Progressive compression and weight reinforcement for spiking neural networks-Reference-Cited by-同舟云学术

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Progressive compression and weight reinforcement for spiking neural networks

Published:2022-03-02 Issue:11 Volume:34 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Elbez Hammouda¹²^ORCID,Kamel Benhaoua Mohammed³^ORCID,Devienne Philippe²,Boulet Pierre²

Affiliation:

1. Computer Science Department, Faculty of Exact Sciences University Mustapha Stambouli of Mascara Mascara Algeria

2. Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL Lille France

3. LAPECI Laboratory, Computer Science Department, Faculty of Exact Sciences University of Oran1 Oran Algeria

Funder

European Commission

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.6891

Reference39 articles.

1. MerollaP ArthurJ AkopyanF ImamN ManoharR&ModhaDSA digital neurosynaptic core using embedded crossbar memory with 45 pJ per spike in 45 nm. Proceedings of the 2011 IEEE Custom Integrated Circuits Conference (CICC); 2011:1‐4.

2. Parameter Exploration to Improve Performance of Memristor-Based Neuromorphic Architectures

3. Gradient-based learning applied to document recognition

4. Unsupervised learning of digit recognition using spike-timing-dependent plasticity

5. Training Deep Spiking Neural Networks Using Backpropagation

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