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Related Concept Videos

Integration of Synaptic Events01:28

Integration of Synaptic Events

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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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Synaptic Signaling01:12

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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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Synaptic Signaling01:09

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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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Overview of Synapses01:25

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A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
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Related Experiment Video

Updated: May 2, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
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Data-driven modeling of synaptic transmission and integration.

Jason S Rothman1, R Angus Silver1

  • 1Department of Neuroscience, Physiology & Pharmacology, University College London, London, UK.

Progress in Molecular Biology and Translational Science
|February 25, 2014
PubMed
Summary
This summary is machine-generated.

This chapter details mathematical modeling of synaptic transmission and integration, focusing on excitatory synapses. It provides a data-driven approach for understanding neural network behavior in health and disease.

Keywords:
AMPA receptorChemical synapsesConductance waveformsDepletion modelsIntegrate-and-fire modelsMathematical modelsNMDA receptorPoisson spike trainsQuantal releaseShort-term plasticitySynaptic depressionSynaptic integrationSynaptic transmissionVesicular release

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Area of Science:

  • Computational Neuroscience
  • Mathematical Biology
  • Neuroscience

Background:

  • Current understanding of synaptic transmission relies on experimental evidence.
  • The mammalian cerebellum's mossy fiber to granule cell synapse serves as a model system.
  • This synapse is well-characterized, providing a benchmark for mathematical models.

Purpose of the Study:

  • To describe the creation of mathematical models for synaptic transmission and integration.
  • To provide a benchmark comparison for synaptic property modeling using a specific cerebellar synapse.
  • To offer a data-driven approach for modeling synaptic transmission and network behavior.

Main Methods:

  • Synopsis of experimental evidence for synaptic transmission.
  • Description of synaptic transmission at the mossy fiber to granule cell synapse.
  • Presentation of mathematical models, starting with simple conductance waveforms and progressing to complex properties like nonlinear voltage dependence, short-term plasticity, and stochastic fluctuations.

Main Results:

  • Development of mathematical descriptions for average synaptic conductance waveforms.
  • Incorporation of complex synaptic properties into models, including nonlinear voltage dependence, short-term plasticity, and stochastic fluctuations.
  • Demonstration that modeling approaches for excitatory synapses can be adapted for inhibitory synapses.

Conclusions:

  • The chapter provides a structured approach to mathematical modeling of synaptic transmission.
  • The methods discussed are applicable to understanding neural network behavior in both healthy and diseased states.
  • This data-driven modeling framework is valuable for researchers in computational neuroscience and related fields.