: 11-2018
Publisher: F1000 Research Ltd
Date: 17-06-2020
DOI: 10.12688/WELLCOMEOPENRES.16004.1
Abstract: By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For ex le, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
Publisher: Elsevier BV
Date: 11-2014
Publisher: Frontiers Media SA
Date: 20-07-2021
DOI: 10.3389/FPSYG.2021.585493
Abstract: This paper proposes a formal reconstruction of the script construct by leveraging the active inference framework, a behavioral modeling framework that casts action, perception, emotions, and attention as processes of (Bayesian or variational) inference. We propose a first principles account of the script construct that integrates its different uses in the behavioral and social sciences. We begin by reviewing the recent literature that uses the script construct. We then examine the main mathematical and computational features of active inference. Finally, we leverage the resources of active inference to offer a formal model of scripts. Our integrative model accounts for the dual nature of scripts (as internal, psychological schema used by agents to make sense of event types and as constitutive behavioral categories that make up the social order) and also for the stronger and weaker conceptions of the construct (which do and do not relate to explicit action sequences, respectively).
Publisher: Frontiers Media SA
Date: 2012
Publisher: Cold Spring Harbor Laboratory
Date: 10-05-2019
DOI: 10.1101/634063
Abstract: The influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on in idual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies however, in idual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler ergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.
Publisher: Frontiers Media SA
Date: 2012
Publisher: Public Library of Science (PLoS)
Date: 29-08-2008
Publisher: Center for Open Science
Date: 23-02-2021
Abstract: This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. We call this approach computational phenomenology because it applies methods originally developed in computational modelling to phenomenology. The first section presents a brief review of the project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project, and situates our project with respect to these projects. The third section reviews the generative modelling framework. The following section presents our new approach to neurophenomenology based on generative modelling. We then discuss how this application of generative modelling differs from previous attempts to use it to explain consciousness. In summary, generative modelling allows us to construct a computational model of the inferential or interpretive process that best explain this or that kind of lived experience.
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 10-2019
Publisher: Frontiers Media SA
Date: 22-12-2015
Publisher: F1000 Research Ltd
Date: 07-08-2020
DOI: 10.12688/WELLCOMEOPENRES.15881.2
Abstract: This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic for ex le, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
Publisher: F1000 Research Ltd
Date: 07-05-2020
DOI: 10.12688/WELLCOMEOPENRES.15881.1
Abstract: This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations—to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic for ex le, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
Publisher: Frontiers Media SA
Date: 25-03-2020
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 10-2018
Publisher: Center for Open Science
Date: 17-10-2023
Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 02-1993
Abstract: We have previously shown bilateral activation of motor pathways and the recruitment of additional motor areas in studies of groups of patients with recovery from motor stroke. We have now developed a new positron emission tomographic technique to measure the changes in regional cerebral blood flow elicited during a motor task in in idual patients, relative to the cerebral activation found in normal subjects. The patterns of cerebral activation in each of 8 in idual patients with capsular lesions of the pyramidal tract and complete recovery from hemiplegia are described by comparison with the pattern found in a representative s le of 10 normal subjects. We found a large ventral extension of the hand field of the contralateral (sensori)motor cortex in all patients with lesions of the posterior limb of the internal capsule. Greater activation than in normal subjects was found in variable combinations of the supplementary motor areas, the insula, the frontal operculum, and the parietal cortex. Structures belonging to motor pathways ipsilateral to the recovered limb were also more activated in the patients than in normal subjects. However, additional activation of the ipsilateral (sensori)motor cortex was only found in the 4 patients who exhibited associated movements of the unaffected hand when the recovered hand performed the motor task. We conclude that recovery from motor stroke due to striatocapsular damage is associated with in idually different patterns of functional reorganization of the brain. These patterns are dependent on the site of the subcortical lesion and the somatotopic organization of the pyramidal tract, both of which may determine the precise potential for recovery of limb function following this type of brain injury.
Publisher: Elsevier BV
Date: 09-2011
Publisher: MDPI AG
Date: 21-06-2023
DOI: 10.3390/E25070964
Abstract: This paper introduces a variational formulation of natural selection, paying special attention to the nature of ‘things’ and the way that different ‘kinds’ of ‘things’ are in iduated from—and influence—each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain—and are constrained by—fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (Bayesian model) selection. The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations of distinct natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses—and identify points of contact with related mathematical formulations of evolution.
Publisher: American Society for Pharmacology & Experimental Therapeutics (ASPET)
Date: 30-09-2022
Publisher: Elsevier BV
Date: 03-2020
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.NEUROIMAGE.2017.09.061
Abstract: Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.
Publisher: American Physiological Society
Date: 05-2009
Abstract: This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.
Publisher: Oxford University Press (OUP)
Date: 07-07-2007
Abstract: To identify and categorize complex stimuli such as familiar objects or speech, the human brain integrates information that is abstracted at multiple levels from its sensory inputs. Using cross-modal priming for spoken words and sounds, this functional magnetic resonance imaging study identified 3 distinct classes of visuoauditory incongruency effects: visuoauditory incongruency effects were selective for 1) spoken words in the left superior temporal sulcus (STS), 2) environmental sounds in the left angular gyrus (AG), and 3) both words and sounds in the lateral and medial prefrontal cortices (IFS/mPFC). From a cognitive perspective, these incongruency effects suggest that prior visual information influences the neural processes underlying speech and sound recognition at multiple levels, with the STS being involved in phonological, AG in semantic, and mPFC/IFS in higher conceptual processing. In terms of neural mechanisms, effective connectivity analyses (dynamic causal modeling) suggest that these incongruency effects may emerge via greater bottom-up effects from early auditory regions to intermediate multisensory integration areas (i.e., STS and AG). This is consistent with a predictive coding perspective on hierarchical Bayesian inference in the cortex where the domain of the prediction error (phonological vs. semantic) determines its regional expression (middle temporal gyrus/STS vs. AG/intraparietal sulcus).
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.NEURON.2022.09.001
Abstract: Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence.
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 10-2020
End Date: 10-2023
Amount: $490,877.00
Funder: Australian Research Council
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