Event - Driven Tactile Learning with Location Spiking Neurons. Kang, P., Banerjee, S., Chopp, H., Katsaggelos, A., & Cossairt, O. In 2022 International Joint Conference on Neural Networks (IJCNN), volume 2022-July, pages 1–9, jul, 2022. IEEE.
Event - Driven Tactile Learning with Location Spiking Neurons [link]Paper  doi  abstract   bibtex   
The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN -enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called 'location spiking neuron', which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model - Location Spike Response Model (LSRM) that serves as a new building block of SNNs11The TSRM is the classical SRM in the literature. We add the character 'T' to highlight its difference with the LSRM.• Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware.
@inproceedings{Peng2022a,
abstract = {The sense of touch is essential for a variety of daily tasks. New advances in event-based tactile sensors and Spiking Neural Networks (SNNs) spur the research in event-driven tactile learning. However, SNN -enabled event-driven tactile learning is still in its infancy due to the limited representative abilities of existing spiking neurons and high spatio-temporal complexity in the data. In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called 'location spiking neuron', which enables us to extract features of event-based data in a novel way. Moreover, based on the classical Time Spike Response Model (TSRM), we develop a specific location spiking neuron model - Location Spike Response Model (LSRM) that serves as a new building block of SNNs11The TSRM is the classical SRM in the literature. We add the character 'T' to highlight its difference with the LSRM.• Furthermore, we propose a hybrid model which combines an SNN with TSRM neurons and an SNN with LSRM neurons to capture the complex spatio-temporal dependencies in the data. Extensive experiments demonstrate the significant improvements of our models over other works on event-driven tactile learning and show the superior energy efficiency of our models and location spiking neurons, which may unlock their potential on neuromorphic hardware.},
author = {Kang, Peng and Banerjee, Srutarshi and Chopp, Henry and Katsaggelos, Aggelos and Cossairt, Oliver},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
doi = {10.1109/IJCNN55064.2022.9892074},
isbn = {978-1-7281-8671-9},
keywords = {Spiking Neural Networks,event-driven tactile learning,location spiking neurons,spiking neuron models},
month = {jul},
pages = {1--9},
publisher = {IEEE},
title = {{Event - Driven Tactile Learning with Location Spiking Neurons}},
url = {https://ieeexplore.ieee.org/document/9892074/},
volume = {2022-July},
year = {2022}
}
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