Results 11 to 20 of about 25,328 (252)
Linear Response of General Observables in Spiking Neuronal Network Models. [PDF]
Entropy (Basel), 2021 Cessac B, Ampuero I, Cofré R.
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Clustering predicts memory performance in networks of spiking and non-spiking neurons [PDF]
Frontiers in Computational Neuroscience, 2011 The problem we address in this paper is that of finding effective and parsimonious patterns of connectivity in sparse associative memories. This problem must be addressed in real neuronal systems, so that results in artificial systems could throw light ...
Weiliang eChen +6 more
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Memristive Izhikevich Spiking Neuron Model and Its Application in Oscillatory Associative Memory
Frontiers in Neuroscience, 2022 The Izhikevich (IZH) spiking neuron model can display spiking and bursting behaviors of neurons. Based on the switching property and bio-plausibility of the memristor, the memristive Izhikevich (MIZH) spiking neuron model is built.
Xiaoyan Fang, Shukai Duan, Lidan Wang
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Bumps and oscillons in networks of spiking neurons [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020 We study localized patterns in an exact mean-field description of a spatially extended network of quadratic integrate-and-fire neurons. We investigate conditions for the existence and stability of localized solutions, so-called bumps, and give an analytic estimate for the parameter range, where these solutions exist in parameter space, when one or more
Helmut Schmidt, Daniele Avitabile
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Belief Propagation in Networks of Spiking Neurons [PDF]
Neural Computation, 2009 From a theoretical point of view, statistical inference is an attractive model of brain operation. However, it is unclear how to implement these inferential processes in neuronal networks. We offer a solution to this problem by showing in detailed simulations how the belief propagation algorithm on a factor graph can be embedded in a network of ...
Andreas Steimer +2 more
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Information capacity of a network of spiking neurons [PDF]
Physica A: Statistical Mechanics and its Applications, 2020 We study a model of spiking neurons, with recurrent connections that result from learning a set of spatio-temporal patterns with a spike-timing dependent plasticity rule and a global inhibition. We investigate the ability of the network to store and selectively replay multiple patterns of spikes, with a combination of spatial population and phase-of ...
Scarpetta, Silvia, de Candia, Antonio
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Partial coupling delay induced multiple spatiotemporal orders in a modular neuronal network. [PDF]
PLoS ONE, 2017 The influence of partial coupling delay on the spatiotemporal spiking dynamics is explored in a modular neuronal network. The modular neuronal network is composed of two subnetworks which present the small-world property and scale-free property ...
XiaoLi Yang, HuiDan Li, ZhongKui Sun
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Stationary Bumps in Networks of Spiking Neurons [PDF]
Neural Computation, 2001 We examine the existence and stability of spatially localized “bumps” of neuronal activity in a network of spiking neurons. Bumps have been proposed in mechanisms of visual orientation tuning, the rat head direction system, and working memory. We show that a bump solution can exist in a spiking network provided the neurons fire asynchronously within ...
Carlo R. Laing, Carson C. Chow
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Dynamic Image Representation in a Spiking Neural Network Supplied by Astrocytes
Mathematics, 2023 The mathematical model of the spiking neural network (SNN) supplied by astrocytes is investigated. The astrocytes are a specific type of brain cells which are not electrically excitable but induce chemical modulations of neuronal firing.
Sergey V. Stasenko, Victor B. Kazantsev
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Optimization Methods for Spiking Neurons and Networks [PDF]
IEEE Transactions on Neural Networks, 2010 Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network.
Alexander F. Russell +6 more
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