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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.

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...

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Syringe-injectable Mesh Electronics for Stable Chronic Rodent Electrophysiology

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Published on: July 21, 2018

Array-level MXene electrode flexible long-term-plasticity synaptic transistors.

Zifan Wang¹, Jiahao Zhu¹, Dexing Liu^1,2

¹School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.

Materials Horizons

|June 10, 2025

Summary

This summary is machine-generated.

Researchers developed novel flexible synaptic transistors using MXene electrodes, achieving long-term plasticity for efficient artificial nervous systems. This breakthrough enables high-speed, low-power neuromorphic computing for advanced applications.

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Area of Science:

Materials Science
Neuroscience
Electrical Engineering

Background:

Artificial nervous devices aim to replicate brain functions for advanced computing.
Flexible synaptic transistors are crucial for emulating neural pathways but face integration and material challenges.
Existing technologies struggle with long-term plasticity and mass production.

Purpose of the Study:

To develop a high-integration flexible synaptic transistor array with long-term plasticity.
To overcome structural and material limitations in current artificial nervous devices.
To enable efficient neuromorphic computing and temporal fusion information storage.

Main Methods:

Fabrication of novel array-level MXene-electrode flexible synaptic transistors.
Utilizing Ti3C2Tx MXene electrodes for proton-induced dielectric defect filling to achieve plasticity.
Employing MXene electrode patterning for high integration on flexible substrates.
Achieving Ohmic contact via matched work functions between MXene and carbon nanotubes.

Main Results:

Demonstrated long-term synaptic plasticity exceeding 1000 seconds without complex structures.
Achieved high integration and array-level patterning on flexible substrates.
Developed an MXene electrode flexible synaptic transistor (MEFST) array.
Attained 93.8% accuracy in handwritten digit recognition, showcasing temporal fusion capabilities.

Conclusions:

The novel MEFST array offers a viable platform for artificial neuromorphic computing.
Long-term plasticity enables rapid target identification and re-iteration-free recognition.
Potential applications include neuromorphic electronic skin, intelligent wearables, and edge computing.