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Cited by 2Neural dynamics and brain functionModel Reduction and Neural NetworksNeural Networks and Applications

Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit

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Burghi, Thiago B., Schapiro, Kyra, Ivanova, Maria, Wang, Huaxinyu, Marder, Eve, O'Leary, Timothy·2025·Frontiers in Computational Neuroscience
DOI 10.3389/fncom.2025.1515194PMC PMC12451236
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Abstract

Fitting models to experimental intracellular data is challenging. While detailed conductance-based models are difficult to train, phenomenological statistical models often fail to capture the rich intrinsic dynamics of circuits such as central pattern generators (CPGs). A recent trend has been to employ tools from deep learning to obtain data-driven models that can quantitatively learn intracellular dynamics from experimental data. This paper addresses the general questions of modeling, training, and interpreting a large class of such models in the context of estimating the dynamics of a neural circuit. In particular, we use recently introduced Recurrent Mechanistic Models to predict the dynamics of a Half-Center Oscillator (HCO), a type of CPG. We construct the HCO by interconnecting two neurons in the Stomatogastric Ganglion using the dynamic clamp experimental protocol. This allows us to gather ground truth synaptic currents, which the model is able to predict–even though these currents are not used during training. We empirically assess the speed and performance of the training methods of teacher forcing, multiple shooting, and generalized teacher forcing, which we present in a unified fashion tailored to data-driven models with explicit membrane voltage variables. From a theoretical perspective, we show that a key contraction condition in data-driven dynamics guarantees the applicability of these training methods. We also show that this condition enables the derivation of data-driven frequency-dependent conductances, making it possible to infer the excitability profile of a real neuronal circuit using a trained model.

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  • There are attempts to develop new methods to characterize the parameters of biological neurons to estimate or evaluate their contributions to neuronal dynamics.

    “Therefore, there are attempts to develop new methods to characterize the parameters of biological neurons to estimate or evaluate their contributions to neuronal dynamics [48][49][50].”

    From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [49]

  • The speed and performance of the training methods of teacher forcing, multiple shooting, and generalized teacher forcing are tailored to data-driven models with explicit membrane voltage variables.

    “The speed and performance of the training methods of teacher forcing, multiple shooting, and generalized teacher forcing are tailored to data-driven models with explicit membrane voltage variables.”

    From A feature and not a bug: Replication failures can lead to new biology (2026) · cites this paper as [49]

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Cited by

Sorted by citations

Authors
Burghi, T. B., Schapiro, K., Ivanova, M., Wang, H., Marder, E., & O'Leary, T.
Affiliations
  • University of Cambridge (GB)
  • Brandeis University (US)
Corresponding author
Burghi, Thiago B.
ORCID
Venue
Frontiers in Computational Neuroscience
Publisher
Frontiers Media SA
Published
2025-09-01
Volume
19
Pages
1515194-1515194
ISSN
1662-5188 (ISSN-L)
DOI
10.3389/fncom.2025.1515194
PMC
PMC12451236
OpenAlex
W4413869614
Cited by
2
References
1
Citations by year
2026: 2
FWCI
1.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Computational Neuroscience (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Kavli Foundation (Data-driven Control of Neural Dynamics as a Tool for Understanding Resilience to Environmental Temperature Change)
  • National Institutes of Health (3R35NS097343, 5T32NS007292)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
half-center oscillator, central pattern generator, stomatogastric ganglion, synaptic currents, intracellular dynamics, dynamic clamp, teacher forcing, multiple shooting, contraction conditions, frequency-dependent conductance, neuronal excitability

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