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Computational neuroscience

Shiwei Li, Hang Zhou, Tingwei Quan, Jing Li, Yuxin Li, Anan Li, Qingming Luo, Hui Gong, Shaoqun Zeng
Digital reconstruction of a single neuron occupies an important position in computational neuroscience. Although many novel methods have been proposed, recent advances in molecular labeling and imaging systems allow for the production of large and complicated neuronal datasets, which pose many challenges for neuron reconstruction, especially when discontinuous neuronal morphology appears in a strong noise environment. Here, we develop a new pipeline to address this challenge. Our pipeline is based on two methods, one is the region-to-region connection (RRC) method for detecting the initial part of a neurite, which can effectively gather local cues, i...
December 7, 2016: Neuroinformatics
Logan Chariker, Robert Shapley, Lai-Sang Young
: A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations...
December 7, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
P-Y Oudeyer, J Gottlieb, M Lopes
This chapter studies the bidirectional causal interactions between curiosity and learning and discusses how understanding these interactions can be leveraged in educational technology applications. First, we review recent results showing how state curiosity, and more generally the experience of novelty and surprise, can enhance learning and memory retention. Then, we discuss how psychology and neuroscience have conceptualized curiosity and intrinsic motivation, studying how the brain can be intrinsically rewarded by novelty, complexity, or other measures of information...
2016: Progress in Brain Research
Roberto Colom
Here I briefly delineate my view about the main question of this International Seminar, namely, what should we expecting from the XXI Century regarding the advancements in intelligence research. This view can be summarized as 'The Brain Connection' (TBC), meaning that neuroscience will be of paramount relevance for increasing our current knowledge related to the key question: why are some people smarter than others? We need answers to the issue of what happens in our brains when the genotype and the environment are integrated...
December 6, 2016: Spanish Journal of Psychology
Ahmed Mahfouz, Sjoerd M H Huisman, Boudewijn P F Lelieveldt, Marcel J T Reinders
The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data...
December 1, 2016: Brain Structure & Function
Patrick C Trettenbrein
Synaptic plasticity is widely considered to be the neurobiological basis of learning and memory by neuroscientists and researchers in adjacent fields, though diverging opinions are increasingly being recognized. From the perspective of what we might call "classical cognitive science" it has always been understood that the mind/brain is to be considered a computational-representational system. Proponents of the information-processing approach to cognitive science have long been critical of connectionist or network approaches to (neuro-)cognitive architecture, pointing to the shortcomings of the associative psychology that underlies Hebbian learning as well as to the fact that synapses are practically unfit to implement symbols...
2016: Frontiers in Systems Neuroscience
Kevin S Weiner, Michael A Barnett, Simon Lorenz, Julian Caspers, Anthony Stigliani, Katrin Amunts, Karl Zilles, Bruce Fischl, Kalanit Grill-Spector
A fundamental hypothesis in neuroscience proposes that underlying cellular architecture (cytoarchitecture) contributes to the functionality of a brain area. However, this hypothesis has not been tested in human ventral temporal cortex (VTC) that contains domain-specific regions causally involved in perception. To fill this gap in knowledge, we used cortex-based alignment to register functional regions from living participants to cytoarchitectonic areas in ex vivo brains. This novel approach reveals 3 findings...
November 30, 2016: Cerebral Cortex
Catharine A Winstanley, Stan B Floresco
Maladaptive decision-making is increasingly recognized to play a significant role in numerous psychiatric disorders, such that therapeutics capable of ameliorating core impairments in judgment may be beneficial in a range of patient populations. The field of "decision neuroscience" is therefore in its ascendancy, with researchers from diverse fields bringing their expertise to bear on this complex and fascinating problem. In addition to the advances in neuroimaging and computational neuroscience that contribute enormously to this area, an increase in the complexity and sophistication of behavioral paradigms designed for nonhuman laboratory animals has also had a significant impact on researchers' ability to test the causal nature of hypotheses pertaining to the neural circuitry underlying the choice process...
November 30, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Alexandre Y Dombrovski, Michael N Hallquist
PURPOSE OF REVIEW: Suicide attempts are usually regretted by people who survive them. Furthermore, addiction and gambling are over-represented among people who attempt or die by suicide, raising the question whether their decision-making is impaired. Advances in decision neuroscience have enabled us to investigate decision processes in suicidal people and to elucidate putative neural substrates of disadvantageous decision-making. RECENT FINDINGS: Early studies have linked attempted suicide to poor performance on gambling tasks...
January 2017: Current Opinion in Psychiatry
Sahar Akram, Jonathan Z Simon, Behtash Babadi
OBJECTIVE: A central problem in computational neuroscience is to characterize brain function using neural activity recorded from the brain in response to sensory inputs with statistical confidence. Most of existing estimation techniques, such as those based on reverse correlation, exhibit two main limitations: first, they are unable to produce dynamic estimates of the neural activity at a resolution comparable with that of the recorded data, and second, they often require heavy averaging across time as well as multiple trials in order to construct statistical confidence intervals for a precise interpretation of data...
November 15, 2016: IEEE Transactions on Bio-medical Engineering
Oliver Rübel, Max Dougherty, Prabhat, Peter Denes, David Conant, Edward F Chang, Kristofer Bouchard
Neuroscience continues to experience a tremendous growth in data; in terms of the volume and variety of data, the velocity at which data is acquired, and in turn the veracity of data. These challenges are a serious impediment to sharing of data, analyses, and tools within and across labs. Here, we introduce BRAINformat, a novel data standardization framework for the design and management of scientific data formats. The BRAINformat library defines application-independent design concepts and modules that together create a general framework for standardization of scientific data...
2016: Frontiers in Neuroinformatics
Estrela Neto, Luís Leitão, Daniela M Sousa, Cecília J Alves, Inês S Alencastre, Paulo Aguiar, Meriem Lamghari
Microfluidic technology has become a valuable tool to the scientific community, allowing researchers to study fine cellular mechanisms with higher variable control compared with conventional systems. It has evolved tremendously, and its applicability and flexibility made its usage grow exponentially and transversely to several research fields. This has been particularly noticeable in neuroscience research, where microfluidic platforms made it possible to address specific questions extending from axonal guidance, synapse formation, or axonal transport to the development of 3D models of the CNS to allow pharmacological testing and drug screening...
November 16, 2016: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Hirokazu Tanaka
What does "understanding the brain" mean? Here, I review how computational neuroscience, a theoretical approach to the brain, can aid our understanding of the brain. First, I illustrate the study of reinforcement learning and dopamine neurons and argue its success in the light of Marr's three levels of computation. Second, I discuss how Marr's program has led to a computational understanding of the brain, and present computational models of the motor cortex and of a spiking neural network as illustrative examples...
November 2016: Brain and Nerve, Shinkei Kenkyū No Shinpo
Golrokh Mirzaei, Hojjat Adeli
In recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies...
December 1, 2016: Reviews in the Neurosciences
Pascale Bédard, Anne-Marie Audet, Patrick Drouin, Johanna-Pascale Roy, Julie Rivard, Pascale Tremblay
Sublexical phonotactic regularities in language have a major impact on language development, as well as on speech processing and production throughout the entire lifespan. To understand the impact of phonotactic regularities on speech and language functions at the behavioral and neural levels, it is essential to have access to oral language corpora to study these complex phenomena in different languages. Yet, probably because of their complexity, oral language corpora remain less common than written language corpora...
November 11, 2016: Behavior Research Methods
Helena Głąbska, Chaitanya Chintaluri, Daniel K Wójcik
A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output...
November 11, 2016: Neuroinformatics
Jian Zhang, Chong Li, Tianzi Jiang
Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence...
2016: Frontiers in Neuroinformatics
L A Pastur-Romay, A B Porto-Pazos, F Cedrón, A Pazos
The human brain is the most complex system in the known universe, but it is the most unknown system. It allows the human beings to possess extraordinary capacities. However, we don´t understand yet how and why most of these capacities are produced. For decades, it have been tried that the computers reproduces these capacities. On one hand, to help understanding the nervous system. On the other hand, to process the data in a more efficient way than before. It is intended to make the computers process the information like the brain does it...
November 4, 2016: Current Topics in Medicinal Chemistry
Ludmila Brochini, Ariadne de Andrade Costa, Miguel Abadi, Antônio C Roque, Jorge Stolfi, Osame Kinouchi
Phase transitions and critical behavior are crucial issues both in theoretical and experimental neuroscience. We report analytic and computational results about phase transitions and self-organized criticality (SOC) in networks with general stochastic neurons. The stochastic neuron has a firing probability given by a smooth monotonic function Φ(V) of the membrane potential V, rather than a sharp firing threshold. We find that such networks can operate in several dynamic regimes (phases) depending on the average synaptic weight and the shape of the firing function Φ...
November 7, 2016: Scientific Reports
Jane Roskams, Zoran Popović
Global neuroscience projects are producing big data at an unprecedented rate that informatic and artificial intelligence (AI) analytics simply cannot handle. Online games, like Foldit, Eterna, and Eyewire-and now a new neuroscience game, Mozak-are fueling a people-powered research science (PPRS) revolution, creating a global community of "new experts" that over time synergize with computational efforts to accelerate scientific progress, empowering us to use our collective cerebral talents to drive our understanding of our brain...
November 2, 2016: Neuron
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