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Frontiers in Computational Neuroscience

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https://www.readbyqxmd.com/read/28638335/quantification-of-head-movement-predictability-and-implications-for-suppression-of-vestibular-input-during-locomotion
#1
Paul R MacNeilage, Stefan Glasauer
Achieved motor movement can be estimated using both sensory and motor signals. The value of motor signals for estimating movement should depend critically on the stereotypy or predictability of the resulting actions. As predictability increases, motor signals become more reliable indicators of achieved movement, so weight attributed to sensory signals should decrease accordingly. Here we describe a method to quantify this predictability for head movement during human locomotion by measuring head motion with an inertial measurement unit (IMU), and calculating the variance explained by the mean movement over one stride, i...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28634449/electromyography-data-processing-impacts-muscle-synergies-during-gait-for-unimpaired-children-and-children-with-cerebral-palsy
#2
Benjamin R Shuman, Michael H Schwartz, Katherine M Steele
Muscle synergies calculated from electromyography (EMG) data identify weighted groups of muscles activated together during functional tasks. Research has shown that fewer synergies are required to describe EMG data of individuals with neurologic impairments. When considering potential clinical applications of synergies, understanding how EMG data processing impacts results and clinical interpretation is important. The aim of this study was to evaluate how EMG signal processing impacts synergy outputs during gait...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28620292/dynamic-responses-in-brain-networks-to-social-feedback-a-dual-eeg-acquisition-study-in-adolescent-couples
#3
Ching-Chang Kuo, Thao Ha, Ashley M Ebbert, Don M Tucker, Thomas J Dishion
Adolescence is a sensitive period for the development of romantic relationships. During this period the maturation of frontolimbic networks is particularly important for the capacity to regulate emotional experiences. In previous research, both functional magnetic resonance imaging (fMRI) and dense array electroencephalography (dEEG) measures have suggested that responses in limbic regions are enhanced in adolescents experiencing social rejection. In the present research, we examined social acceptance and rejection from romantic partners as they engaged in a Chatroom Interact Task...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28620291/network-wide-adaptive-burst-detection-depicts-neuronal-activity-with-improved-accuracy
#4
Inkeri A Välkki, Kerstin Lenk, Jarno E Mikkonen, Fikret E Kapucu, Jari A K Hyttinen
Neuronal networks are often characterized by their spiking and bursting statistics. Previously, we introduced an adaptive burst analysis method which enhances the analysis power for neuronal networks with highly varying firing dynamics. The adaptation is based on single channels analyzing each element of a network separately. Such kind of analysis was adequate for the assessment of local behavior, where the analysis focuses on the neuronal activity in the vicinity of a single electrode. However, the assessment of the whole network may be hampered, if parts of the network are analyzed using different rules...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28611619/hammering-does-not-fit-fitts-law
#5
Tadej Petrič, Cole S Simpson, Aleš Ude, Auke J Ijspeert
While movement is essential to human wellbeing, we are still unable to reproduce the deftness and robustness of human movement in automatons or completely restore function to individuals with many types of motor impairment. To better understand how the human nervous system plans and controls movements, neuromechanists employ simple tasks such as upper extremity reaches and isometric force tasks. However, these simple tasks rarely consider impacts and may not capture aspects of motor control that arise from real-world complexity...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28611618/potential-mechanisms-and-functions-of-intermittent-neural-synchronization
#6
Sungwoo Ahn, Leonid L Rubchinsky
Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28611617/prediction-of-the-seizure-suppression-effect-by-electrical-stimulation-via-a-computational-modeling-approach
#7
Sora Ahn, Sumin Jo, Sang Beom Jun, Hyang Woon Lee, Seungjun Lee
In this paper, we identified factors that can affect seizure suppression via electrical stimulation by an integrative study based on experimental and computational approach. Preferentially, we analyzed the characteristics of seizure-like events (SLEs) using our previous in vitro experimental data. The results were analyzed in two groups classified according to the size of the effective region, in which the SLE was able to be completely suppressed by local stimulation. However, no significant differences were found between these two groups in terms of signal features or propagation characteristics (i...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28596729/detection-and-evaluation-of-spatio-temporal-spike-patterns-in-massively-parallel-spike-train-data-with-spade
#8
Pietro Quaglio, Alper Yegenoglu, Emiliano Torre, Dominik M Endres, Sonja Grün
Repeated, precise sequences of spikes are largely considered a signature of activation of cell assemblies. These repeated sequences are commonly known under the name of spatio-temporal patterns (STPs). STPs are hypothesized to play a role in the communication of information in the computational process operated by the cerebral cortex. A variety of statistical methods for the detection of STPs have been developed and applied to electrophysiological recordings, but such methods scale poorly with the current size of available parallel spike train recordings (more than 100 neurons)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28588471/functional-network-connectivity-patterns-between-idiopathic-generalized-epilepsy-with-myoclonic-and-absence-seizures
#9
Qifu Li, Yongmin Chen, Yong Wei, Shengmei Chen, Lin Ma, Zhiyi He, Zhibin Chen
The extensive cerebral cortex and subcortical structures are considered as the major regions related to the generalized epileptiform discharges in idiopathic generalized epilepsy. However, various clinical syndromes and electroencephalogram (EEG) signs exist across generalized seizures, such as the loss of consciousness during absence seizures (AS) and the jerk of limbs during myoclonic seizures (MS). It is presumed that various functional systems affected by discharges lead to the difference in syndromes of these seizures...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28588470/examining-brain-morphometry-associated-with-self-esteem-in-young-adults-using-multilevel-roi-features-based-classification-method
#10
Bo Peng, Jieru Lu, Aditya Saxena, Zhiyong Zhou, Tao Zhang, Suhong Wang, Yakang Dai
Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28579954/linear-parameter-varying-identification-of-dynamic-joint-stiffness-during-time-varying-voluntary-contractions
#11
Mahsa A Golkar, Ehsan Sobhani Tehrani, Robert E Kearney
Dynamic joint stiffness is a dynamic, nonlinear relationship between the position of a joint and the torque acting about it, which can be used to describe the biomechanics of the joint and associated limb(s). This paper models and quantifies changes in ankle dynamic stiffness and its individual elements, intrinsic and reflex stiffness, in healthy human subjects during isometric, time-varying (TV) contractions of the ankle plantarflexor muscles. A subspace, linear parameter varying, parallel-cascade (LPV-PC) algorithm was used to identify the model from measured input position perturbations and output torque data using voluntary torque as the LPV scheduling variable (SV)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28555102/a-model-of-fast-hebbian-spike-latency-normalization
#12
Hafsteinn Einarsson, Marcelo M Gauy, Johannes Lengler, Angelika Steger
Hebbian changes of excitatory synapses are driven by and enhance correlations between pre- and postsynaptic neuronal activations, forming a positive feedback loop that can lead to instability in simulated neural networks. Because Hebbian learning may occur on time scales of seconds to minutes, it is conjectured that some form of fast stabilization of neural firing is necessary to avoid runaway of excitation, but both the theoretical underpinning and the biological implementation for such homeostatic mechanism are to be fully investigated...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28553220/the-influence-of-volume-conduction-on-dtf-estimate-and-the-problem-of-its-mitigation
#13
Maciej Kaminski, Katarzyna J Blinowska
No abstract text is available yet for this article.
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28553219/a-bayesian-reformulation-of-the-extended-drift-diffusion-model-in-perceptual-decision-making
#14
Pouyan R Fard, Hame Park, Andrej Warkentin, Stefan J Kiebel, Sebastian Bitzer
Perceptual decision making can be described as a process of accumulating evidence to a bound which has been formalized within drift-diffusion models (DDMs). Recently, an equivalent Bayesian model has been proposed. In contrast to standard DDMs, this Bayesian model directly links information in the stimulus to the decision process. Here, we extend this Bayesian model further and allow inter-trial variability of two parameters following the extended version of the DDM. We derive parameter distributions for the Bayesian model and show that they lead to predictions that are qualitatively equivalent to those made by the extended drift-diffusion model (eDDM)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28539881/the-influence-of-mexican-hat-recurrent-connectivity-on-noise-correlations-and-stimulus-encoding
#15
Robert Meyer, Josef Ladenbauer, Klaus Obermayer
Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28522969/equilibrium-propagation-bridging-the-gap-between-energy-based-models-and-backpropagation
#16
Benjamin Scellier, Yoshua Bengio
We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28507514/markovian-analysis-of-the-sequential-behavior-of-the-spontaneous-spinal-cord-dorsum-potentials-induced-by-acute-nociceptive-stimulation-in-the-anesthetized-cat
#17
Mario Martin, Javier Béjar, Gennaro Esposito, Diógenes Chávez, Enrique Contreras-Hernández, Silvio Glusman, Ulises Cortés, Pablo Rudomín
In a previous study we developed a Machine Learning procedure for the automatic identification and classification of spontaneous cord dorsum potentials (CDPs). This study further supported the proposal that in the anesthetized cat, the spontaneous CDPs recorded from different lumbar spinal segments are generated by a distributed network of dorsal horn neurons with structured (non-random) patterns of functional connectivity and that these configurations can be changed to other non-random and stable configurations after the noceptive stimulation produced by the intradermic injection of capsaicin in the anesthetized cat...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28491031/control-of-absence-seizures-by-the-thalamic-feed-forward-inhibition
#18
Mingming Chen, Daqing Guo, Yang Xia, Dezhong Yao
As a subtype of idiopathic generalized epilepsies, absence epilepsy is believed to be caused by pathological interactions within the corticothalamic (CT) system. Using a biophysical mean-field model of the CT system, we demonstrate here that the feed-forward inhibition (FFI) in thalamus, i.e., the pathway from the cerebral cortex (Ctx) to the thalamic reticular nucleus (TRN) and then to the specific relay nuclei (SRN) of thalamus that are also directly driven by the Ctx, may participate in controlling absence seizures...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28487645/forearm-flexor-muscles-in-children-with-cerebral-palsy-are-weak-thin-and-stiff
#19
Ferdinand von Walden, Kian Jalaleddini, Björn Evertsson, Johanna Friberg, Francisco J Valero-Cuevas, Eva Pontén
Children with cerebral palsy (CP) often develop reduced passive range of motion with age. The determining factor underlying this process is believed to be progressive development of contracture in skeletal muscle that likely changes the biomechanics of the joints. Consequently, to identify the underlying mechanisms, we modeled the mechanical characteristics of the forearm flexors acting across the wrist joint. We investigated skeletal muscle strength (Grippit®) and passive stiffness and viscosity of the forearm flexors in 15 typically developing (TD) children (10 boys/5 girls, mean age 12 years, range 8-18 yrs) and nine children with CP Nine children (6 boys/3 girls, mean age 11 ± 3 years (yrs), range 7-15 yrs) using the NeuroFlexor® apparatus...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28487644/a-computational-analysis-of-the-function-of-three-inhibitory-cell-types-in-contextual-visual-processing
#20
Jung H Lee, Christof Koch, Stefan Mihalas
Most cortical inhibitory cell types exclusively express one of three genes, parvalbumin, somatostatin and 5HT3a. We conjecture that these three inhibitory neuron types possess distinct roles in visual contextual processing based on two observations. First, they have distinctive synaptic sources and targets over different spatial extents and from different areas. Second, the visual responses of cortical neurons are affected not only by local cues, but also by visual context. We use modeling to relate structural information to function in primary visual cortex (V1) of the mouse, and investigate their role in contextual visual processing...
2017: Frontiers in Computational Neuroscience
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