Research Abstract


Optimal spike-based communication in excitable networks with strong-sparse and weak-dense links

2012年7月2日 Scientific Reports 2 : 485 doi: 10.1038/srep00485


寺前 順之介1,4, 坪 泰宏1 & 深井 朋樹1,2,3

  1. 理化学研究所 脳科学総合研究センター
  2. 理化学研究所・次世代計算科学
  3. CREST・科学技術振興機構
  4. さきがけ・科学技術振興機構

The connectivity of complex networks and functional implications has been attracting much interest in many physical, biological and social systems. However, the significance of the weight distributions of network links remains largely unknown except for uniformly- or Gaussian-weighted links. Here, we show analytically and numerically, that recurrent neural networks can robustly generate internal noise optimal for spike transmission between neurons with the help of a long-tailed distribution in the weights of recurrent connections. The structure of spontaneous activity in such networks involves weak-dense connections that redistribute excitatory activity over the network as noise sources to optimally enhance the responses of individual neurons to input at sparse-strong connections, thus opening multiple signal transmission pathways. Electrophysiological experiments confirm the importance of a highly broad connectivity spectrum supported by the model. Our results identify a simple network mechanism for internal noise generation by highly inhomogeneous connection strengths supporting both stability and optimal communication.