For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
0
views
20
references
 Top references
cited by
0
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      303
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      Preprint

      Read this article at

      ScienceOpenPublisherArXiv
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Chimera states are spatiotemporal patterns in which coherence and incoherence coexist. We observe the coexistence of synchronous (coherent) and desynchronous (incoherent) domains in a neuronal network. The network is composed of coupled adaptive exponential integrate-and-fire neurons that are connected by means of chemical synapses. In our neuronal network, the chimera states exhibit spatial structures both with spikes and bursts activities. Furthermore, those desynchronised domains not only have either spike or burst activity, but we show that the structures switch between spikes and bursts as the time evolves. Moreover, we verify the existence of multicluster chimera states.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

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

          Romain Brette, Wulfram Gerstner (2005)
          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.
          Article 0 comments Cited 379 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Firing patterns in the adaptive exponential integrate-and-fire model

            Richard Naud, Nicolas Marcille, Claudia Clopath … (2008)
            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.
            Article 0 comments Cited 99 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Stationary bumps in networks of spiking neurons.

              Erika J. C. Laing, Carson Chow (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 the bump. We consider a parameter regime where the bump solution is bistable with an all-off state and can be initiated with a transient excitatory stimulus. We show that the activity profile matches that of a corresponding population rate model. The bump in a spiking network can lose stability through partial synchronization to either a traveling wave or the all-off state. This can occur if the synaptic timescale is too fast through a dynamical effect or if a transient excitatory pulse is applied to the network. A bump can thus be activated and deactivated with excitatory inputs that may have physiological relevance.
              Article 0 comments Cited 91 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Publication date Created: 17 April 2019
                Article
                DOI: 10.1063/1.5087129
                ArXiV ID: 1904.08467
                SO-VID: ea4b9c88-4b63-4c3e-9787-a1a9525c2bc3
                License:

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Categories nlin.AO physics.bio-ph q-bio.NC

                ScienceOpen disciplines: Biophysics,Neurosciences,Nonlinear & Complex systems
                Data availability:
                ScienceOpen disciplines: Biophysics, Neurosciences, Nonlinear & Complex systems

                Comments

                Comment on this article