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Frontiers in Computational Neuroscience

Erik Bresch, Ulf Großekathöfer, Gary Garcia-Molina
Objective: We investigate the design of deep recurrent neural networks for detecting sleep stages from single channel EEG signals recorded at home by non-expert users. We report the effect of data set size, architecture choices, regularization, and personalization on the classification performance. Methods: We evaluated 58 different architectures and training configurations using three-fold cross validation. Results: A network consisting of convolutional (CONV) layers and long short term memory (LSTM) layers can achieve an agreement with a human annotator of Cohen's Kappa of ~0...
2018: Frontiers in Computational Neuroscience
Ahmed A Moustafa, Thierno M O Diallo, Nicola Amoroso, Nazar Zaki, Mubashir Hassan, Hany Alashwal
No abstract text is available yet for this article.
2018: Frontiers in Computational Neuroscience
Mohammad Zarei, Mehran Jahed, Mohammad Reza Daliri
Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced...
2018: Frontiers in Computational Neuroscience
Mitra Abedini, Tahereh Tekieh, Pezhman Sasanpour
[This corrects the article DOI: 10.3389/fncom.2018.00061.].
2018: Frontiers in Computational Neuroscience
Daniel F B Haeufle, Birgit Schmortte, Hartmut Geyer, Roy Müller, Syn Schmitt
It is often assumed that the spinal control of human locomotion combines feed-forward central pattern generation with sensory feedback via muscle reflexes. However, the actual contribution of each component to the generation and stabilization of gait is not well understood, as direct experimental evidence for either is difficult to obtain. We here investigate the relative contribution of the two components to gait stability in a simulation model of human walking. Specifically, we hypothesize that a simple linear combination of feedback and feed-forward control at the level of the spinal cord improves the reaction to unexpected step down perturbations...
2018: Frontiers in Computational Neuroscience
Zeinab Mortezapouraghdam, Farah I Corona-Strauss, Kazutaka Takahashi, Daniel J Strauss
The phase-reset model of oscillatory EEG activity has received a lot of attention in the last decades for decoding different cognitive processes. Based on this model, the ERPs are assumed to be generated as a result of phase reorganization in ongoing EEG. Alignment of the phase of neuronal activities can be observed within or between different assemblies of neurons across the brain. Phase synchronization has been used to explore and understand perception, attentional binding and considering it in the domain of neuronal correlates of consciousness...
2018: Frontiers in Computational Neuroscience
Nima Dehghani
Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales...
2018: Frontiers in Computational Neuroscience
Satoshi Kuroki, Takuya Isomura
Humans have flexible control over cognitive functions depending on the context. Several studies suggest that the prefrontal cortex (PFC) controls this cognitive flexibility, but the detailed underlying mechanisms remain unclear. Recent developments in machine learning techniques allow simple PFC models written as a recurrent neural network to perform various behavioral tasks like humans and animals. Computational modeling allows the estimation of neuronal parameters that are crucial for performing the tasks, which cannot be observed by biologic experiments...
2018: Frontiers in Computational Neuroscience
Ye Yuan, Hong Huo, Tao Fang
As a sophisticated computing unit, the pyramidal neuron requires sufficient metabolic energy to fuel its powerful computational capabilities. However, the majority of previous works focus on nonlinear integration and energy consumption in individual pyramidal neurons but seldom on the effects of metabolic energy on synaptic transmission and dendritic integration. Here, we developed biologically plausible models to simulate the synaptic transmission and dendritic integration of pyramidal neurons, exploring the relations between synaptic transmission and metabolic energy and between dendritic integration and metabolic energy...
2018: Frontiers in Computational Neuroscience
Nitin J Williams, Ian Daly, Slawomir J Nasuto
The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each "trial," using a sparse-MVAR (Multi-Variate Auto-Regressive) model...
2018: Frontiers in Computational Neuroscience
Terence D Sanger
The mechanism by which deep brain stimulation (DBS) improves dystonia is not understood, partly heterogeneity of the underlying disorders leads to differing effects of stimulation in different locations. Similarity between the effects of DBS and the effects of lesions has led to biophysical models of blockade or reduced transmission of involuntary activity in individual cells in the pathways responsible for dystonia. Here, we expand these theories by modeling the effect of DBS on populations of neurons. We emphasize the important observation that the DBS signal itself causes surprisingly few side effects and does not normally appear in the electromyographic signal...
2018: Frontiers in Computational Neuroscience
Timothée Masquelier, Saeed R Kheradpisheh
Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding...
2018: Frontiers in Computational Neuroscience
Yin Tian, Huiling Zhang, Yu Pang, Jinzhao Lin
Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive and negative emotions of around 170 ms at the occipitotemporal electrodes (i...
2018: Frontiers in Computational Neuroscience
Tommaso Gili, Valentina Ciullo, Gianfranco Spalletta
The dynamics of the environment where we live in and the interaction with it, predicting events, provided strong evolutionary pressures for the brain functioning to process temporal information and generate timed responses. As a result, the human brain is able to process temporal information and generate temporal patterns. Despite the clear importance of temporal processing to cognition, learning, communication and sensory, motor and emotional processing, the basal mechanisms of how animals differentiate simple intervals or provide timed responses are still under debate...
2018: Frontiers in Computational Neuroscience
Ritesh A Ramdhani, Anahita Khojandi, Oleg Shylo, Brian H Kopell
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease...
2018: Frontiers in Computational Neuroscience
Brian A Cohn, May Szedlák, Bernd Gärtner, Francisco J Valero-Cuevas
We present Feasibility Theory, a conceptual and computational framework to unify today's theories of neuromuscular control. We begin by describing how the musculoskeletal anatomy of the limb, the need to control individual tendons, and the physics of a motor task uniquely specify the family of all valid muscle activations that accomplish it (its 'feasible activation space'). For our example of producing static force with a finger driven by seven muscles, computational geometry characterizes-in a complete way-the structure of feasible activation spaces as 3-dimensional polytopes embedded in 7-D...
2018: Frontiers in Computational Neuroscience
Mikhail I Rabinovich, Pablo Varona
Discrete sequential information coding is a key mechanism that transforms complex cognitive brain activity into a low-dimensional dynamical process based on the sequential switching among finite numbers of patterns. The storage size of the corresponding process is large because of the permutation capacity as a function of control signals in ensembles of these patterns. Extracting low-dimensional functional dynamics from multiple large-scale neural populations is a central problem both in neuro- and cognitive- sciences...
2018: Frontiers in Computational Neuroscience
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
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
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
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