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Related Concept Videos

Long-term Potentiation01:25

Long-term Potentiation

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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
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Chemical Synapses01:26

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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The Synapse02:47

The Synapse

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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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Integration of Synaptic Events01:28

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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...
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Electrical Synapses01:28

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
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Synaptic Signaling01:09

Synaptic Signaling

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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.
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3D Modeling of Dendritic Spines with Synaptic Plasticity
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Heterosynaptic Plasticity in a Vertical Two-Terminal Synaptic Device.

Haena Yim1, Chansoo Yoon2, Ahrom Ryu1

  • 1Center for Electronic Materials, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

Nano Letters
|July 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-terminal synaptic device that mimics brain-like learning. The device achieves heterosynaptic plasticity, enabling advanced learning functions in neuromorphic systems without extra terminals.

Keywords:
2D perovskitesDion−Jacobson phaseartificial synapseheterosynaptic plasticityneuromodulationneuromorphic device

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Area of Science:

  • Materials Science
  • Neuroscience
  • Electrical Engineering

Background:

  • Resistive switching synaptic devices show promise for artificial intelligence.
  • Mimicking heterosynaptic behavior typically requires an extra terminal, hindering scalability.

Purpose of the Study:

  • To develop a scalable two-terminal device emulating heterosynaptic plasticity.
  • To control synaptic plasticity and learning functions in a simplified device structure.

Main Methods:

  • Fabrication of a vertical two-terminal Pt/bilayer Sr1.8Ag0.2Nb3O10 (SANO) nanosheet/Nb:SrTiO3 (Nb:STO) device.
  • Modulation of trap sites in the SANO nanosheet via tunneling current to control plasticity.
  • Investigation of synaptic plasticity, pulsed pair facilitation, and cutoff frequency.

Main Results:

  • The device successfully emulated heterosynaptic plasticity without an additional terminal.
  • Synaptic plasticity, pulsed pair facilitation, and cutoff frequency were modulated.
  • The device demonstrated control over trap site density through tunneling current.

Conclusions:

  • The developed two-terminal device offers a scalable solution for neuromorphic computing.
  • It enables high-level learning functions, such as associative learning, in simple cross-bar arrays.
  • This approach simplifies the implementation of complex learning behaviors in artificial neural networks.