Self-organizing Rhythmic Patterns with Spatio-temporal spikes in Class I and Class II Neural Networks
スポンサーリンク
概要
- 論文の詳細を見る
The original publication is available at http://www.springerlink.com/content/1554603k38814041/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.
論文 | ランダム
- 高強度コンクリ-トを高所圧送した青森ベイブリッジ (コンクリ-トの強度--限界への挑戦)
- 35Pd-20Cu-45Ag合金の諸性質に及ぼす金添加の影響
- 歯科ろう付に関する研究 (第11報) : 赤外線ろう付における各種合金の前ろう付強度について
- ダイコア用光-化学重合型セメントの接着強度に関する研究 : 剪断および引張り強度試験法の確立とSEM観察による試料表面性状
- B-2 高強度チタンの歯科鋳造