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Related Concept Videos

Synaptic Signaling01:12

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
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Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...

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3D Modeling of Dendritic Spines with Synaptic Plasticity
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Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis.

Juan Morales1, Angel Rodríguez, José-Rodrigo Rodríguez

  • 1Cajal Blue Brain Project, Facultad de Informática, Universidad Politécnica de Madrid Madrid, Spain.

Frontiers in Neuroanatomy
|July 13, 2013
PubMed
Summary

Researchers developed a computational method to measure the synaptic apposition surface (SAS) area. This measurement offers a new way to understand synapse size and function, complementing existing geometrical analyses.

Keywords:
active zonechemical synapsesdata preprocessingdata visualizationelectron microscopypostsynaptic densitysurface extractionthree-dimensional

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Area of Science:

  • Neuroscience
  • Cell Biology
  • Biophysics

Background:

  • Synapse geometry influences function, particularly synaptic release probability and receptor number.
  • The active zone (AZ) and postsynaptic density (PSD) are key synaptic components.
  • 3D reconstruction of synapses is crucial for detailed morphological analysis.

Purpose of the Study:

  • To develop an automated computational technique for extracting the synaptic apposition surface (SAS).
  • To establish the SAS surface area as a functionally relevant measure of synapse size.

Main Methods:

  • Utilized automated focused ion beam milling and scanning electron microscopy (FIB/SEM) for 3D synapse reconstruction.
  • Developed an efficient computational method to automatically extract the SAS from reconstructed synaptic junctions.
  • Calculated SAS surface area from 3D data of actual tissue samples.

Main Results:

  • Successfully developed and validated an automated method for SAS extraction.
  • Demonstrated that SAS surface area is a functionally relevant metric.
  • Showcased the utility of SAS area alongside other geometrical synapse features.

Conclusions:

  • The automated SAS extraction method provides an efficient way to quantify synapse size.
  • SAS surface area serves as a valuable functional correlate for synapse properties.
  • This technique enhances the morphological analysis of synaptic structures.