A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

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 non-somatic 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 micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular 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 multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated.

Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra‐ and extracellular recordings

“…In the most recent study, Buccino et al. (2022) combined patch‐clamp and planar two‐dimensional, high‐density multi‐electrode array (MEA) recordings and also performed cell morphology reconstruction; however, they obtained their data from embryonic rat cell culture.…”

“…Anastassiou et al (2015) were the first to use multichannel silicon probes in colocalized and simultaneous recordings. In the most recent study, Buccino et al (2022) combined patch-clamp and planar two-dimensional, high-density multi-electrode array (MEA) recordings and also performed cell morphology reconstruction; however, they obtained their data from embryonic rat cell culture.…”

“…Multicompartmental models are usually fitted to electrophysiological recordings obtained by single somatic patch‐clamp data. However, single patch‐clamp recordings at the somatic region will never allow us to observe other distinct, local neuronal events, such as the very specific behaviour of the axon initial segment or the active Ca 2 + and Na + spikes generated by more distal dendrites that are believed to be essential for dendritic integration, computation and synaptic plasticity (Buccino et al., 2022; Gidon et al., 2020; Kumar et al., 2022). Building a well‐grounded compartmental model requires a detailed description of channel dynamics, in addition to their density and distribution along the whole neuron, which is still a major challenge.…”

Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation

“…At the same time, missing cerebellar models, such as candelabrum cells ( Osorno et al, 2022 ), have recently seen a surge in interest that can induce other labs to further study them. The missing data for both known and less known neurons can be optimally obtained using advanced recording techniques, including high-density Multi electrode array ( Buccino et al, 2022 ), voltage-sensitive dye imaging and multispot 2-photon laser microscopy ( Ebner and Chen, 1995 ; Gandolfi et al, 2014 ; Miccoli et al, 2019 ; Gagliano et al, 2022 ), as well as tools to target and dissect specific circuit components, such as genetic engineering and optogenetics ( Yang et al, 2018 ; Adam et al, 2019 ; Tognolina et al, 2022 ). We envisage that this roadmap will eventually shed new light on the computational implications of synaptic mechanisms and contribute to explaining the intrinsic algorithms of the cerebellar circuit.…”