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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.

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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.
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Related Experiment Video

Updated: Aug 28, 2025

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

Inferring stimulation induced short-term synaptic plasticity dynamics using novel dual optimization algorithm.

Alireza Ghadimi^1,2,3, Leon Amadeus Steiner^1,4,5,6, Milos R Popovic^2,3

¹Krembil Research Institute - University Health Network (UHN), Toronto, Ontario, Canada.

|September 21, 2022

Summary

This summary is machine-generated.

This study introduces a new computational model to predict short-term synaptic plasticity (STP) dynamics during deep brain stimulation (DBS). The model uses DBS frequencies to infer STP parameters without needing pre-synaptic activity.

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

Published on: January 1, 2018

Area of Science:

Computational Neuroscience
Synaptic Plasticity
Neurostimulation

Background:

Deep brain stimulation (DBS) is known to induce short-term synaptic plasticity (STP).
Understanding DBS-induced STP is crucial for its therapeutic applications.
Current methods for estimating STP require pre-synaptic activity, which is not always available in DBS.

Purpose of the Study:

To develop a computational predictive model for inferring STP dynamics in response to DBS at various frequencies.
To overcome the limitation of requiring pre-synaptic spiking activity in existing STP estimation methods.

Main Methods:

A model-based approach integrating multiple DBS frequencies as pre-synaptic spikes.
Inference of Tsodyks-Markram (TM) model parameters from post-synaptic currents.
A novel dual optimization algorithm distinguishing steady-state and transient responses.
Application to empirical data from rodent subthalamic nucleus (STN) slices during DBS-like stimulation.

Main Results:

The dual optimization algorithm successfully inferred TM model parameters from post-synaptic currents.
The model accurately predicted STP dynamics for inhibitory synaptic inputs.
Proof-of-concept validation using experimental data from STN slices.

Conclusions:

The developed computational model provides a novel method for predicting DBS-induced STP dynamics.
This approach bypasses the need for direct pre-synaptic activity measurements.
The findings have implications for understanding and optimizing DBS therapies.