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The Synapse

The Synapse

Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.

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Synapseg: A Novel Dataset And Image Analysis Framework For Deep Learning-based Synapse Detection And Quantification.

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Synapseg: A Novel Dataset And Image Analysis Framework For Deep Learning-based Synapse Detection And Quantification.

Related Experiment Video

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SynAPSeg: A novel dataset and image analysis framework for deep learning-based synapse detection and quantification.

Pascal Schamber¹, Sahana Darbhamulla¹, Molly Boyer¹

¹Department of Neuroscience, Tufts University, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology

|March 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

SynAPSeg offers a novel deep learning framework for analyzing synaptic puncta, enabling large-scale mapping of neural circuits and revealing age-related synaptic changes in the brain.

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Area of Science:

Neuroscience
Computational Neuroscience
Biophysics

Background:

Quantifying synaptic organization at circuit scales is a major challenge in neuroscience.
Current deep learning tools often fail to segment synaptic puncta in dense tissues.
There is a need for robust, scalable analysis methods for synaptic architecture.

Purpose of the Study:

To introduce SynAPSeg, an open-source deep learning framework for synaptic puncta segmentation and analysis.
To develop and validate deep learning models for accurate synaptic puncta quantification.
To enable large-scale mapping of synaptic organization and study synaptic changes in aging.

Main Methods:

Developed SynAPSeg, an open-source deep learning framework.
Created the first large-scale, publicly available instance segmentation dataset for synaptic puncta.

Trained deep learning models achieving expert-level performance on benchmark datasets.

Integrated models into an interactive interface supporting multi-dimensional data and automated pipelines.

Applied SynAPSeg to map millions of PSD95 puncta in the hippocampus and analyze aging-related synaptic changes in CA1 parvalbumin neurons.

Main Results:

SynAPSeg models achieved expert-level performance in synaptic puncta segmentation.
A comprehensive mapping of nearly 4 million PSD95 puncta in dorsal hippocampus inhibitory interneurons revealed regional differences.
Analysis of aged CA1 parvalbumin neurons showed a reduction in PSD95 density, indicating impaired glutamatergic recruitment.
The framework demonstrated scalability for large-scale synaptic architecture analysis.

Conclusions:

SynAPSeg provides a scalable, automated solution for synaptic puncta segmentation and quantification.
The framework facilitates large-scale synaptic mapping and the study of synaptic plasticity in health and disease.
Findings suggest that reduced PSD95 density in aged CA1 PV neurons may contribute to cognitive decline.