Self-organizing Rhythmic Patterns with Spatio-temporal Spikes in Class I and Class II Neural Networks
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.
KeywordsNeural Network Rhythmic Activity Synaptic Weight Inhibitory Neuron Excitatory Neuron
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- 1.Bi, G., Poo, M.: Synapic modificaion in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of Neuroscience 18, 10464–10472 (1998)Google Scholar
- 6.Hodgkin, A.L.: The local electoric changes associated with repetitive action in a non-medulated axon. Journal of Physiology 107, 165–181 (1948)Google Scholar
- 7.Hosaka, R., Araki, O., Ikeguchi, T.: Spike-timing-dependent synaptic plasticity makes a source of synfire chain. Submitted to Neural Computation (2006)Google Scholar
- 10.Miyakawa, H., Inoue, M.: Biophysics of Neurons. Maruzen (2003) (in Japanese)Google Scholar
- 11.Nicolelis, M.A.L. (ed.): Advances in Neural Population Coding. Elsevier, Amsterdam (2001)Google Scholar
- 13.Rinzel, J., Ermentrout, B.B.: Analysis of neural excitability and oscillations. In: Kock, C., Segev, I. (eds.) Methods in Neuronal Modeling. MIT Press, Cambridge (1989)Google Scholar