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Network: Computation in Neural Systems

Wei Qin, Alex Hadjinicolaou, David B Grayden, Hamish Meffin, Anthony N Burkitt, Michael R Ibbotson, Tatiana Kameneva
There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs...
2017: Network: Computation in Neural Systems
A E Macias-Medri, Jacinto A Liendo, Ricardo J Silva
A hybrid simulation model (macro-molecular dynamics and Monte Carlo method) is proposed to reproduce neurosecretion and exocytosis. A theory has been developed for vesicular dynamics based on quasi-static electric interactions and a simple transition-state model for the vesicular fusion. Under the non-equilibrium electric conditions in an electrolytic fluid, it is considered that the motion of each synaptic vesicle is influenced by electrostatic forces exerted by the membranes of the synaptic bouton, other vesicles, the intracellular and intravesicular fluids, and external elements to the neuron...
2017: Network: Computation in Neural Systems
Yüksel Çakir
A network model of striatum that comprises medium spiny neurons (MSNs) and fast spiking interneurons (FSIs) is constructed following the work of Humphries et al. (2009). The dynamic behavior of striatum microcircuit is investigated using a dopamine-modulated modified Izhikevich neuron model. The influences of dopamine on the synchronization behavior of the striatal microcircuit and the dependence on receptor type are investigated with and without time delay. To investigate the role of two types of dopamine receptors, D1 and D2, on the overall activity of the striatum microcircuit, the activities of two groups are considered as disconnected and connected...
2017: Network: Computation in Neural Systems
Jagriti Saini, Maitreyee Dutta
Epilepsy is considered as fourth most prominent neurological disorder in the world that can affect people of all age groups. Currently, around 65 million people throughout the world are suffering from epilepsy. It is evident that electroencephalograph (EEG) signals are most commonly used for detection of epileptic seizures but today many modern techniques have been developed to analyze underlying features of these EEG signals. As EEG contains a large amount of complicated information, so many researchers are trying to develop automatic systems for complete feature extraction...
2017: Network: Computation in Neural Systems
Yüksel Çakir
Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated...
2016: Network: Computation in Neural Systems
Bernhard U Seeber, Ian C Bruce
This special issue of Network: Computation in Neural Systems on the topic of "Computational models of the electrically stimulated auditory system" incorporates review articles spanning a wide range of approaches to modeling cochlear implant stimulation of the auditory system. The purpose of this overview paper is to provide a historical context for the different modeling endeavors and to point toward how computational modeling could play a key role in the understanding, evaluation, and improvement of cochlear implants in the future...
2016: Network: Computation in Neural Systems
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...
2016: Network: Computation in Neural Systems
Robin S Weiss, Andrej Voss, Werner Hemmert
This review evaluates the potential of optogenetic methods for the stimulation of the auditory nerve and assesses the feasability of optogenetic cochlear implants (CIs). It provides an overview of all critical steps like opsin targeting strategies, how opsins work, how their function can be modeled and included in neuronal models and the properties of light sources available for optical stimulation. From these foundations, quantitative estimates for the number of independent stimulation channels and the temporal precision of optogenetic stimulation of the auditory nerve are derived and compared with state-of-the-art electrical CIs...
2016: Network: Computation in Neural Systems
Marko Takanen, Ian C Bruce, Bernhard U Seeber
Auditory nerve fibers (ANFs) play a crucial role in hearing by encoding and transporting the synaptic input from inner hair cells into afferent spiking information for higher stages of the auditory system. If the inner hair cells are degenerated, cochlear implants may restore hearing by directly stimulating the ANFs. The response of an ANF is affected by several characteristics of the electrical stimulus and of the ANF, and neurophysiological measurements are needed to know how the ANF responds to a particular stimulus...
2016: Network: Computation in Neural Systems
Mathias Dietz
In an increasing number of countries, the standard treatment for deaf individuals is moving toward the implantation of two cochlear implants. Today's device technology and fitting procedure, however, appears as if the two implants would serve two independent ears and brains. Many experimental studies have demonstrated that after careful matching and balancing of left and right stimulation in controlled laboratory studies most patients have almost normal sensitivity to interaural level differences and some sensitivity to interaural time differences (ITDs)...
2016: Network: Computation in Neural Systems
Thirunavukkarasu Radhika, Gnaneswaran Nagamani
In this paper, based on the knowledge of memristor-based recurrent neural networks (MRNNs), the model of the stochastic MRNNs with discrete and distributed delays is established. In real nervous systems and in the implementation of very large-scale integration (VLSI) circuits, noise is unavoidable, which leads to the stochastic model of the MRNNs. In this model, the delay interval is decomposed into two subintervals by using the tuning parameter α such that 0 < α < 1. By constructing proper Lyapunov-Krasovskii functional and employing direct delay decomposition technique, several sufficient conditions are given to guarantee the dissipativity and passivity of the stochastic MRNNs with discrete and distributed delays in the sense of Filippov solutions...
2016: Network: Computation in Neural Systems
Juan M Galeazzi, Joaquín Navajas, Bedeho M W Mender, Rodrigo Quian Quiroga, Loredana Minini, Simon M Stringer
Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device...
2016: Network: Computation in Neural Systems
Maryam Songhorzadeh, Karim Ansari-Asl, Alimorad Mahmoudi
Causal interaction estimation among neuronal groups plays an important role in the assessment of brain functions. These directional relations can be best illustrated by means of graphical modeling which is a mathematical representation of a network. Here, we propose an efficient framework to derive a graphical model for the statistical analysis of multivariate processes from observed time series in a data-driven pipeline to explore the interregional brain interactions. A major part of this analysis is devoted to the graph link estimation, which is a measure capable of dealing with the multivariate analysis obstacles...
2016: Network: Computation in Neural Systems
Tania Hanekom, Johan J Hanekom
Three-dimensional (3D) computational modeling of the auditory periphery forms an integral part of modern-day research in cochlear implants (CIs). These models consist of a volume conduction description of implanted stimulation electrodes and the current distribution around these, coupled with auditory nerve fiber models. Cochlear neural activation patterns can then be predicted for a given input stimulus. The objective of this article is to present the context of 3D modeling within the field of CIs, the different models, and approaches to models that have been developed over the years, as well as the applications and potential applications of these models...
2016: Network: Computation in Neural Systems
Randy K Kalkman, Jeroen J Briaire, Johan H M Frijns
Since the 1970s, computational modeling has been used to investigate the fundamental mechanisms of cochlear implant stimulation. Lumped parameter models and analytical models have been used to simulate cochlear potentials, as well as three-dimensional volume conduction models based on the Finite Difference, Finite Element, and Boundary Element methods. Additionally, in order to simulate neural responses, several of these cochlear models have been combined with nerve models, which were either simple activation functions or active nerve fiber models of the cochlear auditory neurons...
2016: Network: Computation in Neural Systems
Gabrielle E O'Brien, Jay T Rubinstein
In the last few decades, biophysical models have emerged as a prominent tool in the study and improvement of cochlear implants, a neural prosthetic that restores a degree of sound perception to the profoundly deaf. Owing to the spatial phenomena associated with extracellular stimulation, these models have evolved to a relatively high degree of morphological and physiological detail: single-node models in the tradition of Hodgkin-Huxley are paired with cable descriptions of the auditory nerve fiber. No singular model has emerged as a frontrunner to the field; rather, parameter sets deriving from the channel kinetics and morphologies of numerous organisms (mammalian and otherwise) are combined and tuned to foster strong agreement with response properties observed in vivo, such as refractoriness, summation, and strength-duration relationships...
2016: Network: Computation in Neural Systems
Nasir Ali Kant, Mohamad Rafiq Dar, Farooq Ahmad Khanday
The output of every neuron in neural network is specified by the employed activation function (AF) and therefore forms the heart of neural networks. As far as the design of artificial neural networks (ANNs) is concerned, hardware approach is preferred over software one because it promises the full utilization of the application potential of ANNs. Therefore, besides some arithmetic blocks, designing AF in hardware is the most important for designing ANN. While attempting to design the AF in hardware, the designs should be compatible with the modern Very Large Scale Integration (VLSI) design techniques...
2015: Network: Computation in Neural Systems
Faizul Azam, Najah Mohamed, Fatma Alhussen
Green tea catechins have extensively been studied for their imminent role in reducing the risk of various neurodegenerative diseases such as Parkinson's disease (PD). Understanding the molecular interaction of these compounds with various anti-Parkinsonian drug targets is of interest. The present study is intended to explore binding modes of catechins with molecular targets having potential role in PD. Lamarckian genetic algorithm methodology was adopted for molecular docking simulations employing AutoDock 4...
2015: Network: Computation in Neural Systems
M Syed Ali, M Esther Rani
This paper investigates the problem of robust passivity of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters. To reflect most of the dynamical behaviors of the system, both parameter uncertainties and stochastic disturbances are considered; stochastic disturbances are given in the form of a Brownian motion. By utilizing the Lyapunov functional method, the Itô differential rule, and matrix analysis techniques, we establish a sufficient criterion such that, for all admissible parameter uncertainties and stochastic disturbances, the stochastic neural network is robustly passive in the sense of expectation...
2015: Network: Computation in Neural Systems
Mark D McDonnell, Nicolangelo Iannella, Minh-Son To, Henry C Tuckwell, Jürgen Jost, Boris S Gutkin, Lawrence M Ward
Stochastic resonance (SR) is said to be observed when the presence of noise in a nonlinear system enables an output signal from the system to better represent some feature of an input signal than it does in the absence of noise. The effect has been observed in models of individual neurons, and in experiments performed on real neural systems. Despite the ubiquity of biophysical sources of stochastic noise in the nervous system, however, it has not yet been established whether neuronal computation mechanisms involved in performance of specific functions such as perception or learning might exploit such noise as an integral component, such that removal of the noise would diminish performance of these functions...
2015: Network: Computation in Neural Systems
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