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Computational neuroscience

Sergio E Galindo, Pablo Toharia, Oscar D Robles, Luis Pastor
After decades of independent morphological and functional brain research, a key point in neuroscience nowadays is to understand the combined relationships between the structure of the brain and its components and their dynamics on multiple scales, ranging from circuits of neurons at micro or mesoscale to brain regions at macroscale. With such a goal in mind, there is a vast amount of research focusing on modeling and simulating activity within neuronal structures, and these simulations generate large and complex datasets which have to be analyzed in order to gain the desired insight...
2016: Frontiers in Neuroinformatics
Julian C Shillcock, Michael Hawrylycz, Sean Hill, Hanchuan Peng
Large-scale brain initiatives such as the US BRAIN initiative and the European Human Brain Project aim to marshall a vast amount of data and tools for the purpose of furthering our understanding of brains. Fundamental to this goal is that neuronal morphologies must be seamlessly reconstructed and aggregated on scales up to the whole rodent brain. The experimental labor needed to manually produce this number of digital morphologies is prohibitively large. The BigNeuron initiative is assembling community-generated, open-source, automated reconstruction algorithms into an open platform, and is beginning to generate an increasing flow of high-quality reconstructed neurons...
February 24, 2016: Brain Informatics
Xinpei Ma, Chun-An Chou, Hiroki Sayama, Wanpracha Art Chaovalitwongse
Many neuroscience studies have been devoted to understand brain neural responses correlating to cognition using functional magnetic resonance imaging (fMRI). In contrast to univariate analysis to identify response patterns, it is shown that multi-voxel pattern analysis (MVPA) of fMRI data becomes a relatively effective approach using machine learning techniques in the recent literature. MVPA can be considered as a multi-objective pattern classification problem with the aim to optimize response patterns, in which informative voxels interacting with each other are selected, achieving high classification accuracy associated with cognitive stimulus conditions...
September 2016: Brain Informatics
Braden A W Brinkman, Alison I Weber, Fred Rieke, Eric Shea-Brown
Neural circuits reliably encode and transmit signals despite the presence of noise at multiple stages of processing. The efficient coding hypothesis, a guiding principle in computational neuroscience, suggests that a neuron or population of neurons allocates its limited range of responses as efficiently as possible to best encode inputs while mitigating the effects of noise. Previous work on this question relies on specific assumptions about where noise enters a circuit, limiting the generality of the resulting conclusions...
October 2016: PLoS Computational Biology
Johanna Metsomaa, Jukka Sarvas, Risto J Ilmoniemi
OBJECTIVE: Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain-computer interfaces, artifact removal and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses which is challenging because of non-stationary data. METHODS: We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA) which is tailored for event-related multi-trial data...
October 12, 2016: IEEE Transactions on Bio-medical Engineering
Brandon S Coventry, Aravindakshan Parthasarathy, Alexandra L Sommer, Edward L Bartlett
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons...
October 10, 2016: Journal of Computational Neuroscience
Miguel Aguilera, Manuel G Bedia, Xabier E Barandiaran
The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task...
2016: Frontiers in Systems Neuroscience
Maria Rubega, Roberto Fontana, Stefano Vassanelli, Giovanni Sparacino
The quantitative study of cross-frequency coupling (CFC) is a relevant issue in neuroscience. In local field potentials (LFPs), measured either in the cortex or in the hippocampus, how γ-oscillation amplitude is modulated by changes in θ-rhythms-phase is thought to be important in memory formation. Several methods were proposed to quantify CFC, but reported evidence suggests that experimental parameters affect the results. Therefore, a simulation tool to support the determination of minimal requirements for CFC estimation in order to obtain reliable results is particularly useful...
August 11, 2016: Network: Computation in Neural Systems
Christian Mayer, Rachel C Bandler, Gord Fishell
This Matters Arising Response paper addresses the Sultan et al. (2016) Matters Arising paper, published concurrently in Neuron. Clonally related excitatory neurons maintain a coherent relationship following their specification and migration. Whether cortical interneurons behave similarly is a fundamental question in developmental neuroscience. In Mayer et al. (2015), we reported that sibling interneurons disperse over several millimeters, across functional and anatomical boundaries. This finding demonstrated that clonality is not predictive of an interneuron's ultimate circuit specificity...
October 5, 2016: Neuron
Jesper Tegnér, Hector Zenil, Narsis A Kiani, Gordon Ball, David Gomez-Cabrero
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics...
November 13, 2016: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
John P O'Doherty, Jeffrey Cockburn, Wolfgang M Pauli
In this review, we summarize findings supporting the existence of multiple behavioral strategies for controlling reward-related behavior, including a dichotomy between the goal-directed or model-based system and the habitual or model-free system in the domain of instrumental conditioning and a similar dichotomy in the realm of Pavlovian conditioning. We evaluate evidence from neuroscience supporting the existence of at least partly distinct neuronal substrates contributing to the key computations necessary for the function of these different control systems...
September 28, 2016: Annual Review of Psychology
Adam H Marblestone, Greg Wayne, Konrad P Kording
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage...
2016: Frontiers in Computational Neuroscience
Léo Pio-Lopez, Ange Nizard, Karl Friston, Giovanni Pezzulo
Active inference is a general framework for perception and action that is gaining prominence in computational and systems neuroscience but is less known outside these fields. Here, we discuss a proof-of-principle implementation of the active inference scheme for the control or the 7-DoF arm of a (simulated) PR2 robot. By manipulating visual and proprioceptive noise levels, we show under which conditions robot control under the active inference scheme is accurate. Besides accurate control, our analysis of the internal system dynamics (e...
September 2016: Journal of the Royal Society, Interface
Ekaterina Brocke, Upinder S Bhalla, Mikael Djurfeldt, Jeanette Hellgren Kotaleski, Michael Hanke
Multiscale modeling and simulations in neuroscience is gaining scientific attention due to its growing importance and unexplored capabilities. For instance, it can help to acquire better understanding of biological phenomena that have important features at multiple scales of time and space. This includes synaptic plasticity, memory formation and modulation, homeostasis. There are several ways to organize multiscale simulations depending on the scientific problem and the system to be modeled. One of the possibilities is to simulate different components of a multiscale system simultaneously and exchange data when required...
2016: Frontiers in Computational Neuroscience
Orkid Coskuner
Divalent copper and zinc ions bind to the amyloid-β(40) and amyloid-β(42) alloforms and affect their structural stability as well as their chemical and physical properties. Current literature debates the impact of copper ions on amyloid-β alloforms. Recently, we reported the structural and thermodynamic properties of apo amyloid-β and divalent zinc ion bound amyloid-β alloforms (see, Wise-Scira et al. in J Biol Inorg Chem 17:927-938, 2012 and Coskuner et al. in ACS Chem Neurosci 4: 310-320, 2013). In our search for understanding the impacts of transition metal ions on disordered amyloid-β, we also developed and reported new potential functions using quantum mechanics, which are required for high-quality molecular dynamics simulations of divalent copper ion bound amyloid-β alloforms (see, Wise and Coskuner in J Comput Chem 35:1278-1289, 2014)...
September 22, 2016: Journal of Biological Inorganic Chemistry: JBIC
Bettina Sorger, Tabea Kamp, Nikolaus Weiskopf, Judith Caroline Peters, Rainer Goebel
Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i...
September 19, 2016: Neuroscience
Isabelle Lajoie, Felipe B Tancredi, Richard D Hoge
The current generation of calibrated MRI methods goes beyond simple localization of task-related responses to allow the mapping of resting-state cerebral metabolic rate of oxygen (CMRO2) in micromolar units and estimation of oxygen extraction fraction (OEF). Prior to the adoption of such techniques in neuroscience research applications, knowledge about the precision and accuracy of absolute estimates of CMRO2 and OEF is crucial and remains unexplored to this day. In this study, we addressed the question of methodological precision by assessing the regional inter-subject variance and intra-subject reproducibility of the BOLD calibration parameter M, OEF, O2 delivery and absolute CMRO2 estimates derived from a state-of-the-art calibrated BOLD technique, the QUantitative O2 (QUO2) approach...
2016: PloS One
Saeed R Kheradpisheh, Masoud Ghodrati, Mohammad Ganjtabesh, Timothée Masquelier
View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images...
2016: Frontiers in Computational Neuroscience
Yichen Lu, Hongming Lyu, Andrew G Richardson, Timothy H Lucas, Duygu Kuzum
Neural sensing and stimulation have been the backbone of neuroscience research, brain-machine interfaces and clinical neuromodulation therapies for decades. To-date, most of the neural stimulation systems have relied on sharp metal microelectrodes with poor electrochemical properties that induce extensive damage to the tissue and significantly degrade the long-term stability of implantable systems. Here, we demonstrate a flexible cortical microelectrode array based on porous graphene, which is capable of efficient electrophysiological sensing and stimulation from the brain surface, without penetrating into the tissue...
September 19, 2016: Scientific Reports
Philipp Kellmeyer, Thomas Cochrane, Oliver Müller, Christine Mitchell, Tonio Ball, Joseph J Fins, Nikola Biller-Andorno
Closed-loop medical devices such as brain-computer interfaces are an emerging and rapidly advancing neurotechnology. The target patients for brain-computer interfaces (BCIs) are often severely paralyzed, and thus particularly vulnerable in terms of personal autonomy, decisionmaking capacity, and agency. Here we analyze the effects of closed-loop medical devices on the autonomy and accountability of both persons (as patients or research participants) and neurotechnological closed-loop medical systems. We show that although BCIs can strengthen patient autonomy by preserving or restoring communicative abilities and/or motor control, closed-loop devices may also create challenges for moral and legal accountability...
October 2016: Cambridge Quarterly of Healthcare Ethics: CQ: the International Journal of Healthcare Ethics Committees
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