@inproceedings{c97483df8fea42bba33ec900803ce625,
title = "Neuromorphic Spike Timing Dependent Plasticity with adaptive OZ Spiking Neurons",
abstract = "Spike Timing Dependent Plasticity (STDP) is a biologically plausible learning rule routinely used for real-time learning in brain-inspired (neuromorphic) systems. In this work, we utilized an analog design of a Neural Engineering Framework (NEF)-tailored spiking neuron, termed OZ, for STDP-driven learning. We propose analog circuit designs of STDP synapse and frequency adaptation and used them to demonstrate longterm potentiation and depression with adapted OZ neurons. Our design provides NEF-compiled energy-efficient STDP with analog circuitry.",
keywords = "Hebbian learning, long term depression, long-term potentiation, neural engineering framework, neuromorphic engineering, online learning",
author = "Avi Hazan and Tsur, {Elishai Ezra}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 ; Conference date: 06-10-2021 Through 09-10-2021",
year = "2021",
doi = "10.1109/BioCAS49922.2021.9644944",
language = "الإنجليزيّة",
series = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings",
address = "الولايات المتّحدة",
}