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Related Concept Videos

Long-term Depression01:03

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over time, all...
Long-term Depression01:05

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation01:25

Long-term Potentiation

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
LTP can occur when presynaptic neurons...
Long-term Potentiation01:35

Long-term Potentiation

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.
Integration of Synaptic Events01:28

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...
Subliminal Perception01:15

Subliminal Perception

Subliminal perception refers to the processing of sensory information that occurs below the level of conscious awareness. Researchers study subliminal perception by presenting a stimulus, such as a word or image, very quickly, typically around 50 milliseconds. This rapid presentation is often followed by another stimulus, such as a pattern of dots or lines, which blocks further mental processing of the initial stimulus. As a result, if participants cannot identify the initial stimulus better...

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Related Experiment Video

Updated: May 9, 2026

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System
09:51

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System

Published on: January 1, 2018

Short term synaptic depression improves information transfer in perceptual multistability.

Zachary P Kilpatrick1

  • 1Department of Mathematics, University of Houston Houston, TX, USA.

Frontiers in Computational Neuroscience
|July 13, 2013
PubMed
Summary

Competitive neural networks with synaptic depression and noise exhibit realistic switching dynamics. Synaptic depression provides more stimulus information than noise-driven switching, and influences perceptual tristability.

Keywords:
binocular rivalrybump attractorneural fieldring modelshort term depression

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Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
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Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

Assessment of Long-term Depression Induction in Adult Cerebellar Slices
09:30

Assessment of Long-term Depression Induction in Adult Cerebellar Slices

Published on: October 16, 2019

Related Experiment Videos

Last Updated: May 9, 2026

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System
09:51

Recording Synaptic Plasticity in Acute Hippocampal Slices Maintained in a Small-volume Recycling-, Perfusion-, and Submersion-type Chamber System

Published on: January 1, 2018

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
09:39

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

Assessment of Long-term Depression Induction in Adult Cerebellar Slices
09:30

Assessment of Long-term Depression Induction in Adult Cerebellar Slices

Published on: October 16, 2019

Area of Science:

  • Computational neuroscience
  • Perceptual dynamics modeling

Background:

  • Perceptual bistability involves switching between competing interpretations of sensory input.
  • Neural network models are crucial for understanding these dynamics.
  • Switching can be driven by internal noise or adaptive processes like synaptic depression.

Purpose of the Study:

  • To analyze switching statistics in competitive neural networks incorporating short-term synaptic depression and noise.
  • To investigate how these factors influence dominance durations and perceptual stability.
  • To model perceptual tristability and history-dependent switching.

Main Methods:

  • Analysis of a ring model with spatially structured solutions.
  • Study of a space-free network with mutual inhibition.
  • Derivation of approximate energy functions for noise-driven switching.
  • Mathematical modeling of competitive networks for bistability and tristability.

Main Results:

  • Dominance times from depression-driven switching can be approximated by timescale separation.
  • Noise-driven switching shows dominance times exponentially related to input strength.
  • Combined depression and noise yield realistic dominance time distributions.
  • Synaptic depression generates history-dependence in perceptual tristability switching.

Conclusions:

  • Short-term synaptic depression and noise create realistic switching statistics in competitive neural networks.
  • Synaptic depression offers more informational content about stimuli compared to noise-driven switching.
  • The model successfully captures history-dependent dynamics in perceptual tristability.