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Long-term Potentiation

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 Potentiation

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.

Neuroplasticity

Neuroplasticity

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.

Long-term Depression

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 Depression

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.

Plasticity

Plasticity

Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...

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Related Experiment Video

Updated: May 23, 2026

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

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

Published on: February 25, 2022

Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity.

Jason F Hunzinger¹, Victor H Chan, Robert C Froemke

¹Qualcomm Research, San Diego, CA 92121, USA. jhunzing@qualcomm.com

Journal of Neurophysiology

|April 13, 2012

Summary

This study introduces a shared resource model for synaptic plasticity, enabling neural networks to rapidly learn complex temporal patterns. The model accurately predicts spike timing-dependent plasticity (STDP) and rate-dependent effects with a single parameter.

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Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

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

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Published on: February 25, 2022

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

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Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

Published on: August 11, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Synaptic plasticity, including long-term potentiation (LTP) and long-term depression (LTD), underlies learning and memory.
Spike-timing-dependent plasticity (STDP) demonstrates that synaptic strength changes depend on the precise timing of pre- and postsynaptic spikes.
Current models often treat LTP and LTD as separate mechanisms, potentially limiting their explanatory power for complex neural computations.

Purpose of the Study:

To propose a novel shared resource model for synaptic plasticity.
To demonstrate the model's capability for fast, stable, and diverse unsupervised learning of temporal multispike patterns.
To investigate how interdependencies within the model facilitate the decoding of temporally coded information.

Main Methods:

Development of a biologically consistent spiking neural network model.
Integration of interdependent long-term potentiation (LTP) and long-term depression (LTD) mechanisms.
Introduction of dendritic delays and homeostatic properties into the neural network architecture.

Main Results:

The shared resource model enables unsupervised learning of complex temporal spike patterns with high speed and stability.
Neurons effectively decode temporally coded information within spike bursts, even with significant noise and jitter.
The model accurately predicts in vitro STDP observations for complex multispike trains and rate-dependent effects using a single parameter.

Conclusions:

A shared resource model offers a unified framework for understanding synaptic plasticity mechanisms.
This model provides a biologically plausible explanation for efficient temporal information processing in neural networks.
The findings suggest potential commonalities in natural long-term plasticity mechanisms and advance unsupervised learning in spiking neural networks.