A neuromorphic-hardware oriented bio-plausible online-learning spiking neural network model
Neuromorphic hardware inspired by the brain has attracted much attention for its advanced information processing concept. However, implementing online learning in the neuromorphic chip is still challenging. In this paper, we present a bio-plausible online-learning spiking neural network (SNN) model...
Saved in:
Main Authors: | Qiao, G. C., Hu, S. G., Wang, J. J., Zhang, C. M., Ning, N., Yu, Q., Liu, Y., Chen, Tu Pei |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/90044 http://hdl.handle.net/10220/49348 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
HFNet : a CNN architecture co-designed for neuromorphic hardware with a crossbar array of synapses
by: Gopalakrishnan, Roshan, et al.
Published: (2021) -
Low-power, adaptive neuromorphic systems : recent progress and future directions
by: Basu, Arindam, et al.
Published: (2019) -
Organic neuromorphic devices : past, present, and future challenges
by: Tuchman, Yaakov, et al.
Published: (2021) -
TOWARD A RUNTIME PROGRAMMABLE SPIKING NEURAL NETWORK HARDWARE ACCELERATOR WITH ON-CHIP LEARNING
by: NGUYEN NGOC NHU THAO
Published: (2023) -
Diffusive and drift halide perovskite memristive barristors as nociceptive and synaptic emulators for neuromorphic computing
by: John, Rohit Abraham, et al.
Published: (2021)