Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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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BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
TL;DR: BS4NN is introduced, a modification of S4NN in which the synaptic weights are constrained to be binary, in order to decrease memory (ideally, one bit per synapse) and computation footprints and outperforms a simple BNN with the same architectures on those two datasets.
Journal ArticleDOI
A Novel Deep Learning and Polar Transformation Framework for an Adaptive Automatic Modulation Classification
TL;DR: This framework aims to efficiently classify the modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between accuracy and execution latency for any observed channel conditions derived from the main receiver's equalizer.
Journal ArticleDOI
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
TL;DR: In this paper , a simple linear postsynaptic potential function (ReL-PSP) for spiking neurons and a spike-timing-dependent error backpropagation (STDBP) learning algorithm for DeepSNNs are proposed.
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Training a Multi-Layer Photonic Spiking Neural Network With Modified Supervised Learning Algorithm Based on Photonic STDP
TL;DR: The photonic spike timing dependent plasticity (STDP) is applied to design a hardware-friendly biologically plausible supervised learning algorithm for a multi-layer photonic SNN, which is capable of solving the classical XOR problem.
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Fast and deep neuromorphic learning with time-to-first-spike coding
Julian Göltz,Andreas Baumbach,Sebastian Billaudelle,Oliver Breitwieser,Dominik Dold,Laura Kriener,Akos F. Kungl,Walter Senn,Johannes Schemmel,Karlheinz Meier,Mihai A. Petrovici +10 more
TL;DR: This work describes a rigorous derivation of error-backpropagation-based learning for hierarchical networks of leaky integrate-and-fire neurons that narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices.
References
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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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