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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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Related Experiment Video

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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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Gradient-based feature-attribution explainability methods for spiking neural networks.

Ammar Bitar1,2, Rafael Rosales1, Michael Paulitsch1

  • 1Intel Labs, Munich, Germany.

Frontiers in Neuroscience
|October 13, 2023
PubMed
Summary
This summary is machine-generated.

Gradient-based explainability methods are adapted for spiking neural networks (SNNs), enabling efficient identification of important input features in both spiking and real-valued data.

Keywords:
CIFARMNISTXAIbrain-inspired computationevent-based encodingexplainable artificial intelligenceneuromorphicspiking neural networks

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Last Updated: Jul 13, 2025

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) mimic biological neurons, offering low power consumption and ultra-low latency.
  • Existing explainability methods for SNNs have limitations in scalability and scope.

Purpose of the Study:

  • To adapt and evaluate gradient-based explainability methods for SNNs.
  • To generate input feature attribution maps for SNNs processing diverse data types.

Main Methods:

  • Adapted gradient-based methods for SNNs trained via backpropagation.
  • Evaluated methods on classification tasks using real-valued and spiking data.
  • Confirmed accuracy through pixel and spike-level perturbation experiments.

Main Results:

  • Gradient-based SNN attribution methods efficiently identify key pixels and spikes.
  • Achieved significantly less computation time compared to model-agnostic methods.
  • Coding techniques demonstrably influence the significance of input features.

Conclusions:

  • Adapted gradient-based methods offer a viable approach for SNN explainability.
  • These methods enhance understanding of SNN information processing.
  • Potential to contribute to the development of more efficient and accurate SNNs.