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Related Concept Videos

The Synapse02:47

The Synapse

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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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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Overview of Synapses01:25

Overview of Synapses

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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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Chemical Synapses01:26

Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
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Chemical Synapses01:26

Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
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Synaptic Signaling01:09

Synaptic Signaling

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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.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
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Related Experiment Video

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Evaluation of Synapse Density in Hippocampal Rodent Brain Slices
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Evaluation of Synapse Density in Hippocampal Rodent Brain Slices

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Identifiability of a Binomial Synapse.

Camille Gontier1, Jean-Pascal Pfister1,2

  • 1Department of Physiology, University of Bern, Bern, Switzerland.

Frontiers in Computational Neuroscience
|October 29, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a formal definition for practical identifiability of statistical models, crucial for accurately estimating synaptic transmission parameters. It provides a quantitative criterion to assess model reliability in neuroscience research.

Keywords:
binomialmodel selectionpractical identifiabilitystructural identifiabilitysynapse

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Statistical Modeling

Background:

  • Synaptic transmission is inherently stochastic, often modeled using the binomial model.
  • Accurate parameter estimation from synaptic responses relies on model identifiability.
  • Practical identifiability, dependent on experimental design, is typically assessed qualitatively.

Purpose of the Study:

  • To formally define the practical identifiability domain of statistical models.
  • To develop a non-arbitrary criterion for practical identifiability using model selection.
  • To analyze neurotransmitter release at chemical synapses using this new framework.

Main Methods:

  • Proposed a formal definition for the practical identifiability domain of statistical models.
  • Applied a model selection approach to derive a criterion for practical identifiability.
  • Extended the Bayesian Information Criterion (BIC) for models with correlated data.

Main Results:

  • Developed a quantitative criterion for practical identifiability and computed identifiability domains for binomial release models.
  • Demonstrated data-free model selection to verify model identifiability post-fitting.
  • Showcased the application to analyze synaptic stochasticity and neurotransmitter release.

Conclusions:

  • The proposed framework offers a quantitative method to assess practical identifiability in statistical models.
  • This approach enhances the reliability of parameter estimation in synaptic transmission studies.
  • The data-free model selection capability provides a novel tool for validating model choices.