Self-organizing Rhythmic Patterns with Spatio-temporal Spikes in Class I and Class II Neural Networks

  • Ryosuke Hosaka
  • Tohru Ikeguchi
  • Kazuyuki Aihara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


Regularly spiking neurons are classified into two categories, Class I and Class II, by their firing properties for constant inputs. To investigate how the firing properties of single neurons affect to ensemble rhythmic activities in neural networks, we constructed different types of neural networks whose excitatory neurons are the Class I neurons or the Class II neurons. The networks were driven by random inputs and developed with STDP learning. As a result, the Class I and the Class II neural networks generate different types of rhythmic activities: the Class I neural network generates slow rhythmic activities, and the Class II neural network generates fast rhythmic activities.


Neural Network Rhythmic Activity Synaptic Weight Inhibitory Neuron Excitatory Neuron 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ryosuke Hosaka
    • 1
    • 2
    • 3
  • Tohru Ikeguchi
    • 1
  • Kazuyuki Aihara
    • 2
    • 3
  1. 1.Graduate School of Science and EngineeringSaitama UniversitySaitamaJapan
  2. 2.ERATO, JSTAihara Complexity Modelling ProjectTokyoJapan
  3. 3.Institute of Industrial ScienceUniversity of TokyoTokyoJapan

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