Spiking Convolutional Vision Transformer

Authors

Sameerah Talafha, Southern Illinois University
Banafsheh Rekabdar, Portland State UniversityFollow
Christos Mousas, Purdue University
Chinwe Ekenna, University at Albany

Published In

2023 IEEE 17th International Conference on Semantic Computing (ICSC)

Document Type

Citation

Publication Date

2023

Abstract

This paper introduces the design of a Spiking Convolutional Vision Transformer (SCvT) by using the biological-inspired learning rules including spike-timing-dependent plasticity (STDP) and reward–modulated STDP (R-STDP) for a classification task. We describe the structure of the SCvT simulated with deep spiking convolutional neural networks (DSCNN) and give a detailed description of the classification strategy.

Rights

Copyright 2023 IEEE

Locate the Document

https://doi.org/10.1109/ICSC56153.2023.00044

DOI

10.1109/ICSC56153.2023.00044

Persistent Identifier

https://archives.pdx.edu/ds/psu/39694

Publisher

IEEE

Citation Details

S. Talafha, B. Rekabdar, C. Mousas and C. Ekenna, "Spiking Convolutional Vision Transformer," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 225-226, doi: 10.1109/ICSC56153.2023.00044.