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Computational neuroscience

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https://www.readbyqxmd.com/read/28333494/taxonomic-and-thematic-semantic-systems
#1
Daniel Mirman, Jon-Frederick Landrigan, Allison E Britt
Object concepts are critical for nearly all aspects of human cognition, from perception tasks like object recognition, to understanding and producing language, to making meaningful actions. Concepts can have 2 very different kinds of relations: similarity relations based on shared features (e.g., dog-bear), which are called "taxonomic" relations, and contiguity relations based on co-occurrence in events or scenarios (e.g., dog-leash), which are called "thematic" relations. Here, we report a systematic review of experimental psychology and cognitive neuroscience evidence of this distinction in the structure of semantic memory...
March 23, 2017: Psychological Bulletin
https://www.readbyqxmd.com/read/28332742/computational-neuroscience-in-the-european-journal-of-neuroscience
#2
EDITORIAL
Panayiota Poirazi, Stefan Remy, Athanasia Papoutsi
Many of us played computer games during our childhood and youth, and some of us still do, while others decided to do something seemingly more useful - like trying to better understand the mammalian brain. Many computer games have a linear progression; individual levels are subdivisions of a larger, more complex world. The practical advantage of having levels is that they divide a game into manageable sections. This article is protected by copyright. All rights reserved.
March 23, 2017: European Journal of Neuroscience
https://www.readbyqxmd.com/read/28327916/multilayer-modeling-and-analysis-of-human-brain-networks
#3
Manlio De Domenico
Understanding how the human brain is structured, and how its architecture is related to the function, is of paramount importance for a variety of applications, including, but not limited to, new ways to prevent, deal with and cure brain diseases, such as Alzheimer's or Parkinson's, and psychiatric disorders, such as Schizophrenia. The recent advances in structural and functional neuroimaging, together with the increasing attitude to interdisciplinary approaches involving computer science, mathematics and physics, are fostering interesting results from computational neuroscience, that are quite often based on the analysis of complex network representation of human brain...
February 6, 2017: GigaScience
https://www.readbyqxmd.com/read/28324757/perceptual-category-learning-and-visual-processing-an-exercise-in-computational-cognitive-neuroscience
#4
George Cantwell, Maximilian Riesenhuber, Jessica L Roeder, F Gregory Ashby
The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning...
March 6, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28321440/neuronify-an-educational-simulator-for-neural-circuits
#5
Svenn-Arne Dragly, Milad Hobbi Mobarhan, Andreas Våvang Solbrå, Simen Tennøe, Anders Hafreager, Anders Malthe-Sørenssen, Marianne Fyhn, Torkel Hafting, Gaute T Einevoll
Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker)...
March 2017: ENeuro
https://www.readbyqxmd.com/read/28314445/where-does-eeg-come-from-and-what-does-it-mean
#6
REVIEW
Michael X Cohen
Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers...
March 14, 2017: Trends in Neurosciences
https://www.readbyqxmd.com/read/28298701/identification-of-probabilities
#7
Paul M B Vitányi, Nick Chater
Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in principle? We explore this question and find some surprisingly positive and general results...
February 2017: Journal of Mathematical Psychology
https://www.readbyqxmd.com/read/28278228/bids-apps-improving-ease-of-use-accessibility-and-reproducibility-of-neuroimaging-data-analysis-methods
#8
Krzysztof J Gorgolewski, Fidel Alfaro-Almagro, Tibor Auer, Pierre Bellec, Mihai Capotă, M Mallar Chakravarty, Nathan W Churchill, Alexander Li Cohen, R Cameron Craddock, Gabriel A Devenyi, Anders Eklund, Oscar Esteban, Guillaume Flandin, Satrajit S Ghosh, J Swaroop Guntupalli, Mark Jenkinson, Anisha Keshavan, Gregory Kiar, Franziskus Liem, Pradeep Reddy Raamana, David Raffelt, Christopher J Steele, Pierre-Olivier Quirion, Robert E Smith, Stephen C Strother, Gaël Varoquaux, Yida Wang, Tal Yarkoni, Russell A Poldrack
The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package...
March 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28270761/reproducibility-and-comparability-of-computational-models-for-astrocyte-calcium-excitability
#9
Tiina Manninen, Riikka Havela, Marja-Leena Linne
The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28269591/a-compact-representation-for-the-auditory-full-range-response-and-its-fast-denoising-using-an-image-filter-based-on-the-radon-transform
#10
Manuel C Kohl, Daniel J Strauss
The Auditory Brainstem, Middle-Latency and Late Responses, a class of event-related potentials (ERPs), are of considerable interest in neuroscience research as robust neural correlates of different processing stages along the auditory pathway. While most research to date centers around one of the responses at a time for practical reasons, recent efforts indicate a paradigm shift towards acquiring them together, enabling the simultaneous monitoring of all auditory processing stages from the brainstem to the cortex...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269550/modular-multipin-electrodes-for-comfortable-dry-eeg
#11
P Fiedler, D Strohmeier, A Hunold, S Griebel, R Muhle, M Schreiber, P Pedrosa, B Vasconcelos, C Fonseca, F Vaz, J Haueisen
Electrode and cap concepts for continuous and ubiquitous monitoring of brain activity will open up new fields of application and contribute to increased use of electroencephalography (EEG) in clinical routine, neurosciences, brain-computer-interfacing and out-of-the-lab monitoring. However, mobile and unobtrusive applications are currently hindered by the lack of applicable convenient and reliable electrode and cap systems. We propose a novel modular electrode concept based on a flexible polymer substrate, coated with electrically conductive metallic films...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269499/a-time-domain-frequency-selective-multivariate-granger-causality-approach
#12
Lutz Leistritz, Herbert Witte
The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268946/designing-a-hands-on-brain-computer-interface-laboratory-course
#13
Bahar Khalighinejad, Laura Kathleen Long, Nima Mesgarani
Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28261127/the-theory-of-localist-representation-and-of-a-purely-abstract-cognitive-system-the-evidence-from-cortical-columns-category-cells-and-multisensory-neurons
#14
Asim Roy
The debate about representation in the brain and the nature of the cognitive system has been going on for decades now. This paper examines the neurophysiological evidence, primarily from single cell recordings, to get a better perspective on both the issues. After an initial review of some basic concepts, the paper reviews the data from single cell recordings - in cortical columns and of category-selective and multisensory neurons. In neuroscience, columns in the neocortex (cortical columns) are understood to be a basic functional/computational unit...
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/28259780/regression-dcm-for-fmri
#15
Stefan Frässle, Ekaterina I Lomakina, Adeel Razi, Karl J Friston, Joachim M Buhmann, Klaas E Stephan
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks...
March 1, 2017: NeuroImage
https://www.readbyqxmd.com/read/28246033/jive-integration-of-imaging-and-behavioral-data
#16
Qunqun Yu, Benjamin B Risk, Kai Zhang, J S Marron
A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image...
February 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/28243197/computer-aided-experiment-planning-toward-causal-discovery-in-neuroscience
#17
Nicholas J Matiasz, Justin Wood, Wei Wang, Alcino J Silva, William Hsu
Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28242730/behavioural-and-computational-varieties-of-response-inhibition-in-eye-movements
#18
REVIEW
Vassilis Cutsuridis
Response inhibition is the ability to override a planned or an already initiated response. It is the hallmark of executive control as its deficits favour impulsive behaviours, which may be detrimental to an individual's life. This article reviews behavioural and computational guises of response inhibition. It focuses only on inhibition of oculomotor responses. It first reviews behavioural paradigms of response inhibition in eye movement research, namely the countermanding and antisaccade paradigms, both proven to be useful tools for the study of response inhibition in cognitive neuroscience and psychopathology...
April 19, 2017: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/28239346/parallel-steps-large-scale-stochastic-spatial-reaction-diffusion-simulation-with-high-performance-computers
#19
Weiliang Chen, Erik De Schutter
Stochastic, spatial reaction-diffusion simulations have been widely used in systems biology and computational neuroscience. However, the increasing scale and complexity of models and morphologies have exceeded the capacity of any serial implementation. This led to the development of parallel solutions that benefit from the boost in performance of modern supercomputers. In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28230848/computational-approaches-to-fmri-analysis
#20
Jonathan D Cohen, Nathaniel Daw, Barbara Engelhardt, Uri Hasson, Kai Li, Yael Niv, Kenneth A Norman, Jonathan Pillow, Peter J Ramadge, Nicholas B Turk-Browne, Theodore L Willke
Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis...
February 23, 2017: Nature Neuroscience
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