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相关概念视频

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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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
441
Storage01:23

Storage

104
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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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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将结构性可塑性纳入自我组织的循环网络,用于序列学习.

Ye Yuan1, Yongtong Zhu1, Jiaqi Wang1

  • 1School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China.

Frontiers in neuroscience
|August 18, 2023
PubMed
概括

这项研究引入了一种新的尖端神经网络模型,该模型集成了多种形式的可塑性,包括奖励调节的STDP和结构性可塑性,以更好地模仿生物神经网络并改善时间信号处理.

关键词:
恒温的可塑性 恒温的可塑性奖励调节的尖峰时间依赖的可塑性.自己组织的自我组织.尖的神经网络的神经网络.结构性可塑性 结构性可塑性

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科学领域:

  • 计算神经科学是一种神经科学.
  • 人工智能的人工智能

背景情况:

  • 尖端神经网络 (SNN) 受到生物神经网络的启发,在时间编码方面表现出色.
  • 生物神经网络表现出复杂的可塑性 (STDP,结构,静态) 持续塑造网络结构和功能.
  • 这些可塑性在塑造神经网络和信号处理中的相互作用在很大程度上仍未被探索.

研究的目的:

  • 研究神经网络中多种可塑性的相互作用.
  • 开发一种新的SNN模型,其中包括奖励调节的STDP,恒温可塑性和结构可塑性.
  • 探索这些结合的可塑性如何影响神经信号处理和网络自我组织.

主要方法:

  • 提出了一个奖励调节的自我组织循环网络,具有结构可塑性 (RSRN-SP).
  • 实现了R-STDP的突触重量更新,以恒常性可塑性为指导.
  • 嵌入的结构可塑性用于模拟突触连接的生长和修剪.

主要成果:

  • 展示了RSRN-SP在顺序学习任务 (计数,运动预测,运动生成) 上的表示能力.
  • 展示了模型模仿生物神经网络特征的能力.
  • 验证了集成可塑性在塑造网络动态方面的有效性.

结论:

  • RSRN-SP模型有效地整合了多种可塑性,以增强SNN中的时间信号处理.
  • 该模型的自我组织特性和生物一致性为神经计算提供了新的见解.
  • 这种方法促进了更复杂,更具生物学可信性的人工神经网络的开发.