Learning and structure of neuronal networks

Pramana

Quansheng Ren2²  Areejit Samal2²  Kiran M Kolwankar1 2¹  Jürgen Jost2² 

[1] Department of Physics, Ramniranjan Jhunjhunwala College, Ghatkopar (W), Mumbai 400 086, India$$;Max Planck Institute for Mathematics in the Sciences, Inselstr. 22, 04103 Leipzig, Germany$$

关键词: Neuronal networks;    scale-free network;    synapses;    learning;    logistic map.;   

DOI  :  

学科分类：物理（综合）

来源: Indian Academy of Sciences

【 摘 要 】

We study the effect of learning dynamics on network topology. Firstly, a network 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 network we obtain is robust and has a broad degree distribution. Then we study the dynamics of the structure of a formal neural network. For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of the real neural network of 𝐶. elegans and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of model parameters.

【 授权许可】

Unknown