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Barbulescu R, Silveira LM. Black-box model reduction of the C. Elegans nervous system. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:4174-4179doi: 10.1109/EMBC46164.2021.9630241.
Barbulescu, R., & Silveira, L. M. (2021). Black-box model reduction of the C. Elegans nervous system. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 20214174-4179. https://doi.org/10.1109/EMBC46164.2021.9630241
Barbulescu, Ruxandra, and Silveira, L Miguel. "Black-box model reduction of the C. Elegans nervous system." Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference vol. 2021 (2021): 4174-4179. doi: https://doi.org/10.1109/EMBC46164.2021.9630241
Barbulescu R, Silveira LM. Black-box model reduction of the C. Elegans nervous system. Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4174-4179. doi: 10.1109/EMBC46164.2021.9630241. PMID: 34892144.
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Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4174-4179. doi: 10.1109/EMBC46164.2021.9630241.

Black-box model reduction of the C. Elegans nervous system.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

Ruxandra Barbulescu, L Miguel Silveira

PMID: 34892144 DOI: 10.1109/EMBC46164.2021.9630241

Abstract

In recent years, modeling neurons and neuronal collections with high accuracy have become central issues of neuroscience. The development of efficient algorithms for their simulation as well as the increase in computational power and parallelization need to keep up with the quantity and complexity of novel recordings and reconstructions reported by the experimental neuroscientists. The extraction of low-order equivalents that capture the essential aspects of the high-accuracy models is an essential part of the simulation process. The complexity of these models require the use of black-box data-oriented reduction approaches. We create a detailed model of the nervous system of a very known organism, C. Elegans, and show that it can be reduced using a modified data-driven model reduction method up to the order of 4 with very little loss in accuracy. The reduced model is able to predict the behaviour of the original for time ranges beyond the data used for the reduction.

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