Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity

Ali Safa; Ilia Ocket; Andre Bourdoux; Hichem Sahli; Francky Catthoor; Georges G.E. Gielen

doi:10.1109/IJCNN55064.2022.9892362

Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity

Ali Safa, Ilia Ocket, Andre Bourdoux, Hichem Sahli, Francky Catthoor, Georges G.E. Gielen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision.

Original language	English
Title of host publication	2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728186719
DOIs	https://doi.org/10.1109/IJCNN55064.2022.9892362
Publication status	Published - 2022
Externally published	Yes
Event	2022 International Joint Conference on Neural Networks, IJCNN 2022 - Padua, Italy Duration: 18 Jul 2022 → 23 Jul 2022

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
ISSN (Print)	2161-4393
ISSN (Electronic)	2161-4407

Conference

Conference	2022 International Joint Conference on Neural Networks, IJCNN 2022
Country/Territory	Italy
City	Padua
Period	18/07/22 → 23/07/22

Keywords

Event-based camera
Spike-Timing-Dependent Plasticity
Spiking Neural Network

Access to Document

10.1109/IJCNN55064.2022.9892362

Cite this

Safa, A., Ocket, I., Bourdoux, A., Sahli, H., Catthoor, F., & Gielen, G. G. E. (2022). Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity. In 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings (Proceedings of the International Joint Conference on Neural Networks). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN55064.2022.9892362

@inproceedings{4a8918d67cc84a3db32d4b774cd6759d,

title = "Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity",

abstract = "We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3\% on N-MNIST, +7.74\% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision.",

keywords = "Event-based camera, Spike-Timing-Dependent Plasticity, Spiking Neural Network",

author = "Ali Safa and Ilia Ocket and Andre Bourdoux and Hichem Sahli and Francky Catthoor and Gielen, \{Georges G.E.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 International Joint Conference on Neural Networks, IJCNN 2022 ; Conference date: 18-07-2022 Through 23-07-2022",

year = "2022",

doi = "10.1109/IJCNN55064.2022.9892362",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings",

address = "United States",

}

Safa, A, Ocket, I, Bourdoux, A, Sahli, H, Catthoor, F & Gielen, GGE 2022, Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity. in 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings. Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc., 2022 International Joint Conference on Neural Networks, IJCNN 2022, Padua, Italy, 18/07/22. https://doi.org/10.1109/IJCNN55064.2022.9892362

Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity. / Safa, Ali; Ocket, Ilia; Bourdoux, Andre et al.
2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity

AU - Safa, Ali

AU - Ocket, Ilia

AU - Bourdoux, Andre

AU - Sahli, Hichem

AU - Catthoor, Francky

AU - Gielen, Georges G.E.

PY - 2022

Y1 - 2022

N2 - We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision.

AB - We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision.

KW - Event-based camera

KW - Spike-Timing-Dependent Plasticity

KW - Spiking Neural Network

UR - https://www.scopus.com/pages/publications/85140713036

U2 - 10.1109/IJCNN55064.2022.9892362

DO - 10.1109/IJCNN55064.2022.9892362

M3 - Conference contribution

AN - SCOPUS:85140713036

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Safa A, Ocket I, Bourdoux A, Sahli H, Catthoor F, Gielen GGE. Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity. In 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN55064.2022.9892362

Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity

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