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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
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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.

Plasticity

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Neuroplasticity

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Related Experiment Video

Updated: Jan 5, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Embodied Synaptic Plasticity With Online Reinforcement Learning.

Jacques Kaiser¹, Michael Hoff^1,2, Andreas Konle¹

¹FZI Research Center for Information Technology, Karlsruhe, Germany.

Frontiers in Neurorobotics

|October 22, 2019

Summary

This summary is machine-generated.

This study integrates computational neuroscience and robotics to test brain plasticity models in real-world robotic tasks. A novel framework demonstrates a biologically-plausible learning rule (SPORE) capable of visuomotor control.

Keywords:

neuromorphic vision neurorobotics reinforcement learning spiking neural networks synaptic plasticity

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Published on: November 11, 2017

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Published on: October 8, 2019

Area of Science:

Computational Neuroscience
Robotics
Artificial Intelligence

Background:

Understanding the brain requires integrating theoretical models with biological function.
Traditional synaptic plasticity models are often tested in isolation, not in closed-loop control scenarios.
Bridging computational neuroscience and robotics is crucial for realistic brain modeling.

Purpose of the Study:

To develop a framework integrating computational neuroscience and robotics for evaluating biologically-plausible plasticity models.
To assess the Synaptic Plasticity with Online REinforcement learning (SPORE) rule in closed-loop robotic tasks.
To explore the performance of SPORE in visuomotor control.

Main Methods:

Integration of open-source software from computational neuroscience and robotics.
Development of a framework for evaluating plasticity models in closed-loop robotic environments.
Application and testing of the SPORE learning rule on simulated reaching and lane-following tasks.

Main Results:

The SPORE rule successfully learned policies for visuomotor tasks within simulated hours.
Parameter exploration indicated that learning rate and temperature are critical for retaining performance improvements.
The framework facilitates the evaluation of biologically-inspired learning rules in robotics.

Conclusions:

The developed framework effectively bridges computational neuroscience and robotics.
SPORE demonstrates potential for learning complex visuomotor behaviors in robots.
Future work should explore advanced deep reinforcement learning techniques to enhance SPORE's capabilities.