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35
Multisector models
 In Handbook of Development Economics, eds., H. Chenery and T.N. Srinivasan
, 1989
"... To the best of my knowledge, this thesis contains no copy or paraphrase of work published by another person, except where duly acknowledged in the text. This thesis contains no material which has been presented for a degree at the University of Sydney or any other university. ..."
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Cited by 87 (10 self)
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To the best of my knowledge, this thesis contains no copy or paraphrase of work published by another person, except where duly acknowledged in the text. This thesis contains no material which has been presented for a degree at the University of Sydney or any other university.
A neural mass model for MEG/EEG: coupling and neuronal dynamics
 NeuroImage
, 2003
"... Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequencyspecific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their func ..."
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Cited by 81 (21 self)
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Although MEG/EEG signals are highly variable, systematic changes in distinct frequency bands are commonly encountered. These frequencyspecific changes represent robust neural correlates of cognitive or perceptual processes (for example, alpha rhythms emerge on closing the eyes). However, their functional significance remains a matter of debate. Some of the mechanisms that generate these signals are known at the cellular level and rest on a balance of excitatory and inhibitory interactions within and between populations of neurons. The kinetics of the ensuing population dynamics determine the frequency of oscillations. In this work we extended the classical nonlinear lumpedparameter model of alpha rhythms, initially developed by Lopes da Silva and colleagues [Kybernetik 15 (1974) 27], to generate more complex dynamics. We show that the whole spectrum of MEG/EEG signals can be reproduced within the oscillatory regime of this model by simply changing the population kinetics. We used the model to examine the influence of coupling strength and propagation delay on the rhythms generated by coupled cortical areas. The main findings were that (1) coupling induces phaselocked activity, with a phase shift of 0 or &pi; when the coupling is bidirectional, and (2) both coupling and propagation delay are critical determinants of the MEG/EEG spectrum. In forthcoming articles, we will use this model to (1) estimate how neuronal interactions are expressed in MEG/EEG oscillations and establish the construct validity of various indices of nonlinear coupling, and (2) generate eventrelated transients to derive physiologically informed basis functions for statistical modelling of average evoked responses.
Waves and bumps in neuronal networks with axodendritic synaptic interactions
 Physica D
, 2003
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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 instabilities in scalar neural field equations with spacedependent delays
 PHYSICA D
, 2007
"... In this paper we consider a class of scalar integral equations with a form of spacedependent delay. These nonlocal models arise naturally when modelling neural tissue with active axons and passive dendrites. Such systems are known to support a dynamic (oscillatory) Turing instability of the homoge ..."
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Cited by 21 (0 self)
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In this paper we consider a class of scalar integral equations with a form of spacedependent delay. These nonlocal models arise naturally when modelling neural tissue with active axons and passive dendrites. Such systems are known to support a dynamic (oscillatory) Turing instability of the homogeneous steady state. In this paper we develop a weakly nonlinear analysis of the travelling and standing waves that form beyond the point of instability. The appropriate amplitude equations are found to be the coupled meanfield Ginzburg–Landau equations describing a Turing–Hopf bifurcation with modulation group velocity of O(1). Importantly we are able to obtain the coefficients of terms in the amplitude equations in terms of integral transforms of the spatiotemporal kernels defining the neural field equation of interest. Indeed our results cover not only models with axonal or dendritic delays but those which are described by a more general distribution of delayed spatiotemporal interactions. We illustrate the predictive power of this form of analysis with comparison against direct numerical simulations, paying particular attention to the competition between standing and travelling waves and the onset of Benjamin–Feir instabilities.
Spatiotemporal Forward Solution of the EEG and MEG Using Network Modeling
 IEEE Transactions on Medical Imaging
, 2002
"... Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dyna ..."
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Cited by 20 (4 self)
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Dynamic systems have proven to be well suited to describe a broad spectrum of human coordination behavior such synchronization with auditory stimuli. Simultaneous measurements of the spatiotemporal dynamics of electroencephalographic (EEG) and magnetoencephalographic (MEG) data reveals that the dynamics of the brain signals is highly ordered and also accessible by dynamic systems theory. However, models of EEG and MEG dynamics have typically been formulated only in terms of phenomenological modeling such as fixedcurrent dipoles or spatial EEG and MEG patterns. In this paper, it is our goal to connect three levels of organization, that is the level of coordination behavior, the level of patterns observed in the EEG and MEG and the level of neuronal network dynamics. To do so, we develop a methodological framework, which defines the spatiotemporal dynamics of neural ensembles, the neural field, on a sphere in three dimensions. Using magnetic resonance imaging we map the neural field dynamics from the sphere onto the folded cortical surface of a hemisphere. The neural field represents the current flow perpendicular to the cortex and, thus, allows for the calculation of the electric potentials on the surface of the skull and the magnetic fields outside the skull to be measured by EEG and MEG, respectively. For demonstration of the dynamics, we present the propagation of activation at a single cortical site resulting from a transient input. Finally, a mapping between finger movement profile and EEG/MEG patterns is obtained using Volterra integrals.
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
Mechanisms of Cortical Electrical Activity and Emergence of Gamma Rhythm
 Journal of Theoretical Biology
, 2000
"... this paper, unless otherwise noted Parameter Value Meaning 4120 Dendritic synapses from pyramidals 800 Dendritic synapses from interneurons 80 Dendritic synapses from subcortex s### ## "s### !5.9#10## V s IPSP size due to # # when < ### "< ### !0. ..."
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Cited by 15 (7 self)
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this paper, unless otherwise noted Parameter Value Meaning 4120 Dendritic synapses from pyramidals 800 Dendritic synapses from interneurons 80 Dendritic synapses from subcortex s### ## "s### !5.9#10## V s IPSP size due to # # when < ### "< ### !0.060 V Potential at which s### ##### are estimated !0.060 V Rest potential (# "0) 0 V Reversal potential for AMPA channels !0.070 V Reversal potential for GABA channels 68 s## Decay rate in pyramidals of EPSPs 47 s## Decay rate in pyramidals of IPSPs 176 s## Decay rate in interneurons of EPSPs 82 s## Decay rate in interneurons of IPSPs 500 s## Rate of rise of PSPs in pyramidals 500 s## Rate of rise of PSPs in interneurons # 200 s## Cuto! frequency for feedback 100 s## Maximal pyramidal "ring rates 200 s## Maximal interneuron "ring rates 0.005 V Sigmoid width parameter !0.052 V Firing threshold 10 s## Firing rate of subcortical neurons 80 s## Excitatory spatial damping rate 10# s## Inhibitory spatial damping rate 2.3. PARAMETERS Here we discuss each of the values chosen for the model's parameters, in the order in which they appear in Table 1. Here, as elsewhere, the subscript q"e, i refers to the two classes of neurons and the subscript p"e, i, s to the three sources of dendritic inputs
Independent EEG sources are dipolar
 PLoS ONE
, 2012
"... Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and nonbrain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thi ..."
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Cited by 14 (2 self)
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Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and nonbrain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity ’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least wellperforming algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonlyused decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volumeconducted projections of partiallysynchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons,
Population dynamics: Variance and the sigmoid activation function
 NEUROIMAGE 42 (2008) 147–157
, 2008
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