Deep Spiking Neural Network with Spike Count based Learning Rule
Jibin Wu,Yansong Chua,Malu Zhang,Qu Yang,Guoqi Li,Haizhou Li +5 more
- pp 1-6
TLDR
A novel spike-based learning rule for rate-coded deep SNNs, whereby the spike count of each neuron is used as a surrogate for gradient backpropagation is introduced, which allows direct deployment to the neuromorphic hardware and supports efficient inference.Abstract:
Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart. However, insufficient attention has been paid to neural encoding when designing SNN learning rules. Remarkably, the temporal credit assignment has been performed on rate-coded spiking inputs, leading to poor learning efficiency. In this paper, we introduce a novel spike-based learning rule for rate-coded deep SNNs, whereby the spike count of each neuron is used as a surrogate for gradient backpropagation. We evaluate the proposed learning rule by training deep spiking multi-layer perceptron (MLP) and spiking convolutional neural network (CNN) on the UCI machine learning and MNIST handwritten digit datasets. We show that the proposed learning rule achieves state-of-the-art accuracies on all benchmark datasets. The proposed learning rule allows introducing latency, spike rate and hardware constraints into the SNN learning, which is superior to the indirect approach in which conventional artificial neural networks are first trained and then converted to SNNs. Hence, it allows direct deployment to the neuromorphic hardware and supports efficient inference. Notably, a test accuracy of 98.40% was achieved on the MNIST dataset in our experiments with only 10 simulation time steps, when the same latency constraint is imposed during training.read more
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Posted Content
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks.
TL;DR: The proposed tandem learning rule offers a novel solution to training efficient, low latency, and high-accuracy deep SNNs with low computing resources and demonstrates competitive pattern recognition and regression capabilities on both the conventional frame- and event-based vision datasets.
Journal ArticleDOI
GLSNN: A Multi-Layer Spiking Neural Network Based on Global Feedback Alignment and Local STDP Plasticity.
TL;DR: This work gives an alternative method to train SNNs by biologically-plausible structural and functional inspirations from the brain, inspired by the significant top-down structural connections, and a differential STDP is used to optimize local plasticity.
Journal ArticleDOI
Fast and energy-efficient neuromorphic deep learning with first-spike times
Julian Göltz,Julian Göltz,Laura Kriener,Andreas Baumbach,Sebastian Billaudelle,Oliver Breitwieser,Benjamin Cramer,Dominik Dold,Dominik Dold,Akos F. Kungl,Walter Senn,Johannes Schemmel,K. Meier,Mihai A. Petrovici,Mihai A. Petrovici +14 more
TL;DR: In this paper, the authors describe a rigorous derivation of a learning rule for such first-spike times in networks of leaky integrate-and-fire neurons, relying solely on input and output spike times, and show how this mechanism can implement error back propagation in hierarchical spiking networks.
Posted Content
Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance
TL;DR: It is shown that a deep temporal-coded SNN can be trained easily and directly over the benchmark datasets CIFAR10 and ImageNet, with testing accuracy within 1% of the DNN of equivalent size and architecture.
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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