@inproceedings{3cb14c274c0443dd93182f578f951923,
title = "Neuromorphic Analog Implementation of Reservoir Computing for Machine Learning",
abstract = "In reservoir computing, dynamical systems are used to drive state-of-the-art machine learning with small training sets and minimal computing resources. Neuromorphic (brain-inspired) computing pose to further improve reservoir computing with energy-efficient spiking neural implementations. Here we propose an analog circuit design for reservoir computing using OZ spiking neurons, STDP (Spike-timing-dependent plasticity) synapses, and learning PES (prescribed error sensitivity) circuitry. We evaluated our design on a small scale using the Iris flower data set, demonstrating the potential application of neuromorphic analog hardware in reservoir computing.",
keywords = "OZ neuron, PES, STDP, Spiking neural networks, iris flower dataset",
author = "Avi Hazan and Tsur, {Elishai Ezra}",
note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Open University of Israel research grant. The authors would like to thank the members of the Neuro-Biomorphic Engineering Lab at the Open University of Israel for the fruitful discussions. Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022 ; Conference date: 24-10-2022 Through 26-10-2022",
year = "2022",
doi = "10.1109/ICECS202256217.2022.9971045",
language = "American English",
series = "ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "ICECS 2022",
address = "United States",
}