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https://www.readbyqxmd.com/read/29054036/snava-a-real-time-multi-fpga-multi-model-spiking-neural-network-simulation-architecture
#1
Athul Sripad, Giovanny Sanchez, Mireya Zapata, Vito Pirrone, Taho Dorta, Salvatore Cambria, Albert Marti, Karthikeyan Krishnamourthy, Jordi Madrenas
Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources...
October 5, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29051550/studying-the-role-of-axon-fasciculation-during-development-in-a-computational-model-of-the-xenopus-tadpole-spinal-cord
#2
Oliver Davis, Robert Merrison-Hort, Stephen R Soffe, Roman Borisyuk
During nervous system development growing axons can interact with each other, for example by adhering together in order to produce bundles (fasciculation). How does such axon-axon interaction affect the resulting axonal trajectories, and what are the possible benefits of this process in terms of network function? In this paper we study these questions by adapting an existing computational model of the development of neurons in the Xenopus tadpole spinal cord to include interactions between axons. We demonstrate that even relatively weak attraction causes bundles to appear, while if axons weakly repulse each other their trajectories diverge such that they fill the available space...
October 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29049910/an-improved-synchronization-likelihood-method-for-quantifying-neuronal-synchrony
#3
Sina Khanmohammadi
Indirect quantification of the synchronization between two dynamical systems from measured experimental data has gained much attention in recent years, especially in the computational neuroscience community where the exact model of the neuronal dynamics is unknown. In this regard, one of the most promising methods for quantifying the interrelationship between nonlinear non-stationary systems is known as Synchronization Likelihood (SL), which is based on the likelihood of the auto-recurrence of embedding vectors (similar patterns) in multiple dynamical systems...
October 3, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29046631/development-of-a-bayesian-estimator-for-audio-visual-integration-a-neurocomputational-study
#4
Mauro Ursino, Andrea Crisafulli, Giuseppe di Pellegrino, Elisa Magosso, Cristiano Cuppini
The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli...
2017: Frontiers in Computational Neuroscience
https://www.readbyqxmd.com/read/29043978/a-unified-internal-model-theory-to-resolve-the-paradox-of-active-versus-passive-self-motion-sensation
#5
Jean Laurens, Dora E Angelaki
Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally-generated ('passive') movements. However, these neurons show reduced responses during self-generated ('active') movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously-established model of optimal passive self-motion estimation...
October 18, 2017: ELife
https://www.readbyqxmd.com/read/29042296/computations-in-the-deep-vs-superficial-layers-of-the-cerebral-cortex
#6
Edmund T Rolls, W Patrick C Mills
A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex...
October 14, 2017: Neurobiology of Learning and Memory
https://www.readbyqxmd.com/read/29040412/random-recurrent-networks-near-criticality-capture-the-broadband-power-distribution-of-human-ecog-dynamics
#7
Rishidev Chaudhuri, Biyu J He, Xiao-Jing Wang
Brain electric field potentials are dominated by an arrhythmic broadband signal, but the underlying mechanism is poorly understood. Here we propose that broadband power spectra characterize recurrent neural networks of nodes (neurons or clusters of neurons), endowed with an effective balance between excitation and inhibition tuned to keep the network on the edge of dynamical instability. These networks show a fast mode reflecting local dynamics and a slow mode emerging from distributed recurrent connections...
October 13, 2017: Cerebral Cortex
https://www.readbyqxmd.com/read/29040294/towards-social-autonomous-vehicles-efficient-collision-avoidance-scheme-using-richardson-s-arms-race-model
#8
Faisal Riaz, Muaz A Niazi
This paper presents the concept of a social autonomous agent to conceptualize such Autonomous Vehicles (AVs), which interacts with other AVs using social manners similar to human behavior. The presented AVs also have the capability of predicting intentions, i.e. mentalizing and copying the actions of each other, i.e. mirroring. Exploratory Agent Based Modeling (EABM) level of the Cognitive Agent Based Computing (CABC) framework has been utilized to design the proposed social agent. Furthermore, to emulate the functionality of mentalizing and mirroring modules of proposed social agent, a tailored mathematical model of the Richardson's arms race model has also been presented...
2017: PloS One
https://www.readbyqxmd.com/read/29035221/spiking-neural-p-systems-with-scheduled-synapses
#9
Francis George C Cabarle, Henry N Adorna, Min Jiang, Xiangxiang Zeng
Spiking neural P systems (in short, SN P systems) are models of computation inspired by biological spiking neurons. SN P systems have neurons as spike processors which are placed on the nodes of a directed and static graph (the edges in the graph are the synapses). In this work, we introduce a variant called SN P systems with scheduled synapses (in short, SSN P systems). SSN P systems are inspired and motivated by the structural dynamism of biological synapses, while incorporating ideas from nonstatic (i.e...
October 16, 2017: IEEE Transactions on Nanobioscience
https://www.readbyqxmd.com/read/29035203/developing-a-nonstationary-computational-framework-with-application-to-modeling-dynamic-modulations-in-neural-spiking-responses
#10
Amir Akbarian, Kaiser Niknam, Mohammadbagher Parsa, Kelsey Clark, Behrad Noudoost, Neda Nategh
OBJECTIVE: This paper aims to develop a computational model that incorporates the functional effects of modulatory covariates (such as context, task, or behavior), which dynamically alter the relationship between the stimulus and the neural response. METHODS: We develop a general computational approach along with an efficient estimation procedure in the widely used generalized linear model (GLM) framework to characterize such nonstationary dynamics in spiking response and spatiotemporal characteristics of a neuron at the level of individual trials...
October 13, 2017: IEEE Transactions on Bio-medical Engineering
https://www.readbyqxmd.com/read/29029962/answering-schr%C3%A3-dinger-s-question-a-free-energy-formulation
#11
REVIEW
Maxwell James Désormeau Ramstead, Paul Benjamin Badcock, Karl John Friston
The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales...
September 20, 2017: Physics of Life Reviews
https://www.readbyqxmd.com/read/29028209/digital-implementation-of-the-two-compartmental-pinsky-rinzel-pyramidal-neuron-model
#12
Elahe Rahimian, Soheil Zabihi, Mahmood Amiri, Bernabe Linares-Barranco
It is believed that brain-like computing system can be achieved by the fusion of electronics and neuroscience. In this way, the optimized digital hardware implementation of neurons, primary units of nervous system, play a vital role in neuromorphic applications. Moreover, one of the main features of pyramidal neurons in cortical areas is bursting activities that has a critical role in synaptic plasticity. The Pinsky-Rinzel model is a nonlinear two-compartmental model for CA3 pyramidal cell that is widely used in neuroscience...
October 12, 2017: IEEE Transactions on Biomedical Circuits and Systems
https://www.readbyqxmd.com/read/29024221/dendrites-deep-learning-and-sequences-in-the-hippocampus
#13
Upinder S Bhalla
The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory...
October 12, 2017: Hippocampus
https://www.readbyqxmd.com/read/29023523/the-role-of-glutamate-in-neuronal-ion-homeostasis-a-case-study-of-spreading-depolarization
#14
Niklas Hübel, Mahshid S Hosseini-Zare, Jokūbas Žiburkus, Ghanim Ullah
Simultaneous changes in ion concentrations, glutamate, and cell volume together with exchange of matter between cell network and vasculature are ubiquitous in numerous brain pathologies. A complete understanding of pathological conditions as well as normal brain function, therefore, hinges on elucidating the molecular and cellular pathways involved in these mostly interdependent variations. In this paper, we develop the first computational framework that combines the Hodgkin-Huxley type spiking dynamics, dynamic ion concentrations and glutamate homeostasis, neuronal and astroglial volume changes, and ion exchange with vasculature into a comprehensive model to elucidate the role of glutamate uptake in the dynamics of spreading depolarization (SD)-the electrophysiological event underlying numerous pathologies including migraine, ischemic stroke, aneurysmal subarachnoid hemorrhage, intracerebral hematoma, and trauma...
October 12, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29022943/simultaneous-measurement-of-sleep-and-feeding-in-individual-drosophila
#15
Keith R Murphy, Jin Hong Park, Robert Huber, William W Ja
Drosophila is widely used for the dissection of genetic and neuronal mechanisms of behavior. Recently, flies have emerged as a model for investigating the regulation of feeding and sleep. Although typically studied in isolation, increasing evidence points to a fundamental connection between these behaviors. Thus, a system for measuring sleep and feeding simultaneously in a single integrated system is important for interpreting behavioral shifts of either state. Here, we describe the construction and use of the Activity Recording Capillary Feeder or CAFE (ARC), a machine-vision (automated image tracking)-based system for the integrated measurement of sleep and feeding in individual Drosophila...
November 2017: Nature Protocols
https://www.readbyqxmd.com/read/29020921/identify-huntington-s-disease-associated-genes-based-on-restricted-boltzmann-machine-with-rna-seq-data
#16
Xue Jiang, Han Zhang, Feng Duan, Xiongwen Quan
BACKGROUND: Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, traditional statistic-based methods are not suitable for identifying key genes related to the disease development. Recent studies have shown that the computational models with deep structure can learn automatically the features of biological data, which is useful for exploring the characteristics of gene expression during the disease progression...
October 11, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29019687/simulated-dynamics-of-glycans-on-ligand-binding-domain-of-nmda-receptors-reveals-strong-dynamic-coupling-between-glycans-and-protein-core
#17
Anton V Sinitskiy, Vijay S Pande
N-methyl-D-aspartate (NMDA) receptors, key neuronal receptors playing the central role in learning and memory, are heavily glycosylated in vivo. Astonishingly little is known about the structure, dynamics and physiological relevance of glycans attached to them. We recently demonstrated that certain glycans on the ligand binding domain (LBD) of NMDA receptors (NMDARs) can serve as intramolecular potentiators, changing EC50 of NMDAR co-agonists. In this work, we use molecular dynamics trajectories, in aggregate 86...
October 11, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28993878/fuzzy-neuronal-model-of-motor-control-inspired-by-cerebellar-pathways-to-online-and-gradually-learn-inverse-biomechanical-functions-in-the-presence-of-delay
#18
Armin Salimi-Badr, Mohammad Mehdi Ebadzadeh, Christian Darlot
Contrary to forward biomechanical functions, which are deterministic, inverse biomechanical functions are generally not. Calculating an inverse biomechanical function is an ill-posed problem, which has no unique solution for a manipulator with several degrees of freedom. Studies of the command and control of biological movements suggest that the cerebellum takes part in the computation of approximate inverse functions, and this ability can control fast movements by predicting the consequence of current motor command...
October 9, 2017: Biological Cybernetics
https://www.readbyqxmd.com/read/28993204/a-stepwise-neuron-model-fitting-procedure-designed-for-recordings-with-high-spatial-resolution-application-to-layer-5-pyramidal-cells
#19
Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M Dale, Ole A Andreassen, Gaute T Einevoll
BACKGROUND: Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level...
October 7, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/28986881/development-of-a-computational-approach-model-to-explore-nmda-receptors-functions
#20
A Florence Keller, Jean-Marie C Bouteiller, Theodore W Berger
Modern laboratory techniques allow studying NMDA receptors (NMDAR) either anatomically with specific antibodies coupled to sophisticated confocal microscopy, or physiologically by live imaging or electrophysiological techniques. However, NMDARs are not fixed in time and space and changes in their composition and/or distribution on the post-synaptic membrane may significantly impact the synaptic strength and overall function. The computational modeling approach therefore constitutes a complementary tool for investigating the properties of biological systems based on the knowledge provided by the lab experiments...
2017: Methods in Molecular Biology
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