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
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Continuous learning of spiking networks trained with local rules
TL;DR: In this paper, SNNs are trained with biologically plausible local training rules based on spike-timing-dependent plasticity (STDP), which prohibits the direct use of CF prevention methods based on gradients of a global loss function.
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Tricking AI chips into Simulating the Human Brain: A Detailed Performance Analysis
TL;DR: In this paper , the inferior olive (IO) was simulated on different AI-platforms, including Graphcore IPU, GroqChip, Nvidia GPU with Tensor Cores and Google TPU, and the performance analysis revealed that the simulation problem maps extremely well onto the GPU and TPU architectures.
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SCANN: Side Channel Analysis of Spiking Neural Networks
TL;DR: In this article , the authors demonstrate eight unique side channel attacks by taking a common analog neuron (axon hillock neuron) as the test case and demonstrate that different synaptic weights, neurons/layer, neuron membrane thresholds, and neuron capacitor sizes yield distinct power and spike timing signatures, making them vulnerable to SCA.
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Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition
TL;DR: In this paper , two-stream convolutional neural networks (CSNNs) are used to extract spatio-temporal information from videos using asynchronous low-energy spikes.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
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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