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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.

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.

Integration of Synaptic Events

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...

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...

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...

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Related Experiment Video

Updated: Mar 19, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

Learning may need only a few bits of synaptic precision.

Carlo Baldassi^1,2, Federica Gerace^1,2, Carlo Lucibello^1,2

¹Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy.

Physical Review. E

|June 15, 2016

Summary

This summary is machine-generated.

Neural network learning with discrete synaptic states is efficient, even with few bits of precision. This study extends prior analysis to multi-state synapses, confirming robustness and guiding practical applications.

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Last Updated: Mar 19, 2026

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

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Published on: November 11, 2017

Presynaptically Silent Synapses Studied with Light Microscopy

Presynaptically Silent Synapses Studied with Light Microscopy

Published on: January 4, 2010

3D Modeling of Dendritic Spines with Synaptic Plasticity

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Published on: May 18, 2020

Area of Science:

Computational Neuroscience
Machine Learning Theory
Artificial Neural Networks

Background:

Discretized synaptic states present unique learning challenges in neural networks.
Biological plausibility and hardware constraints motivate the use of discrete synapses.

Purpose of the Study:

To extend large deviations analysis to multi-state synapses.
To investigate the robustness of efficient learning in networks with discrete synapses.
To determine optimal synaptic precision for practical applications.

Main Methods:

Extended a previous large deviations analysis.
Analyzed networks with multi-state and biologically plausible synaptic features.
Performed quantitative analysis of synaptic precision's impact on learning.

Main Results:

The qualitative picture of efficient learning remains consistent across different discrete synapse models.
Synaptic precision beyond a few bits offers diminishing returns for learning efficiency.
Theoretical analysis can inform the design of efficient algorithmic search strategies.

Conclusions:

Neural network learning with discrete synapses is robust and efficient.
Optimal performance can be achieved with limited synaptic precision (few bits).
The findings support practical applications and efficient algorithm design.