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Frontiers in Computational Neuroscience

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https://www.readbyqxmd.com/read/28736520/combined-effects-of-feedforward-inhibition-and-excitation-in-thalamocortical-circuit-on-the-transitions-of-epileptic-seizures
#1
Denggui Fan, Lixia Duan, Qian Wang, Guoming Luan
The mechanisms underlying electrophysiologically observed two-way transitions between absence and tonic-clonic epileptic seizures in cerebral cortex remain unknown. The interplay within thalamocortical network is believed to give rise to these epileptic multiple modes of activity and transitions between them. In particular, it is thought that in some areas of cortex there exists feedforward inhibition from specific relay nucleus of thalamus (TC) to inhibitory neuronal population (IN) which has even more stronger functions on cortical activities than the known feedforward excitation from TC to excitatory neuronal population (EX)...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28713259/transcallosal-inhibition-during-motor-imagery-analysis-of-a-neural-mass-model
#2
Anna L Mangia, Mauro Ursino, Maurizio Lannocca, Angelo Cappello
The EEG rhythmic activities of the somato-sensory cortex reveal event-related desynchronization (ERD) or event-related synchronization (ERS) in beta band (14-30 Hz) as subjects perform certain tasks or react to specific stimuli. Data reported for imagination of movement support the hypothesis that activation of one sensorimotor area (SMA) can be accompanied by deactivation of the other. In order to improve our understanding of beta ERD/ERS generation, two neural mass models (NMM) of a cortical column taken from Wendling et al...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28713258/relationship-of-topology-multiscale-phase-synchronization-and-state-transitions-in-human-brain-networks
#3
Minkyung Kim, Seunghwan Kim, George A Mashour, UnCheol Lee
How the brain reconstitutes consciousness and cognition after a major perturbation like general anesthesia is an important question with significant neuroscientific and clinical implications. Recent empirical studies in animals and humans suggest that the recovery of consciousness after anesthesia is not random but ordered. Emergence patterns have been classified as progressive and abrupt transitions from anesthesia to consciousness, with associated differences in duration and electroencephalogram (EEG) properties...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28701945/magnetoencephalography-in-preoperative-epileptic-foci-localization-enlightenment-from-cognitive-studies
#4
REVIEW
Qian Wang, Pengfei Teng, Guoming Luan
Over 30% epileptic patients are refractory to medication, who are amenable to neurosurgical treatment. Non-invasive brain imaging technologies including video-electroencephalogram (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG) are widely used in presurgical assessment of epileptic patients. This review mainly discussed the current development of clinical MEG imaging as a diagnose approach, and its correlations with the golden standard intracranial electroencephalogram (iEEG). More importantly, this review discussed the possible applications of functional networks in preoperative epileptic foci localization in future studies...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28690510/eeg-based-monitoring-of-general-anesthesia-taking-the-next-steps
#5
Matthias Kreuzer
No abstract text is available yet for this article.
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28690509/models-of-acetylcholine-and-dopamine-signals-differentially-improve-neural-representations
#6
Raphaël Holca-Lamarre, Jörg Lücke, Klaus Obermayer
Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28690508/cortical-dynamics-in-presence-of-assemblies-of-densely-connected-weight-hub-neurons
#7
Hesam Setareh, Moritz Deger, Carl C H Petersen, Wulfram Gerstner
Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28663729/estimating-the-information-extracted-by-a-single-spiking-neuron-from-a-continuous-input-time-series
#8
Fleur Zeldenrust, Sicco de Knecht, Wytse J Wadman, Sophie Denève, Boris Gutkin
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e., "the neural code") lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information a single neuron transfers from the input it receives to its output spike train. The input is generated by an artificial neural network that responds to a randomly appearing and disappearing "sensory stimulus": the hidden state...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28659783/multistability-and-long-timescale-transients-encoded-by-network-structure-in-a-model-of-c-elegans-connectome-dynamics
#9
James M Kunert-Graf, Eli Shlizerman, Andrew Walker, J Nathan Kutz
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28659782/cliques-of-neurons-bound-into-cavities-provide-a-missing-link-between-structure-and-function
#10
Michael W Reimann, Max Nolte, Martina Scolamiero, Katharine Turner, Rodrigo Perin, Giuseppe Chindemi, Paweł Dłotko, Ran Levi, Kathryn Hess, Henry Markram
The lack of a formal link between neural network structure and its emergent function has hampered our understanding of how the brain processes information. We have now come closer to describing such a link by taking the direction of synaptic transmission into account, constructing graphs of a network that reflect the direction of information flow, and analyzing these directed graphs using algebraic topology. Applying this approach to a local network of neurons in the neocortex revealed a remarkably intricate and previously unseen topology of synaptic connectivity...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28659781/coarse-grained-descriptions-of-dynamics-for-networks-with-both-intrinsic-and-structural-heterogeneities
#11
Tom Bertalan, Yan Wu, Carlo Laing, C William Gear, Ioannis G Kevrekidis
Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and ultimately design. Here, we propose and illustrate a systematic and powerful approach to obtaining good collective coarse-grained observables-variables successfully summarizing the detailed state of such networks. Finding such variables can naturally lead to successful reduced dynamic models for the networks. The main premise enabling our approach is the assumption that the behavior of a node in the network depends (after a short initial transient) on the node identity: a set of descriptors that quantify the node properties, whether intrinsic (e...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28649196/estimation-of-time-varying-intrinsic-and-reflex-dynamic-joint-stiffness-during-movement-application-to-the-ankle-joint
#12
Diego L Guarín, Robert E Kearney
Dynamic joint stiffness determines the relation between joint position and torque, and plays a vital role in the control of posture and movement. Dynamic joint stiffness can be quantified during quasi-stationary conditions using disturbance experiments, where small position perturbations are applied to the joint and the torque response is recorded. Dynamic joint stiffness is composed of intrinsic and reflex mechanisms that act and change together, so that nonlinear, mathematical models and specialized system identification techniques are necessary to estimate their relative contributions to overall joint stiffness...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28649195/modulating-stdp-balance-impacts-the-dendritic-mosaic
#13
Nicolangelo Iannella, Thomas Launey
The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/28638335/quantification-of-head-movement-predictability-and-implications-for-suppression-of-vestibular-input-during-locomotion
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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