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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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Related Experiment Video

Updated: Feb 26, 2026

DetectSyn: A Rapid, Unbiased Fluorescent Method to Detect Changes in Synapse Density

DetectSyn: A Rapid, Unbiased Fluorescent Method to Detect Changes in Synapse Density

Published on: July 22, 2022

SynEM, automated synapse detection for connectomics.

Benedikt Staffler¹, Manuel Berning¹, Kevin M Boergens¹

¹Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.

|July 15, 2017

Summary

This summary is machine-generated.

We developed SynEM, an automated method for detecting synapses in 3D electron microscopy images. This tool accurately identifies millions of synapses, significantly advancing connectomic mapping of neural circuits.

Keywords:

cerebral cortex connectomics electron microscopy machine learning mouse neuroscience synapses

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Last Updated: Feb 26, 2026

DetectSyn: A Rapid, Unbiased Fluorescent Method to Detect Changes in Synapse Density

DetectSyn: A Rapid, Unbiased Fluorescent Method to Detect Changes in Synapse Density

Published on: July 22, 2022

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Automated Quantification of Synaptic Fluorescence in C. elegans

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Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Published on: November 16, 2010

Area of Science:

Neuroscience
Computational Biology
Microscopy

Background:

Nerve tissue has a high density of chemical synapses, necessitating efficient methods for large-scale connectomic mapping.
Current connectomic data analysis focuses on neurite reconstruction, with synapse detection becoming a bottleneck for dense mapping.
Manual synapse annotation is time-consuming and impractical for large datasets.

Purpose of the Study:

To develop an automated method for synapse detection in 3D electron microscopy datasets.
To overcome the limitations of manual synapse annotation in dense connectomic mapping.
To enable scalable analysis of neural circuits at synaptic resolution.

Main Methods:

SynEM employs image segmentation to classify borders between neuronal processes.
The method focuses on distinguishing synaptic from non-synaptic junctions.
Automated detection is performed on conventionally en-bloc stained 3D electron microscopy image stacks.

Main Results:

SynEM achieves 97% precision and recall for synapse detection in cortical connectomes.
The method requires no user interaction for synapse identification.
SynEM demonstrates scalability for analyzing large volumes of neural tissue, including potential whole-brain datasets.

Conclusions:

SynEM provides an accurate and efficient solution for automated synapse detection.
This method significantly reduces the manual effort required for dense connectomic mapping.
SynEM facilitates large-scale analysis of neural circuitry and synaptic organization.