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Related Experiment Video

Updated: Jul 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

How robust are neural network models of stimulus generalization?

Daniel W Franks1, Graeme D Ruxton

  • 1York Centre for Complex Systems Analysis, Department of Biology, University of York, YO10 5YW, UK. df525@york.ac.uk

Bio Systems
|March 28, 2008
PubMed
Summary

Artificial feed-forward neural networks model animal behavior, but parameter choices can create artifacts. This study reveals how training and parameter changes affect generalization curves, highlighting critical factors for reliable modeling.

Related Experiment Videos

Last Updated: Jul 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

  • Computational neuroscience
  • Animal behavior modeling

Background:

  • Artificial feed-forward neural networks are widely used for modeling stimulus selection and animal signaling.
  • Stimulus generalization, where similar stimuli elicit similar responses, is a key finding in this field and an inherent property of these networks.

Purpose of the Study:

  • To investigate the influence of network training and parameter perturbations on the behavior of artificial feed-forward neural networks.
  • To understand how parameter variation affects generalization curves in models of stimulus control.

Main Methods:

  • Studied a simple, general feed-forward neural network model.
  • Analyzed the impact of parameter variations and training conditions on network output.
  • Examined the resulting generalization curves.

Main Results:

  • Network training and parameter choices can significantly alter generalization behavior.
  • Certain conditions lead to undesirable artifacts in stimulus generalization.
  • Parameter perturbations can change the shape of generalization curves.

Conclusions:

  • Researchers must be cautious about arbitrary implementation choices in neural network models.
  • Understanding and avoiding artifacts caused by network and training conditions is crucial for accurate modeling of stimulus selection.
  • Confidence in model predictions relies on their biological basis, not implementation details.