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相关概念视频

Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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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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Propagation of Action Potentials01:23

Propagation of Action Potentials

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The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Cognitive Learning01:21

Cognitive Learning

551
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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相关实验视频

Updated: Sep 16, 2025

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
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ACES-GNN:图形神经网络可以学习解释活动悬崖吗?

Xu Chen1, Dazhou Yu2, Liang Zhao2

  • 1Department of Chemistry, Emory University Atlanta Georgia 30322 USA fang.liu@emory.edu.

Digital discovery
|July 7, 2025
PubMed
概括

本研究介绍了ACES-GNN,这是图形神经网络 (GNN) 的新框架,通过专注于药物发现中的活动悬崖来提高分子性质预测的准确性和可解释性.

科学领域:

  • 计算化学计算化学
  • 人工智能在药物发现中的作用
  • 机器学习用于化学信息学

背景情况:

  • 图形神经网络 (GNN) 在分子属性预测方面表现出色,但缺乏透明度.
  • 不透明的GNN决策阻碍了药物发现中的采用.
  • 活动悬崖 (ACs) 由于结构相似性和强度差异存在挑战.

研究的目的:

  • 引入活动悬崖解释监督的GNN (ACES-GNN) 框架.
  • 在分子建模中提高GNN的预测准确性和可解释性.
  • 为GNN预测提供化学家友好的解释,特别是对于ACs.

主要方法:

  • 开发了ACES-GNN框架,整合了ACs的解释监督.
  • 训练有素的GNN专注于将模型属性与化学解释对齐.
  • 在30个药理学目标中验证了框架.

主要成果:

  • 与未经监督的GNN相比,ACES-GNN始终提高了预测准确性.
  • 提高了用于识别和解释AC的归因质量.
  • 证明了改进的预测和准确的解释之间的正相关性.

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结论:

  • ACES-GNN为理解和解释AC提供了一个强大的和可适应的框架.
  • 解释引导式学习在分子建模中推进了可解释的AI.
  • 该框架弥合了GNN预测和药物发现中的化学理解之间的差距.