Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2017

12.09.2016

Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model

verfasst von: Jingyi Qu, Rubin Wang, Chuankui Yan, Ying Du

Erschienen in: Neural Processing Letters | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this paper, the dynamics of a map-based model is proposed firstly. It is a simple model which can not only reproduce rich behaviors of biological neurons but also compute efficiently. Then, the dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. By tuning the coupling strength, synchronization of two spiking or bursting neurons are simulated. Furthermore, the spatiotemporal behavior of a small-world neuronal network using map-based model is studied. Detailed investigations reveal that the collective dynamics of neuronal activity will be affected by varying some key parameters, such as the coupling strength of neurons, connection probability and the number of nearest neighbor in small-world topology. Our study suggests that the map-based model will give us a new opportunity to reproduce the real biological network containing a large number of neurons.
Vorheriger Artikel Feature Selection for Adaptive Dual-Graph Regularized Concept Factorization for Data Representation
Nächster Artikel State Preserving Extreme Learning Machine: A Monotonically Increasing Learning Approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
2.
3.
Hodgkin AL, Huxley AF (1952) A qualitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544CrossRef
4.
Knight BW (1972) Dynamics of encoding in a population of neurons. J Gen Physiol 59:734C766
5.
Stein RB (1967) Some models of neuronal variability. Biophys J 7(1):37–68CrossRef
6.
7.
FitzHugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophys J 1(6):445–466CrossRef
8.
Rose RM, Hindmarsh JL (1989) The assembly of ionic currents in a thalamic neuron. I. The three-dimensional model. Proc R Soc Lond B 237:267–288CrossRef
9.
Morris C, Lecar H (1981) Voltage oscillations in the barnacle giant muscle fiber. Biophys J 35:193–213CrossRef
10.
Bazhenov M, Timofeev I, Steriade M, Sejnowski TJ (2002) Model of thalamocortical slow-wave sleep oscillations and transitions to activated states. J Neurosci 22:8691–8704
11.
Houweling AR, Bazhenov M, Timofeev I, Grenier F, Steriade M, Sejnowski TJ (2002) Frequency- selective augmenting responses by short-term synaptic depression in cat neocortex. J Physiol 542:599C617CrossRef
12.
Destexhe A, Bal T, McCormick DA, Sejnowski TJ (1996) Ionic mechanisms underlying synchronized and propagating waves in a model of ferret thalamic slices. J Neurophysiol 76:2049C2070
13.
Golomb D, Wang XJ, Rinzel J (1996) Propagation of spindle waves in a thalamic slice model. J Neurophysiol 75:750C769
14.
Traub RD, Jefferys JG, Whittington MA (1997) Simulation of gamma rhythms in networks of interneu- rons and pyramidal cells. J Comput Neurosci 4:141–150CrossRefMATH
15.
Bazhenov M, Stopfer M, Rabinovich M, Huerta R, Abarbanel HD, Sejnowski TJ, Laurent G (2001) Model of transient oscillatory synchronization in the locust antennal lobe. Neuron 30:553C567
16.
Rulkov NF, Timofeev I, Bazhenov M (2004) Oscillations in large-scale cortical networks: map-based model. J Comput Neurosci 17:203–223CrossRef
17.
Sausedo JM, Pisarchik AN (2014) Synchronization of map-based neurons with memory and synaptic delay. Physi Lett A 378(30):2108–2112CrossRef
18.
Rulkov NF, Bazhenov M (2008) Oscillations in large-scale cortical network models. J Biol Phys 34:279–299CrossRef
19.
Cao HJ, Wu YG (2013) Bursting types and stable domains of Rulkov neuron network with mean field computing. Int J Bifurc Chaos 23(12):1330041–1330061CrossRefMATH
20.
Wang Q, Chen G, Perc M (2011) Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling. PLoS One 6(1):el5851
21.
Heagy JF, Carroll TL, Pecora LM (1994) Synchronous chaos in coupled oscillator systems. Phys Rev E 50(3):1874–1885CrossRef
22.
Qu JY, Wang RB, Du Y (2012) Synchronization study in ring-like and grid-like neuronal networks. Cogn Neurodyn 6(1):21–31CrossRef
23.
Wu CW, Chua LO (1995) Synchronization in an array of linearly coupled dynamical systems. IEEE Trans Circuits Syst I 42(8):430–447MathSciNetCrossRefMATH
24.
Gade PM (1996) Synchronization of oscillators with random nonlocal connectivity. Phys Rev E 54:64–70CrossRef
25.
Manrubia SC, Mikhailov SM (1999) Synchronization and clustering in randomly coupled chaotic dynamical networks. Phys Rev E 60:1579–1589CrossRef
26.
Qu JY, Wang RB, Yan CK, Du Y (2014) Oscillations and synchrony in a cortical neural network. Cogn Neurodyn 8(2):157–166CrossRef
27.
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small world’ networks. Nature 393:440–442CrossRef
28.
Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12(6):512–523CrossRef
29.
Sun WG, Wang RB, Wang WX, Cao JT (2010) Analyzing inner and outer synchronization between two coupled discrete-time networks with time delays. Cogn Neurodyn 4(3):225C231CrossRef
30.
Volman V, Baruchi I, Ben-Jacob E (2005) Manifestation of function-follow-form in cultured neuronal networks. Phys Biol 2(2):98–110CrossRef
31.
Yu S, Huang DB, Singer W et al (2008) A small world of neuronal synchrony. Cereb Cortex 18(2):2891–2901CrossRef
32.
Roxin A, Riecke H, Solla SA (2004) Self-sustained activity in a small-world network of excitable neurons. Phys Rev Lett 92:198101CrossRef
33.
Kwon O, Moon HT (2002) Coherence resonance in small-world networks of excitable cell. Phys Lett A 298:319–324CrossRefMATH
34.
Gong YB, Xu B, Xu Q, Yang CL, Ren TQ, Hou ZH, Xin HW (2006) Ordering spatiotemporal chaos in complex thermosensitive neuron networks. Phys Rev E 73:046137CrossRef
35.
Wei DQ, Luo XS (2007) Ordering spatiotemporal chaos in discrete neural networks with small-world connections. Europhys Lett 78:68004CrossRef
36.
Wang M, Hou Z, Xin H (2006) Ordering spatiotemporal chaos in small-world neuron networks. Chem Phys Chem 7:579–582
37.
Perc M (2007) Stochastic resonance on excitable small-world networks via a pacemaker. Phys Rev E 76:066203CrossRef
38.
Perc M, Gosak M (2008) Pacemaker-driven stochastic resonance on diffusive and complex networks of bistable oscillators. New J Phys 10:053008CrossRef
39.
Hasegawa H (2005) Synchronizations in small-world networks of spiking neurons: diffusive versus sigmoid couplings. Phys Rev E 72:056139MathSciNetCrossRef
40.
Protopapas AD, Vanier M, Bower JM (1998) Simulating of lagre networks of neurons. In: Koch Ch, Segev I (eds) Methods in neuronal modeling: from ions to networks. MIT, Cambridge, p 461
Metadaten
Titel
Spatiotemporal Behavior of Small-World Neuronal Networks Using a Map-Based Model
verfasst von
Jingyi Qu
Rubin Wang
Chuankui Yan
Ying Du
Publikationsdatum
12.09.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-016-9547-5

Weitere Artikel der Ausgabe 2/2017

Neural Processing Letters 2/2017 Zur Ausgabe

OriginalPaper

Stability and Hopf Bifurcation of Time Fractional Cohen–Grossberg Neural Networks with Diffusion and Time Delays in Leakage Terms

OriginalPaper

Anti-periodic Solutions of Inertial Neural Networks with Time Delays

OriginalPaper

Classification of EEG Signals Based on Autoregressive Model and Wavelet Packet Decomposition

OriginalPaper

Ensemble Multiple-Kernel Based Manifold Regularization

OriginalPaper

State Preserving Extreme Learning Machine: A Monotonically Increasing Learning Approach

OriginalPaper

Cost Sensitive Semi-Supervised Canonical Correlation Analysis for Multi-view Dimensionality Reduction

Neuer Inhalt

    Bildnachweise