Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks

The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (F...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 33; číslo 8; s. 3988 - 4002
Hlavní autoři:	Guo, Wenzhe, Yantir, Hasan Erdem, Fouda, Mohammed E., Eltawil, Ahmed M., Salama, Khaled Nabil
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Biological system modeling Biology Central processing units Computational modeling CPUs Energy consumption Euler method Field programmable gate arrays field-programmable gate array (FPGA) platform Firing pattern Inference Information processing Machine learning Mathematical models Neural networks neuromorphic computing Neurons Numerical methods Numerical models on-chip learning Online instruction Optimization parallel architecture parameter optimization Parameters Runge-Kutta (RK) method Runge-Kutta method Simulation Spiking spiking neural network (SNN) Training unsupervised spike-timing-dependent plasticity (STDP) learning
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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