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      Spike-burst chimera states in an adaptive exponential integrate-and-fire neuronal network

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          Most cited references31

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          Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

          We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. We describe a systematic method to estimate its parameters with simple electrophysiological protocols (current-clamp injection of pulses and ramps) and apply it to a detailed conductance-based model of a regular spiking neuron. Our simple model predicts correctly the timing of 96% of the spikes (+/-2 ms) of the detailed model in response to injection of noisy synaptic conductances. The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro.
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            Firing patterns in the adaptive exponential integrate-and-fire model

            For simulations of large spiking neuron networks, an accurate, simple and versatile single-neuron modeling framework is required. Here we explore the versatility of a simple two-equation model: the adaptive exponential integrate-and-fire neuron. We show that this model generates multiple firing patterns depending on the choice of parameter values, and present a phase diagram describing the transition from one firing type to another. We give an analytical criterion to distinguish between continuous adaption, initial bursting, regular bursting and two types of tonic spiking. Also, we report that the deterministic model is capable of producing irregular spiking when stimulated with constant current, indicating low-dimensional chaos. Lastly, the simple model is fitted to real experiments of cortical neurons under step current stimulation. The results provide support for the suitability of simple models such as the adaptive exponential integrate-and-fire neuron for large network simulations.
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              Chimera States in populations of nonlocally coupled chemical oscillators.

              Chimera states occur spontaneously in populations of coupled photosensitive chemical oscillators. Experiments and simulations are carried out on nonlocally coupled oscillators, with the coupling strength decreasing exponentially with distance. Chimera states with synchronized oscillators, phase waves, and phase clusters coexisting with unsynchronized oscillators are analyzed. Irregular motion of the cores of asynchronous oscillators is found in spiral-wave chimeras.
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                Author and article information

                Journal
                Chaos: An Interdisciplinary Journal of Nonlinear Science
                Chaos
                AIP Publishing
                1054-1500
                1089-7682
                April 2019
                April 2019
                : 29
                : 4
                : 043106
                Affiliations
                [1 ]Department of Physics, Federal University of Paraná, 80060-000 Curitiba, PR, Brazil
                [2 ]Graduate in Science Program-Physics, State University of Ponta Grossa, 84030-900 Ponta Grossa, PR, Brazil
                [3 ]Institute of Physics, University of São Paulo, 05508-900 São Paulo, SP, Brazil
                [4 ]Center for Mathematics Computation and Cognition, Federal University of ABC, 09606-045 São Bernardo do Campo, SP, Brazil
                [5 ]Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xian University of Technology, Xi’an 710048, People’s Republic of China
                [6 ]Xian Technological University, Xi’an 710021, People’s Republic of China
                [7 ]Department of Mathematics and Statistics, State University of Ponta Grossa, 84030-900 Ponta Grossa, PR, Brazil
                [8 ]Institute for Complex Systems and Mathematical Biology, King’s College, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom
                Article
                10.1063/1.5087129
                6d7e08d2-3e40-4922-bfab-f5b9b36c6d73
                © 2019
                History

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