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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...

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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Related Experiment Video

Updated: Apr 30, 2026

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Learning precisely timed spikes.

Raoul-Martin Memmesheimer¹, Ran Rubin², Bence P Olveczky³

¹Donders Institute, Radboud University, Nijmegen 6525, the Netherlands; Center for Brain Science, Harvard University, Cambridge, MA 02138, USA.

|April 29, 2014

Summary

This summary is machine-generated.

Neurons can precisely generate timed spike sequences, with a maximum of 0.1-0.3 output spikes per synapse. A new learning rule enables networks to learn input-output spike sequence mappings for neural computation.

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Last Updated: Apr 30, 2026

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Published on: June 2, 2014

Area of Science:

Computational neuroscience
Neural coding
Systems neuroscience

Background:

Neuronal circuits process sensory information and control motor actions through precisely timed neural firing.
Understanding the efficiency and accuracy of neural computations is crucial for deciphering brain function.

Purpose of the Study:

To develop a theoretical framework for quantifying the capacity of feedforward neural networks to generate specific spike sequences.
To introduce a biologically plausible learning rule for neural networks to learn input-output spike sequence mappings.
To apply this framework to reconstruct synaptic weights and infer behavioral temporal structure from neural activity.

Main Methods:

Developed a theory to characterize the computational capacity of feedforward networks for spike sequence generation.
Proposed a biologically plausible learning rule applicable to both feedforward and recurrent neural networks.
Applied the framework to analyze synaptic plasticity and decode temporal dynamics from premotor neuron spiking.

Main Results:

Determined the maximum number of desired output spikes a single neuron can implement is 0.1-0.3 per synapse.
Demonstrated that the learning rule enables networks to learn multiple input-to-output spike sequence transformations.
Showcased the framework's ability to reconstruct synaptic weights and infer behavioral timing from neural data.

Conclusions:

The developed theory provides a quantitative measure of neural computation efficiency in generating spike sequences.
The novel learning rule facilitates adaptive neural processing and learning of complex temporal patterns.
This research offers a powerful analytical tool for understanding the computational and learning capabilities of neural circuits.