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.
@article{arxiv.2204.00507,
title = {Deep Learning in Spiking Phasor Neural Networks},
author = {Connor Bybee and E. Paxon Frady and Friedrich T. Sommer},
journal= {arXiv preprint arXiv:2204.00507},
year = {2022}
}
Comments
10 pages, 5 figures, work presented at Intel Neuromorphic Community Fall 2019 workshop in Graz, Austria and the UC Berkeley Center for Computational Biology Retreat 2019