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Journal of Computational Neuroscience

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
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...
February 20, 2018: Journal of Computational Neuroscience
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...
January 31, 2018: Journal of Computational Neuroscience
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...
January 25, 2018: Journal of Computational Neuroscience
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...
January 11, 2018: Journal of Computational Neuroscience
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
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
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...
December 8, 2017: Journal of Computational Neuroscience
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...
December 5, 2017: Journal of Computational Neuroscience
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...
December 1, 2017: Journal of Computational Neuroscience
Horacio G Rotstein
The generation of spiking resonances in neurons (preferred spiking responses to oscillatory inputs) requires the interplay of the intrinsic ionic currents that operate at the subthreshold voltage level and the spiking mechanisms. Combinations of the same types of ionic currents in different parameter regimes may give rise to different types of nonlinearities in the voltage equation (e.g., parabolic- and cubic-like), generating subthreshold (membrane potential) oscillations patterns with different properties...
December 2017: Journal of Computational Neuroscience
Armin Salimi-Badr, Mohammad Mehdi Ebadzadeh, Christian Darlot
The main hypothesis of this study, based on experimental data showing the relations between the BG activities and kinematic variables, is that BG are involved in computing inverse kinematics (IK) as a part of planning and decision-making. Indeed, it is assumed that based on the desired kinematic variables (such as velocity) of a limb in the workspace, angular kinematic variables in the joint configuration space are calculated. Therefore, in this paper, a system-level computational model of BG is proposed based on geometrical rules, which is able to compute IK...
December 2017: Journal of Computational Neuroscience
Jung Hoon Lee
The pathophysiology of auditory hallucination, a common symptom of schizophrenia, has yet been understood, but during auditory hallucination, primary auditory cortex (A1) shows paradoxical responses. When auditory stimuli are absent, A1 becomes hyperactive, while A1 responses to auditory stimuli are reduced. Such activation pattern of A1 responses during auditory hallucination is consistent with aberrant gamma rhythms in schizophrenia observed during auditory tasks, raising the possibility that the pathology underlying abnormal gamma rhythms can account for auditory hallucination...
December 2017: Journal of Computational Neuroscience
Grant Gillary, Rüdiger von der Heydt, Ernst Niebur
Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient...
December 2017: Journal of Computational Neuroscience
Denggui Fan, Qingyun Wang, Jianzhong Su, Hongguang Xi
It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation...
December 2017: Journal of Computational Neuroscience
Brian Hu, Ernst Niebur
Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information...
December 2017: Journal of Computational Neuroscience
Pachaya Sailamul, Jaeson Jang, Se-Bum Paik
Correlated neural activities such as synchronizations can significantly alter the characteristics of spike transfer between neural layers. However, it is not clear how this synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. To address this question, we implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance...
December 2017: Journal of Computational Neuroscience
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...
November 20, 2017: Journal of Computational Neuroscience
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...
November 15, 2017: Journal of Computational Neuroscience
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...
November 14, 2017: Journal of Computational Neuroscience
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