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Journal of Computational Neuroscience

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https://www.readbyqxmd.com/read/29766393/linear-feature-projection-based-real-time-decoding-of-limb-state-from-dorsal-root-ganglion-recordings
#1
Sungmin Han, Jun-Uk Chu, Jong Woong Park, Inchan Youn
Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints...
May 15, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29752691/analytical-modelling-of-temperature-effects-on-an-ampa-type-synapse
#2
Dominik S Kufel, Grzegorz M Wojcik
It was previously reported, that temperature may significantly influence neural dynamics on the different levels of brain function. Thus, in computational neuroscience, it would be useful to make models scalable for a wide range of various brain temperatures. However, lack of experimental data and an absence of temperature-dependent analytical models of synaptic conductance does not allow to include temperature effects at the multi-neuron modeling level. In this paper, we propose a first step to deal with this problem: A new analytical model of AMPA-type synaptic conductance, which is able to incorporate temperature effects in low-frequency stimulations...
May 11, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29666978/spike-timing-precision-of-neuronal-circuits
#3
Deniz Kilinc, Alper Demir
Spike timing is believed to be a key factor in sensory information encoding and computations performed by the neurons and neuronal circuits. However, the considerable noise and variability, arising from the inherently stochastic mechanisms that exist in the neurons and the synapses, degrade spike timing precision. Computational modeling can help decipher the mechanisms utilized by the neuronal circuits in order to regulate timing precision. In this paper, we utilize semi-analytical techniques, which were adapted from previously developed methods for electronic circuits, for the stochastic characterization of neuronal circuits...
April 17, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29616382/phase-model-based-neuron-stabilization-into-arbitrary-clusters
#4
Timothy D Matchen, Jeff Moehlis
Deep brain stimulation (DBS) is a common method of combating pathological conditions associated with Parkinson's disease, Tourette syndrome, essential tremor, and other disorders, but whose mechanisms are not fully understood. One hypothesis, supported experimentally, is that some symptoms of these disorders are associated with pathological synchronization of neurons in the basal ganglia and thalamus. For this reason, there has been interest in recent years in finding efficient ways to desynchronize neurons that are both fast-acting and low-power...
April 3, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29589252/the-role-of-phase-shifts-of-sensory-inputs-in-walking-revealed-by-means-of-phase-reduction
#5
Azamat Yeldesbay, Tibor Tóth, Silvia Daun
Detailed neural network models of animal locomotion are important means to understand the underlying mechanisms that control the coordinated movement of individual limbs. Daun-Gruhn and Tóth, Journal of Computational Neuroscience 31(2), 43-60 (2011) constructed an inter-segmental network model of stick insect locomotion consisting of three interconnected central pattern generators (CPGs) that are associated with the protraction-retraction movements of the front, middle and hind leg. This model could reproduce the basic locomotion coordination patterns, such as tri- and tetrapod, and the transitions between them...
March 27, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29574632/an-integrate-and-fire-model-to-generate-spike-trains-with-long-range-dependence
#6
Alexandre Richard, Patricio Orio, Etienne Tanré
Long-range dependence (LRD) has been observed in a variety of phenomena in nature, and for several years also in the spiking activity of neurons. Often, this is interpreted as originating from a non-Markovian system. Here we show that a purely Markovian integrate-and-fire (IF) model, with a noisy slow adaptation term, can generate interspike intervals (ISIs) that appear as having LRD. However a proper analysis shows that this is not the case asymptotically. For comparison, we also consider a new model of individual IF neuron with fractional (non-Markovian) noise...
March 24, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29546529/synaptic-efficacy-shapes-resource-limitations-in-working-memory
#7
Nikhil Krishnan, Daniel B Poll, Zachary P Kilpatrick
Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space...
March 15, 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29464489/learning-neural-connectivity-from-firing-activity-efficient-algorithms-with-provable-guarantees-on-topology
#8
Amin Karbasi, Amir Hesam Salavati, Martin Vetterli
The connectivity of a neuronal network has a major effect on its functionality and role. It is generally believed that the complex network structure of the brain provides a physiological basis for information processing. Therefore, identifying the network's topology has received a lot of attentions in neuroscience and has been the center of many research initiatives such as Human Connectome Project. Nevertheless, direct and invasive approaches that slice and observe the neural tissue have proven to be time consuming, complex and costly...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29387993/perceptual-judgments-via-sensory-motor-interaction-assisted-by-cortical-gaba
#9
Osamu Hoshino, Meihong Zheng, Kazuo Watanabe
Recurrent input to sensory cortex, via long-range reciprocal projections between motor and sensory cortices, is essential for accurate perceptual judgments. GABA levels in sensory cortices correlate with perceptual performance. We simulated a neuron-astrocyte network model to investigate how top-down, feedback signaling from a motor network (Nmot) to a sensory network (Nsen) affects perceptual judgments in association with ambient (extracellular) GABA levels. In the Nsen, astrocytic transporters modulated ambient GABA levels around pyramidal cells...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29372434/linearization-of-excitatory-synaptic-integration-at-no-extra-cost
#10
Danielle Morel, Chandan Singh, William B Levy
In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29327161/effects-of-channel-blocking-on-information-transmission-and-energy-efficiency-in-squid-giant-axons
#11
Yujiang Liu, Yuan Yue, Yuguo Yu, Liwei Liu, Lianchun Yu
Action potentials are the information carriers of neural systems. The generation of action potentials involves the cooperative opening and closing of sodium and potassium channels. This process is metabolically expensive because the ions flowing through open channels need to be restored to maintain concentration gradients of these ions. Toxins like tetraethylammonium can block working ion channels, thus affecting the function and energy cost of neurons. In this paper, by computer simulation of the Hodgkin-Huxley neuron model, we studied the effects of channel blocking with toxins on the information transmission and energy efficiency in squid giant axons...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29222729/coding-of-time-dependent-stimuli-in-homogeneous-and-heterogeneous-neural-populations
#12
Manuel Beiran, Alexandra Kruscha, Jan Benda, Benjamin Lindner
We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29210004/a-mathematical-model-of-recurrent-spreading-depolarizations
#13
Cameron Conte, Ray Lee, Monica Sarkar, David Terman
A detailed biophysical model for a neuron/astrocyte network is developed in order to explore mechanisms responsible for the initiation and propagation of recurrent cortical spreading depolarizations. The model incorporates biophysical processes not considered in the earlier models. This includes a model for the Na+ -glutamate transporter, which allows for a detailed description of reverse glutamate uptake. In particular, we consider the specific roles of elevated extracellular glutamate and K+ in the initiation, propagation and recurrence of spreading depolarizations...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29192377/how-does-transient-signaling-input-affect-the-spike-timing-of-postsynaptic-neuron-near-the-threshold-regime-an-analytical-study
#14
Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, Seyyed Nader Rasuli
The noisy threshold regime, where even a small set of presynaptic neurons can significantly affect postsynaptic spike-timing, is suggested as a key requisite for computation in neurons with high variability. It also has been proposed that signals under the noisy conditions are successfully transferred by a few strong synapses and/or by an assembly of nearly synchronous synaptic activities. We analytically investigate the impact of a transient signaling input on a leaky integrate-and-fire postsynaptic neuron that receives background noise near the threshold regime...
April 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29230640/new-class-of-reduced-computationally-efficient-neuronal-models-for-large-scale-simulations-of-brain-dynamics
#15
Maxim Komarov, Giri Krishnan, Sylvain Chauvette, Nikolai Rulkov, Igor Timofeev, Maxim Bazhenov
During slow-wave sleep, brain electrical activity is dominated by the slow (< 1 Hz) electroencephalogram (EEG) oscillations characterized by the periodic transitions between active (or Up) and silent (or Down) states in the membrane voltage of the cortical and thalamic neurons. Sleep slow oscillation is believed to play critical role in consolidation of recent memories. Past computational studies, based on the Hodgkin-Huxley type neuronal models, revealed possible intracellular and network mechanisms of the neuronal activity during sleep, however, they failed to explore the large-scale cortical network dynamics depending on collective behavior in the large populations of neurons...
February 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29152668/the-role-of-astrocytic-calcium-and-trpv4-channels-in-neurovascular-coupling
#16
Allanah Kenny, Michael J Plank, Tim David
Neuronal activity evokes a localised change in cerebral blood flow in a response known as neurovascular coupling (NVC). Although NVC has been widely studied the exact mechanisms that mediate this response remain unclear; in particular the role of astrocytic calcium is controversial. Mathematical modelling can be a useful tool for investigating the contribution of various signalling pathways towards NVC and for analysing the underlying cellular mechanisms. The lumped parameter model of a neurovascular unit with both potassium and nitric oxide (NO) signalling pathways and comprised of neurons, astrocytes, and vascular cells has been extended to include the glutamate induced astrocytic calcium pathway with epoxyeicosatrienoic acid (EET) signalling and the stretch dependent TRPV4 calcium channel on the astrocytic endfoot...
February 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29143250/cliques-and-cavities-in-the-human-connectome
#17
Ann E Sizemore, Chad Giusti, Ari Kahn, Jean M Vettel, Richard F Betzel, Danielle S Bassett
Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large, distributed networks of brain areas, principled examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing...
February 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29139050/modeling-mesoscopic-cortical-dynamics-using-a-mean-field-model-of-conductance-based-networks-of-adaptive-exponential-integrate-and-fire-neurons
#18
Yann Zerlaut, Sandrine Chemla, Frederic Chavane, Alain Destexhe
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons...
February 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29139049/feedforward-architectures-driven-by-inhibitory-interactions
#19
Yazan N Billeh, Michael T Schaub
Directed information transmission is paramount for many social, physical, and biological systems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neurons play only a stabilizing role for the network dynamics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the diversity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random...
February 2018: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29124505/transitions-between-asynchronous-and-synchronous-states-a-theory-of-correlations-in-small-neural-circuits
#20
Diego Fasoli, Anna Cattani, Stefano Panzeri
The study of correlations in neural circuits of different size, from the small size of cortical microcolumns to the large-scale organization of distributed networks studied with functional imaging, is a topic of central importance to systems neuroscience. However, a theory that explains how the parameters of mesoscopic networks composed of a few tens of neurons affect the underlying correlation structure is still missing. Here we consider a theory that can be applied to networks of arbitrary size with multiple populations of homogeneous fully-connected neurons, and we focus its analysis to a case of two populations of small size...
February 2018: Journal of Computational Neuroscience
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