Results 1  10
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16
Population dynamics: Variance and the sigmoid activation function
 NEUROIMAGE 42 (2008) 147–157
, 2008
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Investigating the functional role of callosal connections with dynamic causal models
 Ann N Y Acad Sci
, 2005
"... ABSTRACT: The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human postmortem experiments are currently complemented by diffusionweighted imaging, which enables noninvasive investigations of callosal connectivity to be conducted. In contrast ..."
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Cited by 7 (1 self)
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ABSTRACT: The anatomy of the corpus callosum has been described in considerable detail. Tracing studies in animals and human postmortem experiments are currently complemented by diffusionweighted imaging, which enables noninvasive investigations of callosal connectivity to be conducted. In contrast to the wealth of anatomical data, little is known about the principles by which interhemispheric integration is mediated by callosal connections. Most importantly, we lack insights into the mechanisms that determine the functional role of callosal connections in a contextdependent fashion. These mechanisms can now be disclosed by models of effective connectivity that explain neuroimaging data from paradigms that manipulate interhemispheric interactions. In this article, we demonstrate that dynamic causal modeling (DCM), in conjunction with Bayesian model selection (BMS), is a powerful approach to disentangling the various factors that determine the functional role of callosal connections. We first review the theoretical foundations of DCM and BMS before demonstrating the application of these techniques to empirical data from a single subject.
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, 2010
"... We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that ne ..."
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We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes freeenergy in a Bayesian fashion. Because freeenergy bounds surprise or the (negative) logevidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speedaccuracy tradeoffs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayesoptimal perception.
BioInfoPhysics models of neuronal signal processes based on theories of electromagnetic fields
 Am. J. Neurosci
"... Abstract: Problem statement: The aphasia is one of human language and action related brain associative diseases. The mechanisms of the diseases and the brain association are still unclear. In this study, we proposed our models of the neuronal signal processes, in a view of BioInforPhysics, to unders ..."
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Abstract: Problem statement: The aphasia is one of human language and action related brain associative diseases. The mechanisms of the diseases and the brain association are still unclear. In this study, we proposed our models of the neuronal signal processes, in a view of BioInforPhysics, to understand the mechanisms. Approach: Our models are based on today’s solidest Electromagnetic Fields (EMF) theoretic fundamentals: Maxwell EMF equations, Poynting theorem and vector, Lorentz law and other well known EMF principles, as well as published biomedical data. Methods cover the signal collections and analysis, correlations and synthesis; the correlations include functions derivatives as well as the functions. Results: (a) The signals have three attributes (or elements): the information, the energies and the matters; (b) the fields intensities are the Information Intensities (II), products of the II are the Information Response Intensities (IRI) of energies expressions, products of the II and the matters (charges) are the IRI of forces expressions; (c) the information can produce the new information; (d) the energies can carry or (and) transmit the information; (e) the matters (charges) can store and produce the information. The EMF information is not conservative in biological fluids because of the charges or the attenuation of the II. Our models in this study are the signals oriented and combine the information, the energies and the matters. Conclusion: Approximately, neurons work like
A mesostatespace model for EEG and MEG
, 2007
"... We present a multiscale generative model for EEG, that entails a minimum number of assumptions about evoked brain responses, namely: (1) bioelectric activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number o ..."
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We present a multiscale generative model for EEG, that entails a minimum number of assumptions about evoked brain responses, namely: (1) bioelectric activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates, (3) mesostates evolve in a temporal structured way and are functionally connected (i.e. influence each other), and (4) the number of mesostates engaged by a cognitive task is small (e.g. between one and a few). A Variational Bayesian learning scheme is described that furnishes the posterior density on the models parameters and its evidence. Since the number of mesosources specifies the model, the model evidence can be used to compare models and find the optimum number of mesosources. In addition to estimating the dynamics at each cortical dipole, the mesostatespace model and its inversion provide a description of brain activity at the level of the mesostates (i.e. in terms of the dynamics of mesosources that are distributed over dipoles). The inclusion of a mesostate level allows one to compute posterior probability maps of each dipole being active (i.e. belonging to an active mesostate). Critically, this model accommodates constraints on the number of mesosources, while retaining the flexibility of distributed source models in explaining data. In short, it bridges the gap between standard distributed and equivalent current dipole models. Furthermore, because it is explicitly spatiotemporal, the model can embed any stochastic dynamical causal model (e.g. a neural mass model) as a Markov process prior on the mesostate dynamics. The approach is evaluated and compared to standard inverse EEG techniques, using synthetic data and real data. The results demonstrate the addedvalue of the mesostatespace model and its variational inversion.
Finite volume and asymptotic methods for stochastic neuron models with correlated inputs
, 2010
"... We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding FokkerPlanck equation with nontrivial boundary conditions resulting from the refractory period and firing threshold. We propose a ..."
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We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding FokkerPlanck equation with nontrivial boundary conditions resulting from the refractory period and firing threshold. We propose a finite volume method that is orders of magnitude faster than the Monte Carlo methods traditionally used to model such systems. The resulting numerical approximations are proved to be accurate, nonnegative and integrate to 1. We also approximate the transient evolution of the system using an Ornstein–Uhlenbeck process, and use the result to examine the properties of the joint output of cell pairs. The results suggests that the joint output of a cell pair is most sensitive to changes in input variance, and less sensitive to changes in input mean and correlation. 1
Reciprocal projections in hierarchically organized evolvable neural circuits affect
, 2013
"... Modular architecture is a hallmark of many brain circuits. In the cerebral cortex, in particular, it has been observed that reciprocal connections are often present between functionally interconnected areas that are hierarchically organized. We investigate the effect of reciprocal connections in a n ..."
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Modular architecture is a hallmark of many brain circuits. In the cerebral cortex, in particular, it has been observed that reciprocal connections are often present between functionally interconnected areas that are hierarchically organized. We investigate the effect of reciprocal connections in a network of modules of simulated spiking neurons. The neural activity is recorded by means of virtual electrodes and EEGlike signals, called electrochipograms (EChG), analyzed by time and frequencydomain methods. A major feature of our approach is the implementation of important bioinspired processes that affect the connectivity within a neural module: synaptogenesis, cell death, spiketimingdependent plasticity and synaptic pruning. These bioinspired processes drive the buildup of autoassociative links within each module, which generate an areal activity, recorded by EChG, that reflect the changes in the corresponding functional connectivity within and between neuronal modules. We found that circuits with intralayer reciprocal projections exhibited enhanced stimuluslocked response. We show evidence that all networks of modules are able to process and maintain patterns of activity associated with the stimulus after its offset. The presence of feedback and horizontal projections was necessary to evoke crosslayer coherence in bursts of γfrequency at regular intervals. These findings bring new insights to the understanding of the relation between the functional organization of neural circuits and the electrophysiological signals generated by large cell assemblies.
An analytic solution of the reentrant Poisson master equation and
"... Population density techniques are statistical methods to describe large populations of spiking neurons. They describe the response of such a population to a stochastic input. These techniques are sometimes defined as the interaction of neuronal dynamics and a Poisson point process. In earlier work I ..."
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Population density techniques are statistical methods to describe large populations of spiking neurons. They describe the response of such a population to a stochastic input. These techniques are sometimes defined as the interaction of neuronal dynamics and a Poisson point process. In earlier work I showed that one can transform away neuronal dynamics, which leaves only the problem of solving the master equation for the Poisson point process. Previously, I used a numerical solution for the master equation. In this work, I will present an analytic solution, which is based on a formal solution by Sirovich (2003). I will show that using this solution for solving the population density equation results in a much faster and manifestly stable algorithm.