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Inferring and validating mechanistic models of neural microcircuits based on spike-train data
Josef Ladenbauer1, Sam McKenzie2, Daniel Fine English3
1Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, École Normale Supérieure, PSL Research University, 29 rue d'Ulm, 75005, Paris, France. josef.ladenbauer@gmail.com.
Nature Communications
|November 1, 2019
Summary
This study introduces new analytical methods to accurately fit spiking circuit models to neuronal data. These methods enable quantitative, mechanistic interpretations of neural activity, bridging statistical and model-based neuroscience.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Theoretical Neuroscience
Background:
- Statistical models offer parameter estimation but limited microcircuit insights.
- Mechanistic models offer microcircuit insights but are difficult to quantitatively match to experimental data.
- Methodological challenges hinder quantitative fitting of mechanistic spiking circuit models.
Purpose of the Study:
- To develop analytical methods for efficiently fitting spiking circuit models to single-trial spike trains.
- To enable quantitative, mechanistic interpretation of recorded neuronal population activity.
- To bridge statistical, data-driven, and theoretical neuroscience approaches.
Main Methods:
- Derived likelihood functions for statistical inference.
- Parameter estimation of hidden inputs, neuronal adaptation, and connectivity.
- Analysis of coupled integrate-and-fire neuron models.
Main Results:
- Accurate and efficient parameter estimation for spiking circuit models.
- Successful validation on synthetic, in-vitro, and in-vivo recordings.
- Effective performance even with highly subsampled network data.
Conclusions:
- The developed methods provide a quantitative link between experimental data and mechanistic models.
- This approach facilitates a deeper understanding of microcircuit dynamics.
- It advances the mechanistic interpretation of neuronal population activity.
