@inproceedings{b7218678b3234bef9bbf9fe1671f301c,
title = "Reservoir Computing with VCO-Based Spiking Neurons for Regression and Classification",
abstract = "Reservoir computing (RC) significantly reduces the requirement on hardware and training resources, making it suitable for edge-computing applications. This work proposes using voltage-controlled oscillator (VCO)-based spiking neurons for RC to leverage the intrinsic randomness and variability of the neuron circuit for low-power operations. We describe the underlying circuit design and propose a network architecture based on the spiking neuron for RC. We demonstrate the effectiveness of the proposed RC network using VCO-based spiking neurons through benchmark tasks and electrocardiogram (ECG) classification.",
keywords = "Analog Neuron, Artificial Intelligence, Neuromorphic Computing, Reservoir Computing, Spiking Neural Network",
author = "Kanta Yoshioka and Parker Allred and Taylor Barton and Sahoo, \{Bibhu Datta\} and Kuan, \{Yen Cheng\} and Chiang, \{Shiuh Hua Wood\} and Hakaru Tamukoh",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025 ; Conference date: 25-05-2025 Through 28-05-2025",
year = "2025",
doi = "10.1109/ISCAS56072.2025.11043229",
language = "English",
series = "Proceedings - IEEE International Symposium on Circuits and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings",
address = "United States",
}