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Computational neuroscience

Tiina Manninen, Riikka Havela, Marja-Leena Linne
The computational neuroscience field has heavily concentrated on the modeling of neuronal functions, largely ignoring other brain cells, including one type of glial cell, the astrocytes. Despite the short history of modeling astrocytic functions, we were delighted about the hundreds of models developed so far to study the role of astrocytes, most often in calcium dynamics, synchronization, information transfer, and plasticity in vitro , but also in vascular events, hyperexcitability, and homeostasis. Our goal here is to present the state-of-the-art in computational modeling of astrocytes in order to facilitate better understanding of the functions and dynamics of astrocytes in the brain...
2018: Frontiers in Computational Neuroscience
Alice Mizrahi, Tifenn Hirtzlin, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Julie Grollier, Damien Querlioz
In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements...
April 18, 2018: Nature Communications
Karla Batista-García-Ramó, Caridad Ivette Fernández-Verdecia
How the human brain works is still a question, as is its implication with brain architecture: the non-trivial structure–function relationship. The main hypothesis is that the anatomic architecture conditions, but does not determine, the neural network dynamic. The functional connectivity cannot be explained only considering the anatomical substrate. This involves complex and controversial aspects of the neuroscience field and that the methods and methodologies to obtain structural and functional connectivity are not always rigorously applied...
April 18, 2018: Behavioral Sciences
Haibo Shi, Yaoru Sun, Jie Li
Conventional models of motor control exploit the spatial representation of the controlled system to generate control commands. Typically, the control command is gained with the feedback state of a specific instant in time, which behaves like an optimal regulator or spatial filter to the feedback state. Yet, recent neuroscience studies found that the motor network may constitute an autonomous dynamical system and the temporal patterns of the control command can be contained in the dynamics of the motor network, that is, the dynamical system hypothesis (DSH)...
2018: Computational Intelligence and Neuroscience
Weijian Yang, Rafael Yuste
Optical imaging methods are powerful tools in neuroscience as they can systematically monitor the activity of neuronal populations with high spatiotemporal resolution using calcium or voltage indicators. Moreover, caged compounds and optogenetic actuators enable to optically manipulate neural activity. Among optical methods, computer-generated holography offers an enormous flexibility to sculpt the excitation light in three-dimensions (3D), particularly when combined with two-photon light sources. By projecting holographic light patterns on the sample, the activity of multiple neurons across a 3D brain volume can be simultaneously imaged or optically manipulated with single-cell precision...
April 13, 2018: Current Opinion in Neurobiology
Alexandra Sebastian, Birte U Forstmann, Dora Matzke
Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking...
April 13, 2018: Neuroscience and Biobehavioral Reviews
Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Jin Yan, Guorong Wu
Functional connectivity (FC) has been widely investigated in many imaging-based neuroscience and clinical studies. Since functional Magnetic Resonance Image (MRI) signal is just an indirect reflection of brain activity, it is difficult to accurately quantify the FC strength only based on signal correlation. To address this limitation, we propose a learning-based tensor model to derive high sensitivity and specificity connectome biomarkers at the individual level from resting-state fMRI images. First, we propose a learning-based approach to estimate the intrinsic functional connectivity...
June 2017: Information Processing in Medical Imaging: Proceedings of the ... Conference
Lorenz Esch, Limin Sun, Viktor Klüber, Seok Lew, Daniel Baumgarten, P Ellen Grant, Yoshio Okada, Jens Haueisen, Matti S Hämäläinen, Christoph Dinh
BACKGROUND: Magnetoencephalography (MEG) and Electroencephalography (EEG) are noninvasive techniques to study the electrophysiological activity of the human brain. Thus, they are well suited for real-time monitoring and analysis of neuronal activity. Real-time MEG/EEG data processing allows adjustment of the stimuli to the subject's responses for optimizing the acquired information especially by providing dynamically changing displays to enable neurofeedback. NEW METHOD: We introduce MNE Scan, an acquisition and real-time analysis software based on the multipurpose software library MNE-CPP...
April 2, 2018: Journal of Neuroscience Methods
Rajeev V Rikhye, Ralf D Wimmer, Michael M Halassa
The thalamus has long been suspected to have an important role in cognition, yet recent theories have favored a more corticocentric view. According to this view, the thalamus is an excitatory feedforward relay to or between cortical regions, and cognitively relevant computations are exclusively cortical. Here, we review anatomical, physiological, and behavioral studies along evolutionary and theoretical dimensions, arguing for essential and unique thalamic computations in cognition. Considering their architectural features as well as their ability to initiate, sustain, and switch cortical activity, thalamic circuits appear uniquely suited for computing contextual signals that rapidly reconfigure task-relevant cortical representations...
April 4, 2018: Annual Review of Neuroscience
Irene Dowding, Stefan Haufe
Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-level data by a single quantity per subject, which is often the mean or the difference between class means, and treat these as samples in a group-level t -test...
2018: Frontiers in Human Neuroscience
Meng Hu, Wu Li, Hualou Liang
In systems neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure the causal interactions among multiple neurons in the brain is crucial to understanding how neurons work in concert to generate specific brain functions. The development of new statistical methods for assessing causal influence between spike trains is still an active field of neuroscience research. Here, we suggest a copula-based Granger causality measure for the analysis of neural spike train data...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Kuang Gong, Jinxiu Cheng-Liao, Guobao Wang, Kevin T Chen, Ciprian Catana, Jinyi Qi
Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information...
April 2018: IEEE Transactions on Medical Imaging
Alexander E Hramov, Nikita S Frolov, Vladimir A Maksimenko, Vladimir V Makarov, Alexey A Koronovskii, Juan Garcia-Prieto, Luis Fernando Antón-Toro, Fernando Maestú, Alexander N Pisarchik
Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations...
March 2018: Chaos
Steven M Silverstein, Michael Wibral, William A Phillips
Information theory provides a formal framework within which information processing and its disorders can be described. However, information theory has rarely been applied to modeling aspects of the cognitive neuroscience of schizophrenia. The goal of this article is to highlight the benefits of an approach based on information theory, including its recent extensions, for understanding several disrupted neural goal functions as well as related cognitive and symptomatic phenomena in schizophrenia. We begin by demonstrating that foundational concepts from information theory-such as Shannon information, entropy, data compression, block coding, and strategies to increase the signal-to-noise ratio-can be used to provide novel understandings of cognitive impairments in schizophrenia and metrics to evaluate their integrity...
October 2017: Computational psychiatry
Juan Mei, Ji Zhao
Presynaptic neurotoxins and postsynaptic neurotoxins are two important neurotoxins isolated from venoms of venomous animals and have been proven to be potential effective in neurosciences and pharmacology. With the number of toxin sequences appeared in the public databases, there was a need for developing a computational method for fast and accurate identification and classification of the novel presynaptic neurotoxins and postsynaptic neurotoxins in the large databases. In this study, the Multinomial Naive Bayes Classifier (MNBC) had been developed to discriminate the presynaptic neurotoxins and postsynaptic neurotoxins based on the different kinds of features...
March 26, 2018: Journal of Theoretical Biology
Azamat Yeldesbay, Tibor Tóth, Silvia Daun
Detailed neural network models of animal locomotion are important means to understand the underlying mechanisms that control the coordinated movement of individual limbs. Daun-Gruhn and Tóth, Journal of Computational Neuroscience 31(2), 43-60 (2011) constructed an inter-segmental network model of stick insect locomotion consisting of three interconnected central pattern generators (CPGs) that are associated with the protraction-retraction movements of the front, middle and hind leg. This model could reproduce the basic locomotion coordination patterns, such as tri- and tetrapod, and the transitions between them...
March 27, 2018: Journal of Computational Neuroscience
Ferihan Popova, Antoaneta Kovacheva, Petar Garov, Stefan Sivkov, Sevdalina Kandilarova, Nickolay Sirakov, Magdalena Stoeva, Kichka G Velkova
RATIONALE, AIMS, AND OBJECTIVES: Human brain connectome is a new and rapidly developing field in neuroscience. The pattern of structural and functional connectivity in the brain is not fixed but is continuously changing in response to experiences. Exploring these phenomena opens a powerful arsenal of analyses and computational approaches that could provide important new insights into clinical and cognitive neuroscience. The aim of the present study was to investigate the activations of adult brain cortical areas during a memory task performance by using functional MRI with a specific focus on gender differences...
March 23, 2018: Journal of Evaluation in Clinical Practice
Gabriel B Mindlin
Vocal development is usually studied from the perspective of neuroscience. In this issue, Zhang and Ghazanfar propose a way in which body growth might condition the process. They study the vocalizations of marmoset infants with a wide range of techniques that include computational models and experiments that mimic growth reversal. Their results suggest that the qualitative changes that occur during development are rooted in the nonlinear interaction between the nervous system and the biomechanics involved in respiration...
March 22, 2018: PLoS Biology
Harald Hampel, Nicola Toschi, Claudio Babiloni, Filippo Baldacci, Keith L Black, Arun L W Bokde, René S Bun, Francesco Cacciola, Enrica Cavedo, Patrizia A Chiesa, Olivier Colliot, Cristina-Maria Coman, Bruno Dubois, Andrea Duggento, Stanley Durrleman, Maria-Teresa Ferretti, Nathalie George, Remy Genthon, Marie-Odile Habert, Karl Herholz, Yosef Koronyo, Maya Koronyo-Hamaoui, Foudil Lamari, Todd Langevin, Stéphane Lehéricy, Jean Lorenceau, Christian Neri, Robert Nisticò, Francis Nyasse-Messene, Craig Ritchie, Simone Rossi, Emiliano Santarnecchi, Olaf Sporns, Steven R Verdooner, Andrea Vergallo, Nicolas Villain, Erfan Younesi, Francesco Garaci, Simone Lista
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND...
March 16, 2018: Journal of Alzheimer's Disease: JAD
Marco K Wittmann, Patricia L Lockwood, Matthew F S Rushworth
Activity in a network of areas spanning the superior temporal sulcus, dorsomedial frontal cortex, and anterior cingulate cortex is concerned with how nonhuman primates negotiate the social worlds in which they live. Central aspects of these circuits are retained in humans. Activity in these areas codes for primates' interactions with one another, their attempts to find out about one another, and their attempts to prevent others from finding out too much about themselves. Moreover, important features of the social world, such as dominance status, cooperation, and competition, modulate activity in these areas...
March 21, 2018: Annual Review of Neuroscience
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