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相关概念视频

Direct Motor Pathways01:11

Direct Motor Pathways

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The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and...
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Indirect Motor Pathways01:22

Indirect Motor Pathways

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The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...
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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...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

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分子自主探路器使用深度强化学习学习

Ken-Ichi Nomura1, Ankit Mishra1, Tian Sang1

  • 1Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States.

The journal of physical chemistry letters
|May 9, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了一个人工智能框架,使用深度强化学习来发现无形材料中的节能扩散途径. 这种方法揭示了原子扩散机制,克服了玻璃状固体中的挑战.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 人工智能的人工智能

背景情况:

  • 固体中的扩散对于化学反应至关重要,但由于缺乏定义的路径,在无形材料中了解得很少.
  • 材料故障通常与玻璃状固体中的缓慢扩散过程有关.
  • 预测无形材料中的扩散机制带来了重大的计算挑战.

研究的目的:

  • 开发一种人工智能引导的模拟方法,以发现无形固体中的节能扩散途径.
  • 为了实现扩散过程的长期原子模拟.
  • 为了弥合缓慢扩散和玻璃材料中的材料故障之间的差距.

主要方法:

  • 提出了分子自主探路器 (MAP) 框架,利用深度强化学习 (DRL).
  • 采用了Deep Q-Network架构,配备了分布式优先重复缓冲区,以实现高效的代理培训.
  • 使用异步剂进行加速体验采样,并采用一条一条的弹性带方法来精制能量配置.

主要成果:

  • 在MAP框架中,成功地确定了无形中的原子扩散路径及其能量配置.
  • 实现了与实验观测可比的模拟时间尺度.
  • 演示了人工智能在无序材料中发现复杂的扩散机制的能力.

结论:

  • 由人工智能引导的MAP框架为研究无形材料中的扩散提供了一种有效的方法.
  • 这种方法可以加速对扩散控制过程和材料降解的理解.
  • 能够在具有挑战性的玻璃系统中准确预测扩散机制和时间尺度.