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dynamic neural field

Wei-Long Zheng, Baoliang Lu
OBJECTIVE: Covert aspects of ongoing user mental states provide key context information for user-aware human computer interactions. In this paper, we focus on the problem of estimating the vigilance of users using EEG and EOG signals. APPROACH: The PERCLOS index as vigilance annotation is obtained from eye tracking glasses. To improve the feasibility and wearability of vigilance estimation devices for real-world applications, we adopt a novel electrode placement for forehead EOG and extract various eye movement features, which contain the principal information of traditional EOG...
January 19, 2017: Journal of Neural Engineering
Ben W Dulken, Dena S Leeman, Stéphane C Boutet, Katja Hebestreit, Anne Brunet
Neural stem cells (NSCs) in the adult mammalian brain serve as a reservoir for the generation of new neurons, oligodendrocytes, and astrocytes. Here, we use single-cell RNA sequencing to characterize adult NSC populations and examine the molecular identities and heterogeneity of in vivo NSC populations. We find that cells in the NSC lineage exist on a continuum through the processes of activation and differentiation. Interestingly, rare intermediate states with distinct molecular profiles can be identified and experimentally validated, and our analysis identifies putative surface markers and key intracellular regulators for these subpopulations of NSCs...
January 17, 2017: Cell Reports
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons...
January 17, 2017: PLoS Computational Biology
Daniel E Winkowski, Daniel A Nagode, Kevin J Donaldson, Pingbo Yin, Shihab A Shamma, Jonathan B Fritz, Patrick O Kanold
Sensory environments change over a wide dynamic range and sensory processing can change rapidly to facilitate stable perception. While rapid changes may occur throughout the sensory processing pathway, cortical changes are believed to profoundly influence perception. Prior stimulation studies showed that orbitofrontal cortex (OFC) can modify receptive fields and sensory coding in A1, but the engagement of OFC during listening and the pathways mediating OFC influences on A1 are unknown. We show in mice that OFC neurons respond to sounds consistent with a role of OFC in audition...
January 8, 2017: Cerebral Cortex
Shuangming Yang, Bin Deng, Jiang Wang, Huiyan Li, Chen Liu, Chris Fietkiewicz, Kenneth A Loparo
Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization...
January 9, 2017: Scientific Reports
Qihe Shan, Huaguang Zhang, Zhanshan Wang, Zhao Zhang
Neural networks (NNs) in the stochastic environment were widely modeled as stochastic differential equations, which were driven by white noise, such as Brown or Wiener process in the existing papers. However, they are not necessarily the best models to describe dynamic characters of NNs disturbed by nonwhite noise in some specific situations. In this paper, general noise disturbance, which may be nonwhite, is introduced to NNs. Since NNs with nonwhite noise cannot be described by Itô integral equation, a novel modeling method of stochastic NNs is utilized...
December 29, 2016: IEEE Transactions on Neural Networks and Learning Systems
Mathis Richter, Jonas Lins, Gregor Schöner
Describing actions entails that relations between objects are discovered. A pervasively neural account of this process requires that fundamental problems are solved: the neural pointer problem, the binding problem, and the problem of generating discrete processing steps from time-continuous neural processes. We present a prototypical solution to these problems in a neural dynamic model that comprises dynamic neural fields holding representations close to sensorimotor surfaces as well as dynamic neural nodes holding discrete, language-like representations...
January 5, 2017: Topics in Cognitive Science
Florian Fiebig, Anders Lansner
: A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations...
January 4, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Iris I A Groen, Edward H Silson, Chris I Baker
Visual scene analysis in humans has been characterized by the presence of regions in extrastriate cortex that are selectively responsive to scenes compared with objects or faces. While these regions have often been interpreted as representing high-level properties of scenes (e.g. category), they also exhibit substantial sensitivity to low-level (e.g. spatial frequency) and mid-level (e.g. spatial layout) properties, and it is unclear how these disparate findings can be united in a single framework. In this opinion piece, we suggest that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and discuss why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition...
February 19, 2017: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
Maria Giulia Preti, Thomas Aw Bolton, Dimitri Van De Ville
Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment...
December 26, 2016: NeuroImage
Nathan Caruana, Genevieve McArthur, Alexandra Woolgar, Jon Brock
Social interactions are, by their nature, dynamic and reciprocal - your behaviour affects my behaviour, which affects your behaviour in return. However, until recently, the field of social cognitive neuroscience has been dominated by paradigms in which participants passively observe social stimuli from a detached "third person" perspective. Here we consider the unique conceptual and methodological challenges involved in adopting a "second person" approach whereby social cognitive mechanisms and their neural correlates are investigated within social interactions (Schilbach et al...
December 24, 2016: Neuroscience and Biobehavioral Reviews
Laurence Aitchison, Máté Lengyel
Probabilistic inference offers a principled framework for understanding both behaviour and cortical computation. However, two basic and ubiquitous properties of cortical responses seem difficult to reconcile with probabilistic inference: neural activity displays prominent oscillations in response to constant input, and large transient changes in response to stimulus onset. Indeed, cortical models of probabilistic inference have typically either concentrated on tuning curve or receptive field properties and remained agnostic as to the underlying circuit dynamics, or had simplistic dynamics that gave neither oscillations nor transients...
December 2016: PLoS Computational Biology
Oscar Herreras
The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media...
2016: Frontiers in Neural Circuits
Alberto Galbusera, Alessia De Felice, Stefano Girardi, Giacomo Bassetto, Marta Maschietto, Katsuhiko Nishimori, Bice Chini, Francesco Papaleo, Stefano Vassanelli, Alessandro Gozzi
The neuropeptides oxytocin (OXT) and vasopressin (AVP) have been identified as modulators of emotional social behaviors and associated with neuropsychiatric disorders characterized by social dysfunction. Experimental and therapeutic use of OXT and AVP via the intranasal route is the subject of extensive clinical research. However, the large-scale functional substrates directly engaged by these peptides and their functional dynamics remain elusive. By using cerebral blood volume (CBV) weighted fMRI in the mouse, we show that intranasal administration of OXT rapidly elicits the transient activation of cortical regions and a sustained activation of hippocampal and forebrain areas characterized by high oxytocin receptor density...
January 18, 2017: Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology
Milos Jordanski, Milos Radovic, Zarko Milosevic, Nenad Filipovic, Zoran Obradovic
Computer simulations based on the finite element method (FEM) represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis...
December 14, 2016: IEEE Journal of Biomedical and Health Informatics
Woojae Kim, Mark A Pitt, Zhong-Lin Lu, Jay I Myung
Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one-step-ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only...
December 18, 2016: Cognitive Science
Simon L Dettmer, H Chau Nguyen, Johannes Berg
Nonequilibrium systems lack an explicit characterization of their steady state like the Boltzmann distribution for equilibrium systems. This has drastic consequences for the inference of the parameters of a model when its dynamics lacks detailed balance. Such nonequilibrium systems occur naturally in applications like neural networks and gene regulatory networks. Here, we focus on the paradigmatic asymmetric Ising model and show that we can learn its parameters from independent samples of the nonequilibrium steady state...
November 2016: Physical Review. E
Birger Kollmeier, Jürgen Kiessling
A review about technical and perceptual factors in hearing aid technology, research and development is provided, covering current commercial solutions, underlying models of hearing loss for usage in hearing devices and emerging future technical solutions for hearing aid functionalities. A chain of techniques has provided incremental, but steady increases in user benefit, e.g. in the fields of hearing aid amplification, feedback suppression, dynamic compression, noise reduction and situation adaptation. The models describing the perceptual consequences of sensorineural hearing impairment describe the effects on the acoustical level, the neurosensory level and the cognitive level and provide the framework for compensatory (or even substitutional) functions of hearing aids in terms of the attenuation component, the distortion component and the neural component of the hearing loss...
December 13, 2016: International Journal of Audiology
Xinyi Geng, Jianguo Zhang, Yin Jiang, Keyoumars Ashkan, Thomas Foltynie, Patricia Limousin, Ludvic Zrinzo, Alexander Green, Tipu Aziz, Peter Brown, Shouyan Wang
OBJECTIVES: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been successfully used to treat both Parkinson's disease (PD) and dystonia. Local field potentials (LFPs) recorded from the STN of PD patients demonstrate prominent beta frequency band activity. It is unclear whether such activity occurs in the STN in dystonia, and, if not, whether dystonia has another distinctive neural population activity in the STN. METHODS: Twelve patients with PD, and eight patients with dystonia underwent DBS electrode implantation targeting the STN...
February 2017: Neurobiology of Disease
G Velmurugan, R Rakkiyappan, V Vembarasan, Jinde Cao, Ahmed Alsaedi
As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay...
November 9, 2016: Neural Networks: the Official Journal of the International Neural Network Society
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