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Journal of Computational Neuroscience

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https://read.qxmd.com/read/30737596/outgrowing-seizures-in-childhood-absence-epilepsy-time-delays-and-bistability
#1
Yue Liu, John Milton, Sue Ann Campbell
We formulate a conductance-based model for a 3-neuron motif associated with Childhood Absence Epilepsy (CAE). The motif consists of neurons from the thalamic relay (TC) and reticular nuclei (RT) and the cortex (CT). We focus on a genetic defect common to the mouse homolog of CAE which is associated with loss of GABAA receptors on the TC neuron, and the fact that myelination of axons as children age can increase the conduction velocity between neurons. We show the combination of low GABAA mediated inhibition of TC neurons and the long corticothalamic loop delay gives rise to a variety of complex dynamics in the motif, including bistability...
February 9, 2019: Journal of Computational Neuroscience
https://read.qxmd.com/read/30737595/emerging-techniques-in-statistical-analysis-of-neural-data
#2
EDITORIAL
Sridevi V Sarma
No abstract text is available yet for this article.
February 9, 2019: Journal of Computational Neuroscience
https://read.qxmd.com/read/30661144/network-structure-and-input-integration-in-competing-firing-rate-models-for-decision-making
#3
Victor J Barranca, Han Huang, Genji Kawakita
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characterizing decision making in the face of a large set of alternatives remains challenging. We explore this issue by formulating a scalable mechanistic network model for decision making and analyzing the dynamics evoked given various potential network structures. In the case of a fully-connected network, we provide an analytical characterization of the model fixed points and their stability with respect to winner-take-all behavior for fair tasks...
January 19, 2019: Journal of Computational Neuroscience
https://read.qxmd.com/read/30547292/dendritic-sodium-spikes-endow-neurons-with-inverse-firing-rate-response-to-correlated-synaptic-activity
#4
Tomasz Górski, Romain Veltz, Mathieu Galtier, Hélissande Fragnaud, Jennifer S Goldman, Bartosz Teleńczuk, Alain Destexhe
Many neurons possess dendrites enriched with sodium channels and are capable of generating action potentials. However, the role of dendritic sodium spikes remain unclear. Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods...
December 13, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30511274/an-exploratory-data-analysis-method-for-identifying-brain-regions-and-frequencies-of-interest-from-large-scale-neural-recordings
#5
Macauley S Breault, Pierre Sacré, Jorge González-Martínez, John T Gale, Sridevi V Sarma
High-resolution whole brain recordings have the potential to uncover unknown functionality but also present the challenge of how to find such associations between brain and behavior when presented with a large number of regions and spectral frequencies. In this paper, we propose an exploratory data analysis method that sorts through a massive quantity of multivariate neural recordings to quickly extract a subset of brain regions and frequencies that encode behavior. This approach combines existing tools and exploits low-rank approximation of matrices without a priori selection of regions and frequency bands for analysis...
December 4, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30488148/motor-imagery-and-mental-fatigue-inter-relationship-and-eeg-based-estimation
#6
Upasana Talukdar, Shyamanta M Hazarika, John Q Gan
Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue...
November 29, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30443813/modeling-the-interactions-between-stimulation-and-physiologically-induced-aps-in-a-mammalian-nerve-fiber-dependence-on-frequency-and-fiber-diameter
#7
Vijay Sadashivaiah, Pierre Sacré, Yun Guan, William S Anderson, Sridevi V Sarma
Electrical stimulation of nerve fibers is used as a therapeutic tool to treat neurophysiological disorders. Despite efforts to model the effects of stimulation, its underlying mechanisms remain unclear. Current mechanistic models quantify the effects that the electrical field produces near the fiber but do not capture interactions between action potentials (APs) initiated by stimulus and APs initiated by underlying physiological activity. In this study, we aim to quantify the effects of stimulation frequency and fiber diameter on AP interactions involving collisions and loss of excitability...
November 15, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30382451/a-detailed-anatomical-and-mathematical-model-of-the-hippocampal-formation-for-the-generation-of-sharp-wave-ripples-and-theta-nested-gamma-oscillations
#8
Amélie Aussel, Laure Buhry, Louise Tyvaert, Radu Ranta
The mechanisms underlying the broad variety of oscillatory rhythms measured in the hippocampus during the sleep-wake cycle are not yet fully understood. In this article, we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this is able to reproduce both the theta-nested gamma oscillations that are seen in awake brains and the sharp-wave ripple complexes measured during slow-wave sleep...
October 31, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30377880/replicability-or-reproducibility-on-the-replication-crisis-in-computational-neuroscience-and-sharing-only-relevant-detail
#9
Marcin Miłkowski, Witold M Hensel, Mateusz Hohol
Replicability and reproducibility of computational models has been somewhat understudied by "the replication movement." In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons...
October 31, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30317462/an-intracerebral-exploration-of-functional-connectivity-during-word-production
#10
Amandine Grappe, Sridevi V Sarma, Pierre Sacré, Jorge González-Martínez, Catherine Liégeois-Chauvel, F-Xavier Alario
Language is mediated by pathways connecting distant brain regions that have diverse functional roles. For word production, the network includes a ventral pathway, connecting temporal and inferior frontal regions, and a dorsal pathway, connecting parietal and frontal regions. Despite the importance of word production for scientific and clinical purposes, the functional connectivity underlying this task has received relatively limited attention, and mostly from techniques limited in either spatial or temporal resolution...
October 13, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30315514/predicting-state-transitions-in-brain-dynamics-through-spectral-difference-of-phase-space-graphs
#11
Patrick Luckett, Elena Pavelescu, Todd McDonald, Lee Hively, Juan Ochoa
Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy...
October 12, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30294750/linear-nonlinear-time-warp-poisson-models-of-neural-activity
#12
Patrick N Lawlor, Matthew G Perich, Lee E Miller, Konrad P Kording
Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability...
October 8, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30306384/a-numerical-simulation-of-neural-fields-on-curved-geometries
#13
R Martin, D J Chappell, N Chuzhanova, J J Crofts
Despite the highly convoluted nature of the human brain, neural field models typically treat the cortex as a planar two-dimensional sheet of ne;urons. Here, we present an approach for solving neural field equations on surfaces more akin to the cortical geometries typically obtained from neuroimaging data. Our approach involves solving the integral form of the partial integro-differential equation directly using collocation techniques alongside efficient numerical procedures for determining geodesic distances between neural units...
October 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30298220/a-common-goodness-of-fit-framework-for-neural-population-models-using-marked-point-process-time-rescaling
#14
Long Tao, Karoline E Weber, Kensuke Arai, Uri T Eden
A critical component of any statistical modeling procedure is the ability to assess the goodness-of-fit between a model and observed data. For spike train models of individual neurons, many goodness-of-fit measures rely on the time-rescaling theorem and assess model quality using rescaled spike times. Recently, there has been increasing interest in statistical models that describe the simultaneous spiking activity of neuron populations, either in a single brain region or across brain regions. Classically, such models have used spike sorted data to describe relationships between the identified neurons, but more recently clusterless modeling methods have been used to describe population activity using a single model...
October 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30191352/adjusted-regularization-of-cortical-covariance
#15
Giuseppe Vinci, Valérie Ventura, Matthew A Smith, Robert E Kass
It is now common to record dozens to hundreds or more neurons simultaneously, and to ask how the network activity changes across experimental conditions. A natural framework for addressing questions of functional connectivity is to apply Gaussian graphical modeling to neural data, where each edge in the graph corresponds to a non-zero partial correlation between neurons. Because the number of possible edges is large, one strategy for estimating the graph has been to apply methods that aim to identify large sparse effects using an [Formula: see text] penalty...
October 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30146661/firing-rate-models-for-neurons-with-a-broad-repertoire-of-spiking-behaviors
#16
Thomas Heiberg, Birgit Kriener, Tom Tetzlaff, Gaute T Einevoll, Hans E Plesser
Capturing the response behavior of spiking neuron models with rate-based models facilitates the investigation of neuronal networks using powerful methods for rate-based network dynamics. To this end, we investigate the responses of two widely used neuron model types, the Izhikevich and augmented multi-adapative threshold (AMAT) models, to a range of spiking inputs ranging from step responses to natural spike data. We find (i) that linear-nonlinear firing rate models fitted to test data can be used to describe the firing-rate responses of AMAT and Izhikevich spiking neuron models in many cases; (ii) that firing-rate responses are generally too complex to be captured by first-order low-pass filters but require bandpass filters instead; (iii) that linear-nonlinear models capture the response of AMAT models better than of Izhikevich models; (iv) that the wide range of response types evoked by current-injection experiments collapses to few response types when neurons are driven by stationary or sinusoidally modulated Poisson input; and (v) that AMAT and Izhikevich models show different responses to spike input despite identical responses to current injections...
October 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30062615/a-method-for-decomposing-multivariate-time-series-into-a-causal-hierarchy-within-specific-frequency-bands
#17
Jonathan D Drover, Nicholas D Schiff
We propose a method - Frequency extracted hierarchical decomposition (FEHD) - for studying multivariate time series that identifies linear combinations of its components that possess a causally hierarchical structure - the method orders the components so that those at the "top" of the hierarchy drive those below. The method shares many of the features of the "hierarchical decomposition" method of Repucci et al. (Annals of Biomedical Engineering, 29, 1135-1149, 2001) but makes a crucial advance - the proposed method is capable of determining this causal hierarchy over arbitrarily specified frequency bands...
October 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30218225/stability-of-point-process-spiking-neuron-models
#18
Yu Chen, Qi Xin, Valérie Ventura, Robert E Kass
Point process regression models, based on generalized linear model (GLM) technology, have been widely used for spike train analysis, but a recent paper by Gerhard et al. described a kind of instability, in which fitted models can generate simulated spike trains with explosive firing rates. We analyze the problem by extending the methods of Gerhard et al. First, we improve their instability diagnostic and extend it to a wider class of models. Next, we point out some common situations in which instability can be traced to model lack of fit...
September 15, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/30206733/ensembles-of-change-point-detectors-implications-for-real-time-bmi-applications
#19
Zhengdong Xiao, Sile Hu, Qiaosheng Zhang, Xiang Tian, Yaowu Chen, Jing Wang, Zhe Chen
Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods...
September 12, 2018: Journal of Computational Neuroscience
https://read.qxmd.com/read/29946922/a-model-of-motor-and-sensory-axon-activation-in-the-median-nerve-using-surface-electrical-stimulation
#20
Jessica L Gaines, Kathleen E Finn, Julia P Slopsema, Lane A Heyboer, Katharine H Polasek
Surface electrical stimulation has the potential to be a powerful and non-invasive treatment for a variety of medical conditions but currently it is difficult to obtain consistent evoked responses. A viable clinical system must be able to adapt to variations in individuals to produce repeatable results. To more fully study the effect of these variations without performing exhaustive testing on human subjects, a system of computer models was created to predict motor and sensory axon activation in the median nerve due to surface electrical stimulation at the elbow...
August 2018: Journal of Computational Neuroscience
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