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Related Experiment Video

Updated: Jan 9, 2026

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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One-shot Active Neuron Localization with in vivo Fluorescence Image Sequences.

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

This study introduces a new automated method for identifying active neurons in live imaging data. The approach uses one-shot multiple instance learning to reduce manual labor and improve the efficiency of neuron activity analysis.

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Area of Science:

Neuroscience
Computational Biology
Machine Learning

Background:

Active neuron localization in vivo is crucial for understanding neural activity.
Manual observation of fluorescence image sequences is time-consuming and labor-intensive.

Purpose of the Study:

To develop an automated, one-shot active neuron localization method.
To reduce manual intervention and labor costs in neuron activity analysis.

Main Methods:

Formulated the task as a one-shot multiple instance learning (MIL) problem.
Developed an MIL-based framework integrating sequence proposals and temporal filtering.
Utilized a deep neural network trained with self-supervision for feature extraction.
Classified sequences and identified neuron positions by comparing template and proposal features in an MIL fashion.

Main Results:

The proposed method demonstrated a clear performance advantage over existing comparison methods.
Achieved accurate active neuron localization with minimal user intervention.

Conclusions:

The novel MIL-based framework provides an efficient and effective solution for active neuron localization.
This work lays the foundation for advanced physiological analysis of neuron activity.