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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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Related Experiment Video

Updated: May 5, 2026

A Method for Growing Bio-memristors from Slime Mold

A Method for Growing Bio-memristors from Slime Mold

Published on: November 2, 2017

Memristive Artificial Synapses Based on Brownmillerite for Endurable Weight Modulation.

Yoon Jung Lee^1,2, Eun Seok Choi³, Ji Hyun Baek¹

¹Department of Material Science and Engineering, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.

Small (Weinheim an Der Bergstrasse, Germany)

|October 29, 2024

Summary

This study harnesses topotactic phase transitions in SrCoO2.5 memristors for reliable artificial synapses. This approach enhances synaptic weight updates, improving neural network performance and endurability.

Keywords:

1‐D Oxygen vacancy channels artificial synapse brownmillerite SrCoO2.5 neuromorphic Hardware topotactic phase transition

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Published on: July 26, 2024

Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Artificial synapses are crucial for computing paradigms that merge memory and computation.
While memristors offer energy efficiency for artificial synapses, their long-term synaptic modulation is limited by random filament conduction.
Enhancing the endurability and reliability of synaptic weight updates is essential for advanced neural network applications.

Purpose of the Study:

To leverage topotactic phase transition (TPT) in brownmillerite-phased SrCoO2.5 (SCO2.5) for improved artificial synapse endurability.
To demonstrate a novel memristive synapse design utilizing TPT for reversible oxygen ion migration.
To validate the performance of TPT-based artificial synapses in deep and convolutional neural networks.

Main Methods:

Fabrication of a heteroepitaxial Au/SCO2.5/SrRuO3/SrTiO3 2-terminal device.
Utilizing density-functional theory (DFT) calculations and experimental Raman spectroscopy to demonstrate TPT behavior.
Applying voltage pulses to induce TPT and characterize synaptic plasticity (long-term potentiation and depression).

Main Results:

Demonstrated reliable, linear, and symmetric long-term potentiation and depression via voltage pulse-driven TPT.
Achieved consistent and noise-free synaptic weight updates over 32,000 iterations and 640 cycles, showcasing high durability.
Attained very high recognition accuracy in deep neural networks and convolutional neural networks for image datasets.

Conclusions:

Topotactic phase transition in brownmillerite SCO2.5 provides a robust mechanism for reliable and endurable artificial synaptic weight updates.
The developed TPT-based memristive synapse shows significant potential for advancing neuromorphic computing and artificial intelligence.
This work offers critical insights for designing next-generation memristive devices for efficient and dependable neural network implementations.