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相关概念视频

Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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The Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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相关实验视频

Updated: Jul 13, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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基于梯度的特征归因可解释性方法用于尖端神经网络.

Ammar Bitar1,2, Rafael Rosales1, Michael Paulitsch1

  • 1Intel Labs, Munich, Germany.

Frontiers in neuroscience
|October 13, 2023
PubMed
概括
此摘要是机器生成的。

基于梯度的可解释性方法适用于尖端神经网络 (SNN),可以有效地识别尖端和实值数据中的重要输入特征.

关键词:
在CIFAR中,有很多种类.蒙尼斯特主义者 (Mnist)在XAI,XAI就是XAI.大脑启发的计算基于事件的编码.可解释的人工智能这是一个神经形态神经形态的神经形态.尖的神经网络的神经网络.

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相关实验视频

Last Updated: Jul 13, 2025

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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
  • 机器学习 机器学习

背景情况:

  • 尖端神经网络 (SNN) 模仿生物神经元,提供低功耗和超低延迟.
  • 现有的SNN可解释性方法在可扩展性和范围方面存在局限性.

研究的目的:

  • 适应和评估基于梯度的SNN可解释性方法.
  • 为处理各种数据类型的SNN生成输入特征归属地图.

主要方法:

  • 通过反向传播训练的SNN适应的梯度方法.
  • 在使用实值和增值数据进行分类任务的评估方法.
  • 通过像素和尖峰级扰动实验证实了准确性.

主要成果:

  • 基于梯度的SNN归属方法有效地识别关键像素和尖峰.
  • 与模型不可知论方法相比,实现了显著更短的计算时间.
  • 编码技术显然影响了输入特征的意义.

结论:

  • 基于梯度的调整方法为SNN可解释性提供了一种可行的方法.
  • 这些方法提高了对SNN信息处理的理解.
  • 可能有助于开发更有效,更准确的SNNs.