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.
|Title of host publication
|Subtitle of host publication
|29th IEEE International Conference on Electronics, Circuits and Systems
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2022
|29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022 - Glasgow, United Kingdom
Duration: 24 Oct 2022 → 26 Oct 2022
|ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings
|29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022
|24/10/22 → 26/10/22
Bibliographical noteFunding 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.
© 2022 IEEE.
- iris flower dataset
- OZ neuron
- Spiking neural networks