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.
|Title of host publication||BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2021|
|Event||2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021 - Virtual, Online, Germany|
Duration: 6 Oct 2021 → 9 Oct 2021
|Name||BioCAS 2021 - IEEE Biomedical Circuits and Systems Conference, Proceedings|
|Conference||2021 IEEE Biomedical Circuits and Systems Conference, BioCAS 2021|
|Period||6/10/21 → 9/10/21|
Bibliographical noteFunding Information:
The authors would like to thank the students and faculty of the NBEL for the discussions.
© 2021 IEEE.
- Hebbian learning
- long term depression
- long-term potentiation
- neural engineering framework
- neuromorphic engineering
- online learning