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原文連結
論文資訊
- 類型：已發表論文
- 日期：2011
摘要
We study the effect of learning dynamics on 網絡 topology. Firstly, a 網絡 of discrete dynamical systems is considered for this purpose and the coupling strengths are made to evolve according to a temporal learning rule that is based on the paradigm of spike-time-dependent plasticity (STDP). This incorporates necessary competition between different edges. The final 網絡 we obtain is-robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal 神經 網絡. For properly chosen input signals, there exists a steady state with a residual 網絡. We compare the motif profile of such a 網絡 with that of the real 神經 網絡 of C. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting 網絡 is robust
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