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An homeostatic activity-dependent structural plasticity algorithm for richer input combination

Tanguy Cazalets (UGent) and Joni Dambre (UGent)
(2023) 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. In IEEE International Joint Conference on Neural Networks (IJCNN)
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Abstract
This paper introduces a novel rate-based variant of homeostatic activity-dependent structural plasticity (HADSP) for echo state networks. Despite its importance in brain development, structural plasticity has been largely overlooked in artificial neural networks. Our algorithm, although using only homeostatic plasticity, let emerge principles of Hebbian learning. Our analysis sheds light on the information processing capabilities of HADSP-powered echo state networks and suggests that HADSP effectively leverages the inter-relationships of the network's inputs. The study highlights the potential for rate-based HADSP to contribute to the field of computational neuroscience and plasticity in echo state networks. Furthermore, our findings highlight the crucial role of structural plasticity in influencing network function and organization and contribute significantly to the ongoing research on leveraging plasticity for the advancement of reservoir computing techniques.

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Citation

Please use this url to cite or link to this publication:

MLA
Cazalets, Tanguy, and Joni Dambre. “An Homeostatic Activity-Dependent Structural Plasticity Algorithm for Richer Input Combination.” 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, IEEE, 2023, doi:10.1109/ijcnn54540.2023.10191230.
APA
Cazalets, T., & Dambre, J. (2023). An homeostatic activity-dependent structural plasticity algorithm for richer input combination. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. Presented at the 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia. https://doi.org/10.1109/ijcnn54540.2023.10191230
Chicago author-date
Cazalets, Tanguy, and Joni Dambre. 2023. “An Homeostatic Activity-Dependent Structural Plasticity Algorithm for Richer Input Combination.” In 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. IEEE. https://doi.org/10.1109/ijcnn54540.2023.10191230.
Chicago author-date (all authors)
Cazalets, Tanguy, and Joni Dambre. 2023. “An Homeostatic Activity-Dependent Structural Plasticity Algorithm for Richer Input Combination.” In 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. IEEE. doi:10.1109/ijcnn54540.2023.10191230.
Vancouver
1.
Cazalets T, Dambre J. An homeostatic activity-dependent structural plasticity algorithm for richer input combination. In: 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN. IEEE; 2023.
IEEE
[1]
T. Cazalets and J. Dambre, “An homeostatic activity-dependent structural plasticity algorithm for richer input combination,” in 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, Gold Coast, Australia, 2023.
@inproceedings{01HA9JD87MMA45HSH0G7H4RQTR,
  abstract     = {{This paper introduces a novel rate-based variant of homeostatic activity-dependent structural plasticity (HADSP) for echo state networks. Despite its importance in brain development, structural plasticity has been largely overlooked in artificial neural networks. Our algorithm, although using only homeostatic plasticity, let emerge principles of Hebbian learning. Our analysis sheds light on the information processing capabilities of HADSP-powered echo state networks and suggests that HADSP effectively leverages the inter-relationships of the network's inputs. The study highlights the potential for rate-based HADSP to contribute to the field of computational neuroscience and plasticity in echo state networks. Furthermore, our findings highlight the crucial role of structural plasticity in influencing network function and organization and contribute significantly to the ongoing research on leveraging plasticity for the advancement of reservoir computing techniques.}},
  author       = {{Cazalets, Tanguy and Dambre, Joni}},
  booktitle    = {{2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN}},
  isbn         = {{9781665488679}},
  issn         = {{2161-4393}},
  language     = {{eng}},
  location     = {{Gold Coast, Australia}},
  pages        = {{8}},
  publisher    = {{IEEE}},
  title        = {{An homeostatic activity-dependent structural plasticity algorithm for richer input combination}},
  url          = {{http://doi.org/10.1109/ijcnn54540.2023.10191230}},
  year         = {{2023}},
}
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