A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays - Neuro-Dol Access content directly
Journal Articles Neural Computation Year : 2024

A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays

Alessio Paolo Buccino
  • Function : Author
Tanguy Damart
  • Function : Author
  • PersonId : 1391576
Julian Bartram
  • Function : Author
  • PersonId : 1391577
Darshan Mandge
  • Function : Author
  • PersonId : 1391578
Xiaohan Xue
  • Function : Author
  • PersonId : 1391579
Mickael Zbili
Tobias Gänswein
  • Function : Author
  • PersonId : 1391580
Aurélien Jaquier
  • Function : Author
  • PersonId : 1391581
Vishalini Emmenegger
  • Function : Author
  • PersonId : 1391582
Henry Markram
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Andreas Hierlemann
  • Function : Author
  • PersonId : 1391583
Werner van Geit
  • Function : Author
  • PersonId : 1391584

Abstract

Abstract In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of nonsomatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density microelectrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at subcellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multimodal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and provide the field with neuronal models that are more realistic and can be better validated.
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Dates and versions

hal-04610077 , version 1 (12-06-2024)

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Alessio Paolo Buccino, Tanguy Damart, Julian Bartram, Darshan Mandge, Xiaohan Xue, et al.. A Multimodal Fitting Approach to Construct Single-Neuron Models With Patch Clamp and High-Density Microelectrode Arrays. Neural Computation, 2024, 36 (7), pp.1286-1331. ⟨10.1162/neco_a_01672⟩. ⟨hal-04610077⟩
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