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Neuroplasticity01:01

Neuroplasticity

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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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Plasticity00:58

Plasticity

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Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
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Chemical Synapses01:26

Chemical Synapses

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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.
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...
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Electrical Synapses01:28

Electrical Synapses

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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.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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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
LTP can occur when...
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Ligand-Gated Ion Channel Receptor: Gating Mechanism01:30

Ligand-Gated Ion Channel Receptor: Gating Mechanism

2.6K
Ligand-gated ion channels are transmembrane proteins that play a vital role in intercellular communication and functions of the nervous system. They allow the influx of ions across the membrane once the neurotransmitter binds, allowing the subsequent transmission of electrical excitation across the neurons. Other ligand-gated ion channels, like the γ-aminobutyric acid (GABA) receptor, permit anions like chloride into the cells on the binding of the GABA molecule. Their entry into the cell...
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Updated: Sep 10, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Sinapsis de ionoelastómero con plasticidad sináptica configurable

Sijie Zheng1, Zhong-Da Zhang2, Xiaowei Wang1

  • 1Jiangsu Engineering Laboratory of Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, College of Chemistry, Suzhou Key Laboratory of Soft Material and New Energy, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, 215123, China.

Advanced materials (Deerfield Beach, Fla.)
|August 21, 2025
PubMed
Resumen

Los investigadores desarrollaron nuevas sinapsis artificiales utilizando ionoelastómeros y polímeros semiconductores. La selección de aniones sintoniza la plasticidad sináptica, permitiendo tareas de red neuronal de alta precisión como el reconocimiento de imágenes con menos estados.

Palabras clave:
Sinapsis artificialeselectrónica flexibleElastómero iónicoel memristor

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Área de la Ciencia:

  • Ciencias de los materiales
  • La neurociencia
  • Productos electrónicos orgánicos

Sus antecedentes:

  • Las sinapsis artificiales son cruciales para el desarrollo de sistemas informáticos neuromórficos.
  • Las sinapsis artificiales existentes a menudo enfrentan desafíos en la modulación de la plasticidad y la eficiencia energética.
  • Las heteroestructuras orgánicas ofrecen potencial para nuevas funcionalidades sinápticas.

Objetivo del estudio:

  • Introducir una nueva clase de sinapsis artificiales basadas en ionoelastómeros y polímeros semiconductores.
  • Investigar el papel de las especies aniónicas en la modulación de la plasticidad sináptica.
  • Demostrar la aplicación de estas sinapsis artificiales para emular redes neuronales blandas para tareas de reconocimiento.

Principales métodos:

  • Fabricación de una heteroestructura orgánica compuesta por un ionoelastómero y un polímero semiconductor.
  • Modulación de las pesas sinápticas a través de la redistribución espacial de aniones en respuesta a estímulos eléctricos.
  • Evaluación del rendimiento del dispositivo en tareas de reconocimiento de imágenes manuscritas y de moda.

Principales resultados:

  • Las sinapsis de ionoelastómero exhibieron plasticidad sináptica ajustable controlada por la selección de aniones.
  • Los dispositivos demostraron estados continuamente programables y no volátiles.
  • Se lograron altas precisiones de reconocimiento al emular una red neuronal blanda, comparable a los modelos ideales, pero con solo 16 estados discretos.

Conclusiones:

  • Las heteroestructuras orgánicas basadas en ionoelastómeros representan una nueva plataforma prometedora para las sinapsis artificiales.
  • La selección aniónica proporciona un método simple pero eficaz para modular la plasticidad sináptica.
  • Estas sinapsis artificiales muestran potencial para aplicaciones informáticas neuromórficas eficientes y precisas.