Area Efficient Architecture for Large Scale Implementation of Biologically Plausible Spiking Neural Networks on Reconfigurable Hardware

2006 ◽  
Author(s):  
Arfan Ghani ◽  
T. McGinnity ◽  
Liam Maguire ◽  
Jim Harkin
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Reconfigurable Hardware ◽  
Spiking Neural Networks ◽  
Area Efficient

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
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks

scholarly journals The POEtic Electronic Tissue and Its Role in the Emulation of Large-Scale Biologically Inspired Spiking Neural Networks Models

Complexus ◽  
2006 ◽  
Vol 3 (1-3) ◽  
pp. 32-47 ◽  
Author(s):  
J.Manuel Moreno ◽  
Yann Thoma ◽  
Eduardo Sanchez ◽  
Jan Eriksson ◽  
Javier Iglesias ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Biologically Inspired

scholarly journals LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2123 ◽  
Author(s):  
Lingfei Mo ◽  
Minghao Wang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Network Structure ◽  
Large Scale ◽  
Spike Timing ◽  
Spiking Neural Networks ◽  
Logical Operation ◽  
Large Scale Network ◽  
Learning Capabilities ◽  
Scale Network

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.

Real-Time Neuromorphic System for Large-Scale Conductance-Based Spiking Neural Networks

2019 ◽  
Vol 49 (7) ◽  
pp. 2490-2503 ◽  
Author(s):  
Shuangming Yang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Chen Liu ◽  
Huiyan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Large Scale ◽  
Spiking Neural Networks ◽  
Neuromorphic System
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