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Related Concept Videos

Integration of Synaptic Events01:28

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...
Long-term Potentiation01:25

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 presynaptic neurons...
Long-term Potentiation01:35

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.
Chemical Synapses01:26

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
Chemical Synapses01:26

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
The Synapse02:47

The Synapse

Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.

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Updated: May 27, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Published on: March 9, 2019

Crystallization-Driven Stable Resistive Switching and Reproducible Synaptic Learning in GeSe-Based Artificial

Girish U Kamble1, Somnath S Kundale2,3, Dhanaji Malavekar1

  • 1Optoelectronics Convergence Research Center and Department of Materials Science and Engineering, Chonnam National University, Gwangju 61186, Republic of Korea.

ACS Applied Materials & Interfaces
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study showcases crystalline germanium selenide (GeSe) memristive devices that exhibit associative learning for neuromorphic computing. These devices offer stable, energy-efficient performance for artificial intelligence hardware.

Keywords:
Pavlovian associative learninggermanium selenide (GeSe)neuromorphic computingresistive switchingsynaptic plasticitythin films

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Published on: January 1, 2018

Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Neuroscience

Background:

  • The demand for high-performance, energy-efficient neuromorphic systems is increasing.
  • Chalcogenide materials are being explored for their tunable electronic properties in neuromorphic applications.

Purpose of the Study:

  • To demonstrate Ag/GeSe/FTO resistive-switching devices capable of Pavlovian associative learning.
  • To compare the performance of amorphous and crystalline GeSe thin films for memristive applications.
  • To establish crystalline GeSe as a viable material for artificial intelligence hardware.

Main Methods:

  • RF sputtering deposition of GeSe thin films.
  • Annealing to induce phase crystallization.
  • Structural and electrical characterization of resistive-switching devices.
  • Analysis of conduction mechanisms and ion migration.
  • Implementation of an artificial neural network (ANN) using device characteristics.

Main Results:

  • Crystalline GeSe devices exhibit superior performance: lower SET/RESET voltages, larger hysteresis, high endurance (>4000 cycles), and long retention (>1500 s).
  • Conduction transitions to space-charge-limited transport, enabling controlled Ag+ ion migration and stable filament formation.
  • Devices demonstrate stable potentiation, depression, paired-pulse facilitation, and inhibition of long-term potentiation, mimicking synaptic behavior.
  • Successful reproduction of associative learning and ANN classification accuracies up to ~87% on MNIST datasets.

Conclusions:

  • Crystalline GeSe is a promising, stable, and cost-effective memristive material for energy-efficient neuromorphic and AI hardware.
  • The demonstrated associative learning capabilities highlight the potential for advanced computing paradigms.
  • The findings support the use of GeSe in next-generation artificial intelligence systems.