A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Complexus ◽  
2006 ◽  
Vol 3 (1-3) ◽  
pp. 32-47 ◽  
Author(s):  
J.Manuel Moreno ◽  
Yann Thoma ◽  
Eduardo Sanchez ◽  
Jan Eriksson ◽  
Javier Iglesias ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2123 ◽  
Author(s):  
Lingfei Mo ◽  
Minghao Wang

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.


2019 ◽  
Vol 49 (7) ◽  
pp. 2490-2503 ◽  
Author(s):  
Shuangming Yang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Chen Liu ◽  
Huiyan Li ◽  
...  

2009 ◽  
Vol 22 (5-6) ◽  
pp. 791-800 ◽  
Author(s):  
Jayram Moorkanikara Nageswaran ◽  
Nikil Dutt ◽  
Jeffrey L. Krichmar ◽  
Alex Nicolau ◽  
Alexander V. Veidenbaum

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