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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.
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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.
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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...
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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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Related Experiment Video

Updated: May 22, 2025

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Heterointerface-Modulated Synthetic Synapses Exhibiting Complex Multiscale Plasticity.

Xingji Liu1, Yao Ni1, Zujun Wang2

  • 1School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510006, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed an artificial synapse (HRAS) that precisely controls channel charge, simulating dual-neurotransmitter actions. This breakthrough enables multi-level plasticity and spatiotemporal computing for secure information processing and enhanced neural networks.

Keywords:
bio‐inspired cryptographic applicationsdual‐neurotransmitterslateral modulationspatiotemporal propertiessynaptic transistor

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Artificial synapses are crucial for developing neuromorphic computing systems.
  • Existing artificial synapse models often lack the complexity to mimic intricate neural processes like lateral inhibition and multi-level plasticity.

Purpose of the Study:

  • To develop a novel artificial synapse capable of simulating multi-level coordinated actions of dual-neurotransmitters.
  • To achieve intricate interplay among lateral inhibition/enhancement and short-/long-term plasticity at a multi-level scale.
  • To explore the application of this artificial synapse in bio-inspired computing and secure information processing.

Main Methods:

  • Development of an asymmetric dual-gate heterointerface-regulated artificial synapse (HRAS).
  • Utilizing indium tin zinc oxide (ITZO) dual-interface channels regulated by a main gate and a lateral gate.
  • Employing dielectric coupling and ionic effects for precise control over channel charge.

Main Results:

  • The HRAS device successfully simulates multi-level coordinated actions of dual-neurotransmitters.
  • Achieved, for the first time, multi-level lateral inhibition/enhancement and short-/long-term plasticity.
  • Demonstrated HRAS capability in simulating frequency-dependent image filtering and dynamic visual persistence.
  • Proposed a dual-gate input neural network architecture based on HRAS with enhanced recognition capabilities.

Conclusions:

  • The developed HRAS offers a versatile device-level platform for secure information processing by harnessing spatiotemporal properties.
  • The novel neural network architecture based on HRAS shows promise for advanced bio-inspired computing applications.
  • This work represents a significant advancement in creating more sophisticated and functional artificial synapse devices.