Deep Learning in Spiking Phasor Neural Networks

• URL: http://arxiv.org/abs/2204.00507v1
• Date: Fri, 1 Apr 2022 15:06:15 GMT
• Title: Deep Learning in Spiking Phasor Neural Networks
• Authors: Connor Bybee and E. Paxon Frady and Friedrich T. Sommer
• Abstract summary: Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware. In this paper, we introduce Spiking Phasor Neural Networks (SPNNs) SPNNs are based on complex-valued Deep Neural Networks (DNNs), representing phases by spike times.
• Score: 0.6767885381740952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking Neural Networks (SNNs) have attracted the attention of the deep learning community for use in low-latency, low-power neuromorphic hardware, as well as models for understanding neuroscience. In this paper, we introduce Spiking Phasor Neural Networks (SPNNs). SPNNs are based on complex-valued Deep Neural Networks (DNNs), representing phases by spike times. Our model computes robustly employing a spike timing code and gradients can be formed using the complex domain. We train SPNNs on CIFAR-10, and demonstrate that the performance exceeds that of other timing coded SNNs, approaching results with comparable real-valued DNNs.

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