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Related Concept Videos

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.
Long-term Potentiation01:25

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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.
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Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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Related Experiment Video

Updated: May 25, 2026

Ex Vivo Optogenetic Interrogation of Long-Range Synaptic Transmission and Plasticity from Medial Prefrontal Cortex to Lateral Entorhinal Cortex
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Ex Vivo Optogenetic Interrogation of Long-Range Synaptic Transmission and Plasticity from Medial Prefrontal Cortex to Lateral Entorhinal Cortex

Published on: February 25, 2022

Stochastic perturbation methods for spike-timing-dependent plasticity.

Todd K Leen1, Robert Friel

  • 1Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA. leent@ohsu.edu

Neural Computation
|February 3, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel perturbation expansion for synaptic weight dynamics in machine learning and spike-timing-dependent plasticity (STDP). This method accurately models jump process Markov chains where traditional Fokker-Planck equations fail.

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Area of Science:

  • Computational neuroscience
  • Machine learning theory
  • Statistical physics

Background:

  • Many machine learning and biological learning rules, such as spike-timing-dependent plasticity (STDP), create jump process Markov chains for synaptic weights.
  • Approximating these dynamics with Fokker-Planck equations (FPE) can be inaccurate in certain regimes.

Purpose of the Study:

  • To develop a more accurate analytical method for modeling synaptic weight dynamics.
  • To provide a robust alternative to the Fokker-Planck equation approximation for jump process Markov chains.

Main Methods:

  • A novel perturbation expansion for the dynamics of synaptic weights was developed.
  • This approach extends existing system size expansions to include probability densities and moments.
  • The method was applied to analyze two specific STDP learning rules.

Main Results:

  • The perturbation expansion provides a well-justified approximation for jump process Markov chains.
  • The new method shows strong agreement with Monte Carlo simulations in regimes where FPE breaks down.
  • The approach is also applicable to the dynamics of stochastic neural activity.

Conclusions:

  • The developed perturbation expansion offers a superior analytical tool for understanding synaptic plasticity and neural dynamics.
  • This method enhances the accuracy of modeling complex stochastic processes in neural systems.
  • The framework can be extended to analyze transient dynamics beyond equilibrium solutions.