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Karl Friston

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https://www.readbyqxmd.com/read/27913429/heritability-of-the-effective-connectivity-in-the-resting-state-default-mode-network
#1
Junhai Xu, Xuntao Yin, Haitao Ge, Yan Han, Zengchang Pang, Baolin Liu, Shuwei Liu, Karl Friston
The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity...
November 23, 2016: Cerebral Cortex
https://www.readbyqxmd.com/read/27871729/an-integrative-tinnitus-model-based-on-sensory-precision
#2
REVIEW
William Sedley, Karl J Friston, Phillip E Gander, Sukhbinder Kumar, Timothy D Griffiths
Tinnitus is a common disorder that often complicates hearing loss. Its mechanisms are incompletely understood. Current theories proposing pathophysiology from the ear to the cortex cannot individually - or collectively - explain the range of experimental evidence available. We propose a new framework, based on predictive coding, in which spontaneous activity in the subcortical auditory pathway constitutes a 'tinnitus precursor' which is normally ignored as imprecise evidence against the prevailing percept of 'silence'...
December 2016: Trends in Neurosciences
https://www.readbyqxmd.com/read/27870614/active-inference-a-process-theory
#3
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo
This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence-or minimizing variational free energy-we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena...
November 21, 2016: Neural Computation
https://www.readbyqxmd.com/read/27718099/bayesian-modelling-of-induced-responses-and-neuronal-rhythms
#4
Dimitris A Pinotsis, Roman Loonis, Andre M Bastos, Earl K Miller, Karl J Friston
Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing-and explaining-oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses-and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity...
October 7, 2016: Brain Topography
https://www.readbyqxmd.com/read/27683898/neural-signatures-of-value-comparison-in-human-cingulate-cortex-during-decisions-requiring-an-effort-reward-trade-off
#5
Miriam C Klein-Flügge, Steven W Kennerley, Karl Friston, Sven Bestmann
UNLABELLED: Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs...
September 28, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/27683002/active-inference-and-robot-control-a-case-study
#6
Léo Pio-Lopez, Ange Nizard, Karl Friston, Giovanni Pezzulo
Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e...
September 2016: Journal of the Royal Society, Interface
https://www.readbyqxmd.com/read/27639356/dynamic-causal-modelling-of-seizure-activity-in-a-rat-model
#7
Margarita Papadopoulou, Gerald Cooray, Richard Rosch, Rosalyn Moran, Daniele Marinazzo, Karl Friston
This paper presents a physiological account of seizure activity and its evolution over time using a rat model of induced epilepsy. We analyse spectral activity recorded in the hippocampi of three rats who received kainic acid injections in the right hippocampus. We use dynamic causal modelling of seizure activity and Bayesian model reduction to identify the key synaptic and connectivity parameters that underlie seizure onset. Using recent advances in hierarchical modelling (parametric empirical Bayes), we characterise seizure onset in terms of slow fluctuations in synaptic excitability of specific neuronal populations...
September 14, 2016: NeuroImage
https://www.readbyqxmd.com/read/27593199/intersubject-variability-and-induced-gamma-in-the-visual-cortex-dcm-with-empirical-bayes-and-neural-fields
#8
Dimitris A Pinotsis, Gavin Perry, Vladimir Litvak, Krish D Singh, Karl J Friston
This article describes the first application of a generic (empirical) Bayesian analysis of between-subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non-invasive (MEG) data can be used to characterize subject-specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation-inhibition balance in a canonical cortical circuit...
December 2016: Human Brain Mapping
https://www.readbyqxmd.com/read/27535770/neural-processes-mediating-contextual-influences-on-human-choice-behaviour
#9
Francesco Rigoli, Karl J Friston, Raymond J Dolan
Contextual influences on choice are ubiquitous in ecological settings. Current evidence suggests that subjective values are normalized with respect to the distribution of potentially available rewards. However, how this context-sensitivity is realised in the brain remains unknown. To address this, here we examine functional magnetic resonance imaging (fMRI) data during performance of a gambling task where blocks comprise values drawn from one of two different, but partially overlapping, reward distributions or contexts...
August 18, 2016: Nature Communications
https://www.readbyqxmd.com/read/27517087/computational-phenotyping-in-psychiatry-a-worked-example
#10
Philipp Schwartenbeck, Karl Friston
Computational psychiatry is a rapidly emerging field that uses model-based quantities to infer the behavioral and neuronal abnormalities that underlie psychopathology. If successful, this approach promises key insights into (pathological) brain function as well as a more mechanistic and quantitative approach to psychiatric nosology-structuring therapeutic interventions and predicting response and relapse. The basic procedure in computational psychiatry is to build a computational model that formalizes a behavioral or neuronal process...
July 2016: ENeuro
https://www.readbyqxmd.com/read/27506256/bridging-the-gap-dynamic-causal-modeling-and-granger-causality-analysis-of-resting-state-functional-magnetic-resonance-imaging
#11
Sahil Bajaj, Bhim M Adhikari, Karl J Friston, Mukesh Dhamala
Granger causality (GC) and dynamic causal modeling (DCM) are the two key approaches used to determine the directed interactions among brain areas. Recent discussions have provided a constructive account of the merits and demerits. GC, on one side, considers dependencies among measured responses, whereas DCM, on the other, models how neuronal activity in one brain area causes dynamics in another. In this study, our objective was to establish construct validity between GC and DCM in the context of resting state functional magnetic resonance imaging (fMRI)...
September 16, 2016: Brain Connectivity
https://www.readbyqxmd.com/read/27471478/free-energy-and-virtual-reality-in-neuroscience-and-psychoanalysis-a-complexity-theory-of-dreaming-and-mental-disorder
#12
Jim Hopkins
The main concepts of the free energy (FE) neuroscience developed by Karl Friston and colleagues parallel those of Freud's Project for a Scientific Psychology. In Hobson et al. (2014) these include an innate virtual reality generator that produces the fictive prior beliefs that Freud described as the primary process. This enables Friston's account to encompass a unified treatment-a complexity theory-of the role of virtual reality in both dreaming and mental disorder. In both accounts the brain operates to minimize FE aroused by sensory impingements-including interoceptive impingements that report compliance with biological imperatives-and constructs a representation/model of the causes of impingement that enables this minimization...
2016: Frontiers in Psychology
https://www.readbyqxmd.com/read/27450778/the-dysconnection-hypothesis-2016
#13
REVIEW
Karl Friston, Harriet R Brown, Jakob Siemerkus, Klaas E Stephan
Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience...
October 2016: Schizophrenia Research
https://www.readbyqxmd.com/read/27391681/active-inference-and-learning-in-the-cerebellum
#14
Karl Friston, Ivan Herreros
This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can account for spontaneous blinking, startle responses, and (delay or trace) conditioning. We then establish the face validity of the model using simulated responses to unconditioned and conditioned stimuli to reproduce the sorts of behavior that are observed empirically. The scheme's anatomical validity is then addressed by associating variables in the predictive coding scheme with nuclei and neuronal populations to match the (extrinsic and intrinsic) connectivity of the cerebellar (eyeblink conditioning) system...
September 2016: Neural Computation
https://www.readbyqxmd.com/read/27378899/scene-construction-visual-foraging-and-active-inference
#15
M Berk Mirza, Rick A Adams, Christoph D Mathys, Karl J Friston
This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes...
2016: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/27375276/active-inference-and-learning
#16
REVIEW
Karl Friston, Thomas FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, John O'Doherty, Giovanni Pezzulo
This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits...
September 2016: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/27346080/the-bayesian-savant
#17
Karl Friston
No abstract text is available yet for this article.
July 15, 2016: Biological Psychiatry
https://www.readbyqxmd.com/read/27328323/a-bayesian-model-of-context-sensitive-value-attribution
#18
Francesco Rigoli, Karl J Friston, Cristina Martinelli, Mirjana Selaković, Sukhwinder S Shergill, Raymond J Dolan
Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels...
June 22, 2016: ELife
https://www.readbyqxmd.com/read/27327516/the-100-most-cited-articles-in-neuroimaging-a-bibliometric-analysis
#19
Hye Jeong Kim, Dae Young Yoon, Eun Soo Kim, Kwanseop Lee, Jong Seok Bae, Ju-Hun Lee
The purpose of our study was to identify and characterize the 100 most-cited articles in neuroimaging. Based on the database of Journal Citation Reports, we selected 669 journals that were considered as potential outlets for neuroimaging articles. The Web of Science search tools were used to identify the 100 most-cited articles relevant to neuroimaging within the selected journals. The following information was recorded for each article: publication year, journal, category and impact factor of journal, number of citations, number of annual citations, authorship, department, institution, country, article type, imaging technique used, and topic...
June 18, 2016: NeuroImage
https://www.readbyqxmd.com/read/27317193/active-inference-epistemic-value-and-vicarious-trial-and-error
#20
Giovanni Pezzulo, Emilio Cartoni, Francesco Rigoli, Léo Pio-Lopez, Karl Friston
Balancing habitual and deliberate forms of choice entails a comparison of their respective merits-the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments with simulations that reproduce rodent spatial decisions in T-mazes. In this context, deliberation has been associated with vicarious trial and error (VTE) behavior (i...
July 2016: Learning & Memory
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