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Related Experiment Video

Updated: May 20, 2025

Fluorescent Calcium Imaging and Subsequent In Situ Hybridization for Neuronal Precursor Characterization in Xenopus laevis

Fluorescent Calcium Imaging and Subsequent In Situ Hybridization for Neuronal Precursor Characterization in Xenopus laevis

Published on: February 18, 2020

Precise calcium-to-spike inference using biophysical generative models.

Gerard Joey Broussard¹, Giovanni Diana², Francisco J Urra Quiroz²

¹Neuroscience Institute, Washington Road, Princeton University, Princeton, New Jersey USA 08540.

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|May 2, 2025

Summary

This summary is machine-generated.

New algorithms improve neural spike time accuracy. Biophysical models for GCaMP indicators reduce false positives, achieving near-theoretical limits for precise action potential detection.

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Last Updated: May 20, 2025

Fluorescent Calcium Imaging and Subsequent In Situ Hybridization for Neuronal Precursor Characterization in Xenopus laevis

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Published on: February 18, 2020

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Published on: November 26, 2019

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Area of Science:

Neuroscience
Biophysics
Biochemistry

Background:

Intramolecular dynamics of neural activity indicators can affect action potential (spike) time estimation.
Existing inference algorithms may produce inaccuracies due to indicator kinetics.

Purpose of the Study:

To develop a more accurate spike inference algorithm.
To characterize the kinetic responses of GCaMP6f, jGCaMP7f, and jGCaMP8f indicators to calcium.

Main Methods:

In vitro stopped-flow and brain slice recordings were used to analyze indicator kinetics.
A multistate model of GCaMP was developed based on experimental data.
Bayesian Sequential Monte Carlo (Biophys_SMC) and machine learning (Biophys_ML) inference methods were created.

Main Results:

jGCaMP8f exhibited use-dependent slowing, leading to false positives with current methods.
The new biophysical inference methods significantly reduced false positives.
Spike time accuracy improved, with a median uncertainty of 4 milliseconds, reaching theoretical limits.

Conclusions:

Model-based approaches offer advantages over model-free algorithms for spike inference.
The developed biophysical methods provide a substantial improvement in action potential detection accuracy.
This framework enhances the precise estimation of neural activity timing.