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Computational neuroscience

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https://www.readbyqxmd.com/read/28732273/using-computational-theory-to-constrain-statistical-models-of-neural-data
#1
REVIEW
Scott W Linderman, Samuel J Gershman
Computational neuroscience is, to first order, dominated by two approaches: the 'bottom-up' approach, which searches for statistical patterns in large-scale neural recordings, and the 'top-down' approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data-albeit highly sophisticated ones...
July 18, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28729016/learning-based-structurally-guided-construction-of-resting-state-functional-correlation-tensors
#2
Lichi Zhang, Han Zhang, Xiaobo Chen, Qian Wang, Pew-Thian Yap, Dinggang Shen
Functional magnetic resonance imaging (fMRI) measures changes in blood-oxygenation-level-dependent (BOLD) signals to detect brain activities. It has been recently reported that the spatial correlation patterns of resting-state BOLD signals in the white matter (WM) also give WM information often measured by diffusion tensor imaging (DTI). These correlation patterns can be captured using functional correlation tensor (FCT), which is analogous to the diffusion tensor (DT) obtained from DTI. In this paper, we propose a noise-robust FCT method aiming at further improving its quality, and making it eligible for further neuroscience study...
July 17, 2017: Magnetic Resonance Imaging
https://www.readbyqxmd.com/read/28728020/neuroscience-inspired-artificial-intelligence
#3
REVIEW
Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick
The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals...
July 19, 2017: Neuron
https://www.readbyqxmd.com/read/28727965/is-psychology-headed-in-the-right-direction-yes-no-and-maybe
#4
Carol S Dweck
In this piece, I first celebrate the growing contribution of psychology to the understanding and solution of pressing social issues. Then, despite these exciting developments, I worry about whether we have created a field that our students want to spend their lives in, and I suggest concerns that might fruitfully be addressed. Finally, I worry about the potential fragmentation of psychology and applaud programs of research that have shown the unique and important contributions to be made when the methods and perspectives of neuroscience, cognitive science, and computational modeling are integrated with those of social, personality, and developmental psychology...
July 2017: Perspectives on Psychological Science: a Journal of the Association for Psychological Science
https://www.readbyqxmd.com/read/28724914/enhanced-learning-through-multimodal-training-evidence-from-a-comprehensive-cognitive-physical-fitness-and-neuroscience-intervention
#5
N Ward, E Paul, P Watson, G E Cooke, C H Hillman, N J Cohen, A F Kramer, A K Barbey
The potential impact of brain training methods for enhancing human cognition in healthy and clinical populations has motivated increasing public interest and scientific scrutiny. At issue is the merits of intervention modalities, such as computer-based cognitive training, physical exercise training, and non-invasive brain stimulation, and whether such interventions synergistically enhance cognition. To investigate this issue, we conducted a comprehensive 4-month randomized controlled trial in which 318 healthy, young adults were enrolled in one of five interventions: (1) Computer-based cognitive training on six adaptive tests of executive function; (2) Cognitive and physical exercise training; (3) Cognitive training combined with non-invasive brain stimulation and physical exercise training; (4) Active control training in adaptive visual search and change detection tasks; and (5) Passive control...
July 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28719603/the-%C3%A2-100-lab-a-3d-printable-open-source-platform-for-fluorescence-microscopy-optogenetics-and-accurate-temperature-control-during-behaviour-of-zebrafish-drosophila-and-caenorhabditis-elegans
#6
Andre Maia Chagas, Lucia L Prieto-Godino, Aristides B Arrenberg, Tom Baden
Small, genetically tractable species such as larval zebrafish, Drosophila, or Caenorhabditis elegans have become key model organisms in modern neuroscience. In addition to their low maintenance costs and easy sharing of strains across labs, one key appeal is the possibility to monitor single or groups of animals in a behavioural arena while controlling the activity of select neurons using optogenetic or thermogenetic tools. However, the purchase of a commercial solution for these types of experiments, including an appropriate camera system as well as a controlled behavioural arena, can be costly...
July 2017: PLoS Biology
https://www.readbyqxmd.com/read/28709222/population-density-equations-for-stochastic-processes-with-memory-kernels
#7
Yi Ming Lai, Marc de Kamps
We present a method for solving population density equations (PDEs)--a mean-field technique describing homogeneous populations of uncoupled neurons-where the populations can be subject to non-Markov noise for arbitrary distributions of jump sizes. The method combines recent developments in two different disciplines that traditionally have had limited interaction: computational neuroscience and the theory of random networks. The method uses a geometric binning scheme, based on the method of characteristics, to capture the deterministic neurodynamics of the population, separating the deterministic and stochastic process cleanly...
June 2017: Physical Review. E
https://www.readbyqxmd.com/read/28707628/brainframe-a-node-level-heterogeneous-accelerator-platform-for-neuron-simulations
#8
Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harry Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris de Zeeuw, Christos Strydis
OBJECTIVE: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements...
July 14, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28697300/direct-laser-writing-of-tubular-microtowers-for-3d-culture-of-human-pluripotent-stem-cell-derived-neuronal-cells
#9
Sanna Turunen, Tiina Joki, Maiju Leena Hiltunen, Teemu Olavi Ihalainen, Susanna Narkilahti, Minna Kellomäki
As the complex structure of nervous tissue cannot be mimicked in 2D cultures, the development of 3D neuronal cell culture platforms is a topical issue in the field of neuroscience and neural tissue engineering. Computer-assisted laser-based fabrication techniques such as direct laser writing by two-photon polymerization (2PP-DLW) offer a versatile tool to fabricate 3D cell culture platforms with highly ordered geometries in the size scale of natural 3D cell environments. In this study, we present the design and 2PP-DLW fabrication process of a novel 3D neuronal cell culture platform based on tubular microtowers...
July 11, 2017: ACS Applied Materials & Interfaces
https://www.readbyqxmd.com/read/28692956/deepfix-a-fully-convolutional-neural-network-for-predicting-human-eye-fixations
#10
Srinivas S S Kruthiventi, Kumar Ayush, R Venkatesh Babu
Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner...
September 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28692700/noise-multisensory-integration-and-previous-response-in-perceptual-disambiguation
#11
Cesare V Parise, Marc O Ernst
Sensory information about the state of the world is generally ambiguous. Understanding how the nervous system resolves such ambiguities to infer the actual state of the world is a central quest for sensory neuroscience. However, the computational principles of perceptual disambiguation are still poorly understood: What drives perceptual decision-making between multiple equally valid solutions? Here we investigate how humans gather and combine sensory information-within and across modalities-to disambiguate motion perception in an ambiguous audiovisual display, where two moving stimuli could appear as either streaming through, or bouncing off each other...
July 10, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28690580/locke-s-view-of-the-hard-problem-of-consciousness-and-its-implications-for-neuroscience-and-computer-science
#12
John E Lisman
No abstract text is available yet for this article.
2017: Frontiers in Psychology
https://www.readbyqxmd.com/read/28690444/the-benefits-of-a-real-time-web-based-response-system-for-enhancing-engaged-learning-in-classrooms-and-public-science-events
#13
Mark A Sarvary, Kathleen M Gifford
Large introduction to neuroscience classes and small science cafés have the same goal: bridging the gap between the presenter and the audience to convey the information while being engaging. Early classroom response systems became the cornerstone of flipped and engaged learning. These "clickers" helped turn lectures into dialogues, allowing the presenter to become a facilitator rather than a "sage on the stage." Rapid technological developments, especially the increase of computing power opened up new opportunities, moving these systems from a clicker device onto cellphones and laptops...
2017: Journal of Undergraduate Neuroscience Education: JUNE: a Publication of FUN, Faculty for Undergraduate Neuroscience
https://www.readbyqxmd.com/read/28690439/grasshopper-dcmd-an-undergraduate-electrophysiology-lab-for-investigating-single-unit-responses-to-behaviorally-relevant-stimuli
#14
Dieu My T Nguyen, Mark Roper, Stanislav Mircic, Robert M Olberg, Gregory J Gage
Avoiding capture from a fast-approaching predator is an important survival skill shared by many animals. Investigating the neural circuits that give rise to this escape behavior can provide a tractable demonstration of systems-level neuroscience research for undergraduate laboratories. In this paper, we describe three related hands-on exercises using the grasshopper and affordable technology to bring neurophysiology, neuroethology, and neural computation to life and enhance student understanding and interest...
2017: Journal of Undergraduate Neuroscience Education: JUNE: a Publication of FUN, Faculty for Undergraduate Neuroscience
https://www.readbyqxmd.com/read/28690432/an-algebra-based-introductory-computational-neuroscience-course-with-lab
#15
Christian G Fink
A course in computational neuroscience has been developed at Ohio Wesleyan University which requires no previous experience with calculus or computer programming, and which exposes students to theoretical models of neural information processing and techniques for analyzing neural data. The exploration of theoretical models of neural processes is conducted in the classroom portion of the course, while data analysis techniques are covered in lab. Students learn to program in MATLAB and are offered the opportunity to conclude the course with a final project in which they explore a topic of their choice within computational neuroscience...
2017: Journal of Undergraduate Neuroscience Education: JUNE: a Publication of FUN, Faculty for Undergraduate Neuroscience
https://www.readbyqxmd.com/read/28675490/the-significance-of-negative-correlations-in-brain-connectivity
#16
Liang Zhan, Lisanne M Jenkins, Ouri E Wolfson, Johnson Jonaris GadElkarim, Kevin Nocito, Paul M Thompson, Olusola A Ajilore, Moo K Chung, Alex D Leow
Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative...
July 4, 2017: Journal of Comparative Neurology
https://www.readbyqxmd.com/read/28669605/industrial-medicinal-chemistry-insights-neuroscience-hit-generation-at-janssen
#17
REVIEW
Gary Tresadern, Frederik J R Rombouts, Daniel Oehlrich, Gregor Macdonald, Andres A Trabanco
The role of medicinal chemistry has changed over the past 10 years. Chemistry had become one step in a process; funneling the output of high-throughput screening (HTS) on to the next stage. The goal to identify the ideal clinical compound remains, but the means to achieve this have changed. Modern medicinal chemistry is responsible for integrating innovation throughout early drug discovery, including new screening paradigms, computational approaches, novel synthetic chemistry, gene-family screening, investigating routes of delivery, and so on...
June 29, 2017: Drug Discovery Today
https://www.readbyqxmd.com/read/28668365/recurrent-neural-networks-as-versatile-tools-of-neuroscience-research
#18
REVIEW
Omri Barak
Recurrent neural networks (RNNs) are a class of computational models that are often used as a tool to explain neurobiological phenomena, considering anatomical, electrophysiological and computational constraints. RNNs can either be designed to implement a certain dynamical principle, or they can be trained by input-output examples. Recently, there has been large progress in utilizing trained RNNs both for computational tasks, and as explanations of neural phenomena. I will review how combining trained RNNs with reverse engineering can provide an alternative framework for modeling in neuroscience, potentially serving as a powerful hypothesis generation tool...
June 29, 2017: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/28659756/proprioceptive-feedback-through-a-neuromorphic-muscle-spindle-model
#19
Lorenzo Vannucci, Egidio Falotico, Cecilia Laschi
Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28654358/predicting-motivation-computational-models-of-pfc-can-explain-neural-coding-of-motivation-and-effort-based-decision-making-in-health-and-disease
#20
Eliana Vassena, James Deraeve, William H Alexander
Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted to obtain potential benefits. Medial pFC (MPFC) and dorsolateral pFC (DLPFC) are frequently implicated in the expectation of effortful control, showing increased activity as a function of predicted task difficulty. Such activity partially overlaps with expectation of reward and has been observed both during decision-making and during task preparation. Recently, novel computational frameworks have been developed to explain activity in these regions during cognitive control, based on the principle of prediction and prediction error (predicted response-outcome [PRO] model [Alexander, W...
June 27, 2017: Journal of Cognitive Neuroscience
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