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Frontiers in Computational Neuroscience

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https://www.readbyqxmd.com/read/30210326/gender-dependent-changes-in-time-production-following-quadrato-motor-training-in-dyslexic-and-normal-readers
#1
Tal Dotan Ben-Soussan, Joseph Glicksohn
Time estimation is an important component of the ability to organize and plan sequences of actions as well as cognitive functions, both of which are known to be altered in dyslexia. While attention deficits are accompanied by short Time Productions (TPs), expert meditators have been reported to produce longer durations, and this seems to be related to their increased attentional resources. In the current study, we examined the effects of a month of Quadrato Motor Training (QMT), which is a structured sensorimotor training program that involves sequencing of motor responses based on verbal commands, on TP using a pre-post design...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30210325/eeg-microstate-sequences-from-different-clustering-algorithms-are-information-theoretically-invariant
#2
Frederic von Wegner, Paul Knaut, Helmut Laufs
We analyse statistical and information-theoretical properties of EEG microstate sequences, as seen through the lens of five different clustering algorithms. Microstate sequences are computed for n = 20 resting state EEG recordings during wakeful rest. The input for all clustering algorithms is the set of EEG topographic maps obtained at local maxima of the spatial variance. This data set is processed by two classical microstate clustering algorithms (1) atomize and agglomerate hierarchical clustering (AAHC) and (2) a modified K-means algorithm, as well as by (3) K-medoids, (4) principal component analysis (PCA) and (5) fast independent component analysis (Fast-ICA)...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30190674/a-comprehensive-review-of-magnetoencephalography-meg-studies-for-brain-functionality-in-healthy-aging-and-alzheimer-s-disease-ad
#3
REVIEW
Pravat K Mandal, Anwesha Banerjee, Manjari Tripathi, Ankita Sharma
Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30186130/alterations-of-muscle-synergies-during-voluntary-arm-reaching-movement-in-subacute-stroke-survivors-at-different-levels-of-impairment
#4
Bingyu Pan, Yingfei Sun, Bin Xie, Zhipei Huang, Jiankang Wu, Jiateng Hou, Yijun Liu, Zhen Huang, Zhiqiang Zhang
Motor system uses muscle synergies as a modular organization to simplify the control of movements. Motor cortical impairments, such as stroke and spinal cord injuries, disrupt the orchestration of the muscle synergies and result in abnormal movements. In this paper, the alterations of muscle synergies in subacute stroke survivors were examined during the voluntary reaching movement. We collected electromyographic (EMG) data from 35 stroke survivors, ranging from Brunnstrom Stage III to VI, and 25 age-matched control subjects...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30177878/task-evoked-negative-bold-response-in-the-default-mode-network-does-not-alter-its-functional-connectivity
#5
Qolamreza R Razlighi
While functional connectivity networks are often extracted from resting-state fMRI scans, they have been shown to be active during task performance as well. However, the effect of an in-scanner task on functional connectivity networks is not completely understood. While there is evidence that task-evoked positive BOLD response can alter functional connectivity networks, particularly in the primary sensorimotor cortices, the effect of task-evoked negative BOLD response on the functional connectivity of the Default mode network (DMN) is somewhat ambiguous...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30154709/effect-of-stimulus-contrast-and-visual-attention-on-spike-gamma-phase-relationship-in-macaque-primary-visual-cortex
#6
Aritra Das, Supratim Ray
Brain signals often show rhythmic activity in the so-called gamma range (30-80 Hz), whose magnitude and center frequency are modulated by properties of the visual stimulus such as size and contrast, as well as by cognitive processes such as attention. How gamma rhythm can potentially influence cortical processing remains unclear; previous studies have proposed a scheme called phase coding, in which the intensity of the incoming stimulus is coded in the position of the spike relative to the rhythm. Using chronically implanted microelectrode arrays in the primary visual cortex (area V1) of macaques engaged in an attention task while presenting stimuli of varying contrasts, we tested whether the phase of the gamma rhythm relative to spikes varied as a function of stimulus contrast and attentional state...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30154708/beta-rhythm-oscillations-and-synchronization-transition-in-network-models-of-izhikevich-neurons-effect-of-topology-and-synaptic-type
#7
Mahsa Khoshkhou, Afshin Montakhab
Despite their significant functional roles, beta-band oscillations are least understood. Synchronization in neuronal networks have attracted much attention in recent years with the main focus on transition type. Whether one obtains explosive transition or a continuous transition is an important feature of the neuronal network which can depend on network structure as well as synaptic types. In this study we consider the effect of synaptic interaction (electrical and chemical) as well as structural connectivity on synchronization transition in network models of Izhikevich neurons which spike regularly with beta rhythms...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30131688/information-based-principle-induces-small-world-topology-and-self-organized-criticality-in-a-large-scale-brain-network
#8
Kosuke Takagi
The information processing in the large scale network of the human brain is related to its cognitive functions. Due to requirements for adaptation to changing environments under biological constraints, these processes in the brain can be hypothesized to be optimized. The principles based on the information optimization are expected to play a central role in affecting the dynamics and topological structure of the brain network. Recent studies on the functional connectivity between brain regions, referred to as the functional connectome, reveal characteristics of their networks, such as self-organized criticality of brain dynamics and small-world topology...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30123120/multiple-frequency-bands-analysis-of-large-scale-intrinsic-brain-networks-and-its-application-in-schizotypal-personality-disorder
#9
Shouliang Qi, Qingjun Gao, Jing Shen, Yueyang Teng, Xuan Xie, Yueji Sun, Jianlin Wu
The human brain is a complex system composed by several large scale intrinsic networks with distinct functions. The low frequency oscillation (LFO) signal of blood oxygen level dependent (BOLD), measured through resting-state fMRI, reflects the spontaneous neural activity of these networks. We propose to characterize these networks by applying the multiple frequency bands analysis (MFBA) to the LFO time courses (TCs) resulted from the group independent component analysis (ICA). Specifically, seven networks, including the default model network (DMN), dorsal attention network (DAN), control executive network (CEN), salience network, sensorimotor network, visual network and limbic network, are identified...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30123119/recording-neural-activity-based-on-surface-plasmon-resonance-by-optical-fibers-a-computational-analysis
#10
Mitra Abedini, Tahereh Tekieh, Pezhman Sasanpour
An all optical, non-destructive method for monitoring neural activity has been proposed and its performance in detection has been analyzed computationally. The proposed method is based on excitation of Surface Plasmon Resonance (SPR) through the structure of optical fibers. The sensor structure consists of a multimode optical fiber where, the cladding of fiber has been removed and thin film of gold structure has been deposited on the surface. Impinging the laser light with appropriate wavelength inside the fiber and based on the total internal reflection, the evanescent wave will excite surface plasmons in the gold thin film...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30116187/formation-of-opioid-induced-memory-and-its-prevention-a-computational-study
#11
Mehdi Borjkhani, Fariba Bahrami, Mahyar Janahmadi
There are several experimental studies which suggest opioids consumption forms pathological memories in different brain regions. For example it has been empirically demonstrated that the theta rhythm which appears during chronic opioid consumption is correlated with the addiction memory formation. In this paper, we present a minimal computational model that shows how opioids can change firing patterns of the neurons during acute and chronic opioid consumption and also during withdrawal periods. The model consists of a pre- and post-synaptic neuronal circuits and the astrocyte that monitors the synapses...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30108494/factorized-computation-what-the-neocortex-can-tell-us-about-the-future-of-computing
#12
Peter U Diehl, Julien Martel, Jakob Buhmann, Matthew Cook
No abstract text is available yet for this article.
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30100870/a-glutamatergic-spine-model-to-enable-multi-scale-modeling-of-nonlinear-calcium-dynamics
#13
Eric Hu, Adam Mergenthal, Clayton S Bingham, Dong Song, Jean-Marie Bouteiller, Theodore W Berger
In synapses, calcium is required for modulating synaptic transmission, plasticity, synaptogenesis, and synaptic pruning. The regulation of calcium dynamics within neurons involves cellular mechanisms such as synaptically activated channels and pumps, calcium buffers, and calcium sequestrating organelles. Many experimental studies tend to focus on only one or a small number of these mechanisms, as technical limitations make it difficult to observe all features at once. Computational modeling enables incorporation of many of these properties together, allowing for more complete and integrated studies...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30087604/perceptual-dominance-in-brief-presentations-of-mixed-images-human-perception-vs-deep-neural-networks
#14
Liron Z Gruber, Aia Haruvi, Ronen Basri, Michal Irani
Visual perception involves continuously choosing the most prominent inputs while suppressing others. Neuroscientists induce visual competitions in various ways to study why and how the brain makes choices of what to perceive. Recently deep neural networks (DNNs) have been used as models of the ventral stream of the visual system, due to similarities in both accuracy and hierarchy of feature representation. In this study we created non-dynamic visual competitions for humans by briefly presenting mixtures of two images...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30072887/modern-machine-learning-as-a-benchmark-for-fitting-neural-responses
#15
Ari S Benjamin, Hugo L Fernandes, Tucker Tomlinson, Pavan Ramkumar, Chris VerSteeg, Raeed H Chowdhury, Lee E Miller, Konrad P Kording
Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30072886/epileptic-seizure-prediction-based-on-permutation-entropy
#16
Yanli Yang, Mengni Zhou, Yan Niu, Conggai Li, Rui Cao, Bin Wang, Pengfei Yan, Yao Ma, Jie Xiang
Epilepsy is a chronic non-communicable disorder of the brain that affects individuals of all ages. It is caused by a sudden abnormal discharge of brain neurons leading to temporary dysfunction. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. However, the potential that permutation entropy(PE) can be applied in human epilepsy prediction from intracranial electroencephalogram (iEEG) recordings remains unclear...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30072885/sensorimotor-synchronization-with-auditory-and-visual-modalities-behavioral-and-neural-differences
#17
REVIEW
Daniel C Comstock, Michael J Hove, Ramesh Balasubramaniam
It has long been known that the auditory system is better suited to guide temporally precise behaviors like sensorimotor synchronization (SMS) than the visual system. Although this phenomenon has been studied for many years, the underlying neural and computational mechanisms remain unclear. Growing consensus suggests the existence of multiple, interacting, context-dependent systems, and that reduced precision in visuo-motor timing might be due to the way experimental tasks have been conceived. Indeed, the appropriateness of the stimulus for a given task greatly influences timing performance...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30061819/multi-timescale-memory-dynamics-extend-task-repertoire-in-a-reinforcement-learning-network-with-attention-gated-memory
#18
Marco Martinolli, Wulfram Gerstner, Aditya Gilra
The interplay of reinforcement learning and memory is at the core of several recent neural network models, such as the Attention-Gated MEmory Tagging (AuGMEnT) model. While successful at various animal learning tasks, we find that the AuGMEnT network is unable to cope with some hierarchical tasks, where higher-level stimuli have to be maintained over a long time, while lower-level stimuli need to be remembered and forgotten over a shorter timescale. To overcome this limitation, we introduce a hybrid AuGMEnT, with leaky (or short-timescale) and non-leaky (or long-timescale) memory units, that allows the exchange of low-level information while maintaining high-level one...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30042669/an-oscillatory-neural-autoencoder-based-on-frequency-modulation-and-multiplexing
#19
Karthik Soman, Vignesh Muralidharan, V Srinivasa Chakravarthy
Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena...
2018: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/30042668/criteria-on-balance-stability-and-excitability-in-cortical-networks-for-constraining-computational-models
#20
Andrei Maksimov, Markus Diesmann, Sacha J van Albada
During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits...
2018: Frontiers in Computational Neuroscience
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