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A theory of cortical responses
, 2005
"... This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. ..."
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Cited by 260 (30 self)
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This article concerns the nature of evoked brain responses and the principles underlying their generation. We start with the premise that the sensory brain has evolved to represent or infer the causes of changes in its sensory inputs. The problem of inference is well formulated in statistical terms. The statistical fundaments of inference may therefore afford important constraints on neuronal implementation. By formulating the original ideas of Helmholtz on perception, in terms of modernday statistical theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. It turns out that the problems of inferring the causes of sensory input (perceptual inference) and learning the relationship between input and cause (perceptual learning) can be resolved using exactly the same principle. Specifically, both inference and learning rest on minimizing the brain’s free energy, as defined in statistical physics. Furthermore, inference and learning can proceed in a biologically plausible fashion. Cortical responses can be seen as the brain’s attempt to minimize the free energy induced by a stimulus and thereby encode the most likely cause of that stimulus. Similarly, learning emerges from changes in synaptic efficacy that minimize the free energy, averaged over all stimuli encountered. The underlying scheme rests on empirical Bayes and hierarchical models
Dynamic causal modelling of evoked responses
 in EEG and MEG. NeuroImage
"... EEG/MEG with lead field parameterization ..."
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Dynamic causal modelling of evoked responses in eeg/meg with leadfield parameterization. Under revision
, 2005
"... Neuronally plausible, generative or forward models are essential for understanding how eventrelated fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling eventrelated responses measured with EEG or MEG. This approach uses a biologically informed mo ..."
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Cited by 48 (18 self)
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Neuronally plausible, generative or forward models are essential for understanding how eventrelated fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling eventrelated responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers longrange connections among welldefined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neuralmass model that embodied bottomup, topdown and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in corticocortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Categoryselectivity, as indexed by the faceAbbreviations: DCM, dynamic causal Model(ing); EEG, electroencephalography; ERF, eventrelated field; ERP, eventrelated potential;
Modelling eventrelated responses in the brain
 NeuroImage
, 2005
"... The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magnetoencephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural ..."
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Cited by 38 (9 self)
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The aim of this work was to investigate the mechanisms that shape evoked electroencephalographic (EEG) and magnetoencephalographic (MEG) responses. We used a neuronally plausible model to characterise the dependency of response components on the models parameters. This generative model was a neural mass model of hierarchically arranged areas using three kinds of interarea connections (forward, backward and lateral). We investigated how responses, at each level of a cortical hierarchy, depended on the strength of connections or coupling. Our strategy was to systematically add connections and examine the responses of each successive architecture. We did this in the context of deterministic responses and then with stochastic spontaneous activity. Our aim was to show, in a simple way, how eventrelated dynamics depend on extrinsic connectivity. To emphasise the importance of nonlinear interactions, we tried to disambiguate the components of eventrelated potentials (ERPs) or eventrelated fields
Dynamic causal modelling of evoked potentials: a reproducibility study
 NeuroImage
, 2007
"... Dynamic causal modelling (DCM) has been applied recently to eventrelated responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the ..."
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Cited by 33 (5 self)
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Dynamic causal modelling (DCM) has been applied recently to eventrelated responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (Fmodel), backward connections (Bmodel) or both (FBmodel). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FBmodel was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FBmodel supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/ MEG data and its ability to model ERPs in a mechanistic fashion. © 2007 Elsevier Inc. All rights reserved.
Stochastic models of neuronal dynamics
, 2005
"... Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In ot ..."
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Cited by 16 (5 self)
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Cortical activity is the product of interactions among neuronal populations. Macroscopic electrophysiological phenomena are generated by these interactions. In principle, the mechanisms of these interactions afford constraints on biologically plausible models of electrophysiological responses. In other words, the macroscopic features of cortical activity can be modelled in terms of the microscopic behaviour of neurons. An evoked response potential (ERP) is the mean electrical potential measured from an electrode on the scalp, in response to some event. The purpose of this paper is to outline a population density approach to modelling ERPs. We propose a biologically plausible model of neuronal activity that enables the estimation of physiologically meaningful parameters from electrophysiological data. The model encompasses four basic characteristics of neuronal activity and organization: (i) neurons are dynamic units, (ii) driven by stochastic forces, (iii) organized into populations with similar biophysical properties and response characteristics and (iv) multiple populations interact to form functional networks. This leads to a formulation of population dynamics in terms of the Fokker–Planck equation. The solution of this equation is the temporal evolution of a probability density over statespace, representing the distribution of an ensemble of trajectories. Each trajectory corresponds to the changing state of a
Bilinear dynamical systems
 Philosophical Transactions of the Royal Society of London Series B: Biological Sciences
, 2005
"... In this paper, we propose the use of bilinear dynamical systems (BDS)s for modelbased deconvolution of fMRI timeseries. The importance of this work lies in being able to deconvolve haemodynamic timeseries, in an informed way, to disclose the underlying neuronal activity. Being able to estimate ne ..."
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Cited by 10 (0 self)
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In this paper, we propose the use of bilinear dynamical systems (BDS)s for modelbased deconvolution of fMRI timeseries. The importance of this work lies in being able to deconvolve haemodynamic timeseries, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectationmaximization (EM) algorithm for parameter estimation, in which fMRI timeseries are deconvolved in an Estep and model parameters are updated in an MStep. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.
Mechanisms of evoked and induced responses in MEG/EEG
 NeuroImage
"... Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study eventrelated potentials/fields, or in the time – frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced ..."
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Cited by 9 (2 self)
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Cortical responses, recorded by electroencephalography and magnetoencephalography, can be characterized in the time domain, to study eventrelated potentials/fields, or in the time – frequency domain, to study oscillatory activity. In the literature, there is a common conception that evoked, induced, and ongoing oscillations reflect different neuronal processes and mechanisms. In this work, we consider the relationship between the mechanisms generating neuronal transients and how they are expressed in terms of evoked and induced power. This relationship is addressed using a neuronally realistic model of interacting neuronal subpopulations. Neuronal transients were generated by changing neuronal input (a dynamic mechanism) or by perturbing the systems coupling parameters (a structural mechanism) to produce induced responses. By applying conventional time – frequency analyses, we show that, in contradistinction to common conceptions, induced and evoked oscillations are perhaps more related than previously reported. Specifically, structural mechanisms normally associated with induced responses can be expressed in evoked power. Conversely, dynamic mechanisms posited for evoked responses can induce responses, if there is variation in neuronal input. We conclude, it may be better to consider evoked responses as the results of mixed dynamic and structural effects. We introduce adjusted power to complement induced power. Adjusted power is unaffected by trialtotrial variations in input and can be attributed to structural perturbations without ambiguity. D 2006 Elsevier Inc. All rights reserved.
Population dynamics: Variance and the sigmoid activation function
 NEUROIMAGE 42 (2008) 147–157
, 2008
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Queuing Network Modeling of a RealTime Psychophysiological Index of Mental Workload—P300 in EventRelated Potential (ERP)
"... Abstract—Modeling and predicting of mental workload are among the most important issues in studying human performance in complex systems. Ample research has shown that the amplitude of the P300 component of eventrelated potential (ERP) is an effective realtime index of mental workload, yet no comp ..."
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Cited by 8 (7 self)
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Abstract—Modeling and predicting of mental workload are among the most important issues in studying human performance in complex systems. Ample research has shown that the amplitude of the P300 component of eventrelated potential (ERP) is an effective realtime index of mental workload, yet no computational model exists that is able to account for the change of P300 amplitude in dualtask conditions compared with that in singletask situations. We describe the successful extension and application of a new computational modeling approach in modeling P300 and mental workload—a queuing network approach based on the queuing network theory of human performance and neuroscience discoveries. Based on the neurophysiological mechanisms underlying the generation of P300, the current modeling approach accurately accounts for P300 amplitude both in temporal and intensity dimensions. This approach not only has a basis in its biological plausibility but also has the ability to model and predict workload in real time and can be applied to other applied domains. Further model developments in simulating other dimensions of mental workload and its potential applications in adaptive system design are discussed. Index Terms—Computational modeling, dual task, eventrelated potential (ERP), mental workload, P300, queuing network. I.