Dendritic computation in a point neuron model
- Author
- Alexander Vandesompele, Francis wyffels (UGent) and Joni Dambre (UGent)
- Organization
- Abstract
- Biological neurons possess elaborate dendrites that perform elaborate computations. They are however ignored in the widely used point neuron models. Here, we present a simple addition to the commonly used leaky integrate-and-fire model that introduces the concept of a dendrite. All synapses on the dendrite have a mutual relationship. The result is a form of short term plasticity in which synapse strengths are influenced by recent activity in other synapses. This improves the ability of the neuron to recognize temporal sequences.
- Keywords
- SPINES, Spiking neural networks, Dendritic computation, Point neuron model
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8730910
- MLA
- Vandesompele, Alexander, et al. “Dendritic Computation in a Point Neuron Model.” ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II, edited by i Farkas et al., vol. 12397, Springer, 2020, pp. 599–609, doi:10.1007/978-3-030-61616-8_48.
- APA
- Vandesompele, A., wyffels, F., & Dambre, J. (2020). Dendritic computation in a point neuron model. In i Farkas, P. Masulli, & S. Wermter (Eds.), ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II (Vol. 12397, pp. 599–609). https://doi.org/10.1007/978-3-030-61616-8_48
- Chicago author-date
- Vandesompele, Alexander, Francis wyffels, and Joni Dambre. 2020. “Dendritic Computation in a Point Neuron Model.” In ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II, edited by i Farkas, P Masulli, and S Wermter, 12397:599–609. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-030-61616-8_48.
- Chicago author-date (all authors)
- Vandesompele, Alexander, Francis wyffels, and Joni Dambre. 2020. “Dendritic Computation in a Point Neuron Model.” In ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II, ed by. i Farkas, P Masulli, and S Wermter, 12397:599–609. Berlin, Germany: Springer. doi:10.1007/978-3-030-61616-8_48.
- Vancouver
- 1.Vandesompele A, wyffels F, Dambre J. Dendritic computation in a point neuron model. In: Farkas i, Masulli P, Wermter S, editors. ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II. Berlin, Germany: Springer; 2020. p. 599–609.
- IEEE
- [1]A. Vandesompele, F. wyffels, and J. Dambre, “Dendritic computation in a point neuron model,” in ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II, Bratislava, SLOVAKIA, 2020, vol. 12397, pp. 599–609.
@inproceedings{8730910,
abstract = {{Biological neurons possess elaborate dendrites that perform elaborate computations. They are however ignored in the widely used point neuron models. Here, we present a simple addition to the commonly used leaky integrate-and-fire model that introduces the concept of a dendrite. All synapses on the dendrite have a mutual relationship. The result is a form of short term plasticity in which synapse strengths are influenced by recent activity in other synapses. This improves the ability of the neuron to recognize temporal sequences.}},
author = {{Vandesompele, Alexander and wyffels, Francis and Dambre, Joni}},
booktitle = {{ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING, ICANN 2020, PT II}},
editor = {{Farkas, i and Masulli, P and Wermter, S}},
isbn = {{978-3-030-61616-8}},
issn = {{0302-9743}},
keywords = {{SPINES,Spiking neural networks,Dendritic computation,Point neuron model}},
language = {{eng}},
location = {{Bratislava, SLOVAKIA}},
pages = {{599--609}},
publisher = {{Springer}},
title = {{Dendritic computation in a point neuron model}},
url = {{http://doi.org/10.1007/978-3-030-61616-8_48}},
volume = {{12397}},
year = {{2020}},
}
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