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相关概念视频

Neuroplasticity01:01

Neuroplasticity

267
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 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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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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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...
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Synaptic Signaling01:09

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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.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
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Role of Neurotransmitters in Memory01:23

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Neurotransmitters are integral to the brain's communication system, enabling neurons to transmit signals across synapses. This chemical exchange underpins various cognitive functions, including memory processes. The role of neurotransmitters in memory is multifaceted, influencing the encoding, consolidation, and retrieval of memories through their action on different neural circuits.
 Glutamate and Synaptic Plasticity
Glutamate, the brain's main excitatory neurotransmitter, is...
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相关实验视频

Updated: May 26, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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神经形态计算中的突触可塑性的闪存:一篇评论

Jisung Im1, Sangyeon Pak2, Sung-Yun Woo1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Biomimetics (Basel, Switzerland)
|February 25, 2025
PubMed
概括
此摘要是机器生成的。

使用闪存的神经形态电子产品提供了节能的内存计算. 本综述探讨了针对优化神经网络应用的NOR,AND和NAND闪存进步.

关键词:
和闪存存储器和闪存存储器.这是一个NAND闪存.在 NOR 闪存内存.闪存记忆 闪存记忆是一种闪存记忆.在内存计算中的内存计算.这是一个神经形态神经形态的神经形态.突触装置是一种突触装置.

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科学领域:

  • 神经形态工程的神经形态工程
  • 计算机科学 计算机科学
  • 材料科学 材料科学 材料科学

背景情况:

  • 数据的指数增长需要节能解决方案来存储和处理.
  • 神经形态电子,模仿生物神经系统,提供了一条超越·诺伊曼瓶的道路.
  • 带有突触器件的内存计算承诺减少计算任务的能源消耗.

研究的目的:

  • 审查使用NOR,AND和NAND闪存的神经形态计算的最新进展.
  • 提供基于闪存的神经形态计算的全面概述.
  • 突出闪存在优化神经网络的能源效率方面的潜力.

主要方法:

  • 在突触器件中分析多位非挥发性和生物灵感特征.
  • 在乘法和积累运算中探索欧姆定律的能量的减少.
  • 对数组架构,操作方法和NOR,AND和NAND闪存的电气性能进行审查.

主要成果:

  • 闪存,特别是NOR,AND和NAND类型,在神经形态系统中显示出大规模数据存储的巨大潜力.
  • 基于闪存的突触设备可以有效地减少基本计算操作中的能源消耗.
  • 集成闪存可以有效地实现各种神经网络设计.

结论:

  • 闪存是节能神经形态计算和内存处理的竞争性技术.
  • 了解闪存特征对于优化其在各种神经网络架构中的应用至关重要.
  • 本综述为在各种应用中推进基于闪存的神经形态计算提供了见解.