Sensitivity analysis for the EEG forward problem.

Abstract:

:Sensitivity analysis can provide useful information when one is interested in identifying the parameter θ of a system since it measures the variations of the output u when θ changes. In the literature two different sensitivity functions are frequently used: the traditional sensitivity functions (TSF) and the generalized sensitivity functions (GSF). They can help to determine the time instants where the output of a dynamical system has more information about the value of its parameters in order to carry on an estimation process. Both functions were considered by some authors who compared their results for different dynamical systems (see Banks and Bihari, 2001; Kappel and Batzel, 2006; Banks et al., 2008). In this work we apply the TSF and the GSF to analyze the sensitivity of the 3D Poisson-type equation with interfaces of the forward problem of electroencephalography. In a simple model where we consider the head as a volume consisting of nested homogeneous sets, we establish the differential equations that correspond to TSF with respect to the value of the conductivity of the different tissues and deduce the corresponding integral equations. Afterward we compute the GSF for the same model. We perform some numerical experiments for both types of sensitivity functions and compare the results.

journal_name

Front Comput Neurosci

authors

Troparevsky MI,Rubio D,Saintier N

doi

10.3389/fncom.2010.00138

subject

Has Abstract

pub_date

2010-09-30 00:00:00

pages

138

issn

1662-5188

journal_volume

4

pub_type

杂志文章
  • Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation With Clustered Initial Points.

    abstract::Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analys...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2019.00014

    authors: Mohebian MR,Marateb HR,Karimimehr S,Mañanas MA,Kranjec J,Holobar A

    更新日期:2019-04-02 00:00:00

  • Synaptic encoding of temporal contiguity.

    abstract::Often we need to perform tasks in an environment that changes stochastically. In these situations it is important to learn the statistics of sequences of events in order to predict the future and the outcome of our actions. The statistical description of many of these sequences can be reduced to the set of probabiliti...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2013.00032

    authors: Ostojic S,Fusi S

    更新日期:2013-04-12 00:00:00

  • Learning modular policies for robotics.

    abstract::A promising idea for scaling robot learning to more complex tasks is to use elemental behaviors as building blocks to compose more complex behavior. Ideally, such building blocks are used in combination with a learning algorithm that is able to learn to select, adapt, sequence and co-activate the building blocks. Whil...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章,评审

    doi:10.3389/fncom.2014.00062

    authors: Neumann G,Daniel C,Paraschos A,Kupcsik A,Peters J

    更新日期:2014-06-11 00:00:00

  • A stimulus-dependent spike threshold is an optimal neural coder.

    abstract::A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory sy...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2015.00061

    authors: Jones DL,Johnson EC,Ratnam R

    更新日期:2015-06-02 00:00:00

  • Information diversity in structure and dynamics of simulated neuronal networks.

    abstract::Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neurona...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2011.00026

    authors: Mäki-Marttunen T,Aćimović J,Nykter M,Kesseli J,Ruohonen K,Yli-Harja O,Linne ML

    更新日期:2011-06-01 00:00:00

  • Analysis of Nociceptive Information Encoded in the Temporal Discharge Patterns of Cutaneous C-Fibers.

    abstract::The generation of pain signals from primary afferent neurons is explained by a labeled-line code. However, this notion cannot apply in a simple way to cutaneous C-fibers, which carry signals from a variety of receptors that respond to various stimuli including agonist chemicals. To represent the discharge patterns of ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00118

    authors: Cho K,Jang JH,Kim SP,Lee SH,Chung SC,Kim IY,Jang DP,Jung SJ

    更新日期:2016-11-18 00:00:00

  • Transition Dynamics of a Dentate Gyrus-CA3 Neuronal Network during Temporal Lobe Epilepsy.

    abstract::In temporal lobe epilepsy (TLE), the variation of chemical receptor expression underlies the basis of neural network activity shifts, resulting in neuronal hyperexcitability and epileptiform discharges. However, dynamical mechanisms involved in the transitions of TLE are not fully understood, because of the neuronal d...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2017.00061

    authors: Zhang L,Fan D,Wang Q

    更新日期:2017-07-11 00:00:00

  • Structural Plasticity Denoises Responses and Improves Learning Speed.

    abstract::Despite an abundance of computational models for learning of synaptic weights, there has been relatively little research on structural plasticity, i.e., the creation and elimination of synapses. Especially, it is not clear how structural plasticity works in concert with spike-timing-dependent plasticity (STDP) and wha...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00093

    authors: Spiess R,George R,Cook M,Diehl PU

    更新日期:2016-09-08 00:00:00

  • Emergence of Relaxation Oscillations in Neurons Interacting With Non-stationary Ambient GABA.

    abstract::Dynamics of a homogeneous neural population interacting with active extracellular medium were considered. The corresponding mathematical model was tuned specifically to describe the behavior of interneurons with tonic GABA conductance under the action of non-stationary ambient GABA. The feedback provided by the GABA m...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2018.00019

    authors: Adamchik DA,Matrosov VV,Kazantsev VB

    更新日期:2018-04-05 00:00:00

  • Architectural constraints are a major factor reducing path integration accuracy in the rat head direction cell system.

    abstract::Head direction cells fire to signal the direction in which an animal's head is pointing. They are able to track head direction using only internally-derived information (path integration)In this simulation study we investigate the factors that affect path integration accuracy. Specifically, two major limiting factors ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2015.00010

    authors: Page HJ,Walters D,Stringer SM

    更新日期:2015-02-06 00:00:00

  • Spectral Entropy Based Neuronal Network Synchronization Analysis Based on Microelectrode Array Measurements.

    abstract::Synchrony and asynchrony are essential aspects of the functioning of interconnected neuronal cells and networks. New information on neuronal synchronization can be expected to aid in understanding these systems. Synchronization provides insight in the functional connectivity and the spatial distribution of the informa...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00112

    authors: Kapucu FE,Välkki I,Mikkonen JE,Leone C,Lenk K,Tanskanen JM,Hyttinen JA

    更新日期:2016-10-18 00:00:00

  • Nine criteria for a measure of scientific output.

    abstract::Scientific research produces new knowledge, technologies, and clinical treatments that can lead to enormous returns. Often, the path from basic research to new paradigms and direct impact on society takes time. Precise quantification of scientific output in the short-term is not an easy task but is critical for evalua...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2011.00048

    authors: Kreiman G,Maunsell JH

    更新日期:2011-11-10 00:00:00

  • A Role for Electrotonic Coupling Between Cortical Pyramidal Cells.

    abstract::Many brain regions communicate information through synchronized network activity. Electrical coupling among the dendrites of interneurons in the cortex has been implicated in forming and sustaining such activity in the cortex. Evidence for the existence of electrical coupling among cortical pyramidal cells, however, h...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2019.00033

    authors: Crodelle J,Zhou D,Kovačič G,Cai D

    更新日期:2019-05-28 00:00:00

  • Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis.

    abstract::Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention mod...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00064

    authors: Fu H,Niu Z,Zhang C,Ma J,Chen J

    更新日期:2016-07-14 00:00:00

  • Dopamine-signaled reward predictions generated by competitive excitation and inhibition in a spiking neural network model.

    abstract::Dopaminergic neurons in the mammalian substantia nigra display characteristic phasic responses to stimuli which reliably predict the receipt of primary rewards. These responses have been suggested to encode reward prediction-errors similar to those used in reinforcement learning. Here, we propose a model of dopaminerg...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2011.00021

    authors: Chorley P,Seth AK

    更新日期:2011-05-18 00:00:00

  • Topological View of Flows Inside the BOLD Spontaneous Activity of the Human Brain.

    abstract::Spatio-temporal brain activities with variable delay detectable in resting-state functional magnetic resonance imaging (rs-fMRI) give rise to highly reproducible structures, termed cortical lag threads, that propagate from one brain region to another. Using a computational topology of data approach, we found that pers...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00034

    authors: Don APH,Peters JF,Ramanna S,Tozzi A

    更新日期:2020-04-22 00:00:00

  • Empirical Evaluation of Voluntarily Activatable Muscle Synergies.

    abstract::The muscle synergy hypothesis assumes that individual muscle synergies are independent of each other and voluntarily controllable. However, this assumption has not been empirically tested. This study tested if human subjects can voluntarily activate individual muscle synergies extracted by non-negative matrix factoriz...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2017.00082

    authors: Togo S,Imamizu H

    更新日期:2017-09-06 00:00:00

  • Bursting Neurons in the Hippocampal Formation Encode Features of LFP Rhythms.

    abstract::Burst spike patterns are common in regions of the hippocampal formation such as the subiculum and medial entorhinal cortex (MEC). Neurons in these areas are immersed in extracellular electrical potential fluctuations often recorded as the local field potential (LFP). LFP rhythms within different frequency bands are li...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00133

    authors: Constantinou M,Gonzalo Cogno S,Elijah DH,Kropff E,Gigg J,Samengo I,Montemurro MA

    更新日期:2016-12-26 00:00:00

  • A Spiking Neural Model of HT3D for Corner Detection.

    abstract::Obtaining good quality image features is of remarkable importance for most computer vision tasks. It has been demonstrated that the first layers of the human visual cortex are devoted to feature detection. The need for these features has made line, segment, and corner detection one of the most studied topics in comput...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2018.00037

    authors: Bachiller-Burgos P,Manso LJ,Bustos P

    更新日期:2018-06-01 00:00:00

  • The role of pulvinar in the transmission of information in the visual hierarchy.

    abstract::VISUAL RECEPTIVE FIELD (RF) ATTRIBUTES IN VISUAL CORTEX OF PRIMATES HAVE BEEN EXPLAINED MAINLY FROM CORTICAL CONNECTIONS: visual RFs progress from simple to complex through cortico-cortical pathways from lower to higher levels in the visual hierarchy. This feedforward flow of information is paired with top-down proces...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2012.00029

    authors: Cortes N,van Vreeswijk C

    更新日期:2012-05-28 00:00:00

  • Unsupervised Domain Adaptation With Optimal Transport in Multi-Site Segmentation of Multiple Sclerosis Lesions From MRI Data.

    abstract::Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation,...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00019

    authors: Ackaouy A,Courty N,Vallée E,Commowick O,Barillot C,Galassi F

    更新日期:2020-03-09 00:00:00

  • Multimodal Neural Network for Rapid Serial Visual Presentation Brain Computer Interface.

    abstract::Brain computer interfaces allow users to preform various tasks using only the electrical activity of the brain. BCI applications often present the user a set of stimuli and record the corresponding electrical response. The BCI algorithm will then have to decode the acquired brain response and perform the desired task....

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00130

    authors: Manor R,Mishali L,Geva AB

    更新日期:2016-12-20 00:00:00

  • Learning Generative State Space Models for Active Inference.

    abstract::In this paper we investigate the active inference framework as a means to enable autonomous behavior in artificial agents. Active inference is a theoretical framework underpinning the way organisms act and observe in the real world. In active inference, agents act in order to minimize their so called free energy, or p...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.574372

    authors: Çatal O,Wauthier S,De Boom C,Verbelen T,Dhoedt B

    更新日期:2020-11-16 00:00:00

  • An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

    abstract::For large-scale network simulations, it is often desirable to have computationally tractable, yet in a defined sense still physiologically valid neuron models. In particular, these models should be able to reproduce physiological measurements, ideally in a predictive sense, and under different input regimes in which n...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2012.00062

    authors: Hertäg L,Hass J,Golovko T,Durstewitz D

    更新日期:2012-09-06 00:00:00

  • Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

    abstract::Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00062

    authors: Daemi M,Harris LR,Crawford JD

    更新日期:2016-06-23 00:00:00

  • Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency.

    abstract::This paper proposed a new method to determine the neuronal tuning curves for maximum information efficiency by computing the optimum firing rate distribution. Firstly, we proposed a general definition for the information efficiency, which is relevant to mutual information and neuronal energy consumption. The energy co...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2017.00010

    authors: Han F,Wang Z,Fan H

    更新日期:2017-02-21 00:00:00

  • Anti-kindling Induced by Two-Stage Coordinated Reset Stimulation with Weak Onset Intensity.

    abstract::Abnormal neuronal synchrony plays an important role in a number of brain diseases. To specifically counteract abnormal neuronal synchrony by desynchronization, Coordinated Reset (CR) stimulation, a spatiotemporally patterned stimulation technique, was designed with computational means. In neuronal networks with spike ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00044

    authors: Zeitler M,Tass PA

    更新日期:2016-05-17 00:00:00

  • Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis.

    abstract::Neuron classification is an important component in analyzing network structure and quantifying the effect of neuron topology on signal processing. Current quantification and classification approaches rely on morphology projection onto lower-dimensional spaces. In this paper a 3D visualization and quantification tool i...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2020.00042

    authors: Grein S,Qi G,Queisser G

    更新日期:2020-06-26 00:00:00

  • Disinhibition-Induced Delayed Onset of Epileptic Spike-Wave Discharges in a Five Variable Model of Cortex and Thalamus.

    abstract::Based on a modified neural field network model composed of cortex and thalamus, we here propose a computational framework to investigate the onset control of absence seizure, which is characterized by the spike-wave discharges. Firstly, we briefly demonstrate the existence of various transition types in Taylor's model...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2016.00028

    authors: Liu S,Wang Q,Fan D

    更新日期:2016-04-05 00:00:00

  • A high-capacity model for one shot association learning in the brain.

    abstract::We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like ...

    journal_title:Frontiers in computational neuroscience

    pub_type: 杂志文章

    doi:10.3389/fncom.2014.00140

    authors: Einarsson H,Lengler J,Steger A

    更新日期:2014-11-07 00:00:00