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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

55.1K
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.
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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...
118
Reinforcement01:23

Reinforcement

202
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
202
Classical Conditioning01:18

Classical Conditioning

479
Associative learning, a core principle in behavioral psychology, involves forming connections between events and facilitating learned responses. This concept is vividly illustrated by classical conditioning, a process extensively studied by the Russian physiologist Ivan Pavlov. Pavlov's pioneering research on dogs' digestive systems led to the discovery that behaviors can be learned through association, laying the groundwork for classical conditioning.
Ivan Pavlov observed that dogs...
479
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...
344
Behaviorism01:28

Behaviorism

2.3K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
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相关实验视频

Updated: Jun 26, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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只有在积极的例子中学习性质.

Mehrad Ansari1, Andrew D White1

  • 1Department of Chemical Engineering, University of Rochester Rochester NY 14627 USA andrew.white@rochester.edu.

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|May 17, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种新的半监督深度学习方法,使用积极未标记的学习来发现. 这种方法有效地预测抗微生物的特性,只使用积极的例子,克服数据的限制.

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Peptide-based Identification of Functional Motifs and their Binding Partners
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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 深度学习模型需要广泛的标记数据,包括负面示例,这对于性质来说很少.
  • 高通量选方法难以有效地为基数据库生成负面示例.

研究的目的:

  • 开发一个半监督的深度学习框架,以使用有限的积极例子来预测性质.
  • 为了应对序分析中缺少负数据的挑战.

主要方法:

  • 实施的积极未标记 (PU) 学习策略:调整基础分类器和可靠的负面识别.
  • 构建了深度学习模型,从序列中推断出溶性,血解,SHP-2结合和非污染活性.
  • 对传统的正负分类 (PN) 进行评估的预测性能.

主要成果:

  • 通过PU学习方法,只使用阳性数据,实现了竞争性预测性表现.
  • 在有限的负面例子的情况下,在场景中证明了PU学习的有效性.
  • 成功预测了多种抗微生物的特性.

结论:

  • 半监督PU学习是一个可行的替代品PN学习当负面数据稀缺时.
  • 这种方法可以有效地预测性质,并指导分子设计.
  • 在抗微生物研究中推进深度学习的应用.