Swarm plots of the validation loss for each of the compartments (basal, oblique, apical) of the three LBTW-trained models: Hard Parameter Sharing (MH), Multi-gate Mixture-of-experts (MMoE), and Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx).

<p>Swarm plots of the validation loss for each of the compartments (basal, oblique, apical) of the three LBTW-trained models: Hard Parameter Sharing (MH), Multi-gate Mixture-of-experts (MMoE), and Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx).</p>

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Bibliographic Details
Main Author:	Jonas Verhellen (19267988) (author)
Other Authors:	Kosio Beshkov (19267991) (author), Sebastian Amundsen (19267994) (author), Torbjørn V. Ness (5900183) (author), Gaute T. Einevoll (7300964) (author)
Published:	2024
Subjects:	Biophysics Cell Biology Neuroscience Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified lfps ), electroencephalogram important downstream application human brain operates artificial neural networks mtl architectures due mtl ), allows classical simulation methods membrane potentials together detailed neuron models predict voltage outputs detailed neuron model detailed models neuron model membrane potentials neuron compartments art architectures accurately predict model output electrical potentials xlink "> wider range two orders simultaneous prediction several orders recent innovations paper lies optical readouts neighbouring compartments multitask learning multiple levels main focus magnitude faster magnitude compared large amount gaussian distribution experimental recordings entire electrophysiology enhanced state effective method data involved computationally expensive challenging benchmark also provides
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Swarm plots of the validation loss for each of the compartments (basal, oblique, apical) of the three LBTW-trained models: Hard Parameter Sharing (MH), Multi-gate Mixture-of-experts (MMoE), and Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx).

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