E-STDP: A Spatio-Temporally Local Unsupervised Learning Rule for Sparse Coded Spiking Convolutional Autoencoders
Author:
Affiliation:
1. IIT-Bombay,Department of Electrical Engineering,Mumbai,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191585.pdf?arnumber=10191585
Reference16 articles.
1. Training Spiking ConvNets by STDP and Gradient Descent
2. Unsupervised learning of digit recognition using spike-timing-dependent plasticity
3. Event-Based, Timescale Invariant Unsupervised Online Deep Learning With STDP
4. STDP-based spiking deep convolutional neural networks for object recognition
5. A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition
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1. Efficient sparse spiking auto-encoder for reconstruction, denoising and classification;Neuromorphic Computing and Engineering;2024-08-02
2. An Entorhinal-Hippocampal Loop Model Based on Non-negative Sparse Coding;Journal of The Institution of Engineers (India): Series B;2024-06-10
3. Spiking Auto-Encoder Using Error Modulated Spike Timing Dependant Plasticity;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19
4. Deep Spiking Neural Networks Based on STDP Rules for Image Recognition;2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP);2023-12-15
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