Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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Journal ArticleDOI
Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning
Daniel Hasegan,Matt Deible,Christopher Earl,David J D'Onofrio,Hananel Hazan,Haroon Anwar,Samuel A. Neymotin +6 more
TL;DR: This work trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: spike-timing-dependent reinforcementlearning (STDP-RL) and evolutionary strategy (EVOL), which revealed EVOL as a powerful method for training Snns to perform sensory-motor behaviors.
Journal ArticleDOI
Adaptive VR Test in Music Harmony Based on Conditional Spiking GAN
Anna Shvets,S. Darkazanli +1 more
TL;DR: This article proposes an adaptive VR test for the knowledge level control in music harmony, the first attempt of conditional spiking GAN implementation along with the application of the spiking neural networks in a domain of semantic music generation.
Journal ArticleDOI
Training energy-based single-layer Hopfield and oscillatory networks with unsupervised and supervised algorithms for image classification
TL;DR: In this article , Hopfield neural networks (HNNs) and ONNs were used for image classification. And they achieved state-of-the-art performance on handwritten digits using a simplified MNIST set.
Journal ArticleDOI
On-Chip Trainable Spiking Neural Networks Using Time-To-First-Spike Encoding
Jiseong Im,Jaehyeon Kim,Honam Yoo,Jong Won Baek,Dongseok Kwon,Seongbin Oh,Jang Saeng Kim,Joon Hwang,Byung Gook Park,Jong-Ho Lee +9 more
TL;DR: This paper proposes on-chip trainable spiking neural networks using a time-to-first-spike (TTFS) method, and modify the learning rules of conventional SNNs to be suitable for on- chip learning.
Journal ArticleDOI
A regularization perspective based theoretical analysis for adversarial robustness of deep spiking neural networks.
TL;DR: In this article , a positive semidefinite regularizer is proposed to make the gradients of the output with respect to input closer to zero, thus resulting in inherent robustness against adversarial attacks.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
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TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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