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Related Experiment Videos

Dynamic updating of distributed neural representations using forward models.

Eric L Sauser1, Aude G Billard

  • 1Learning Algorithms and Systems Laboratory (LASA), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. eric.sauser@a3.epfl.ch

Biological Cybernetics
|December 5, 2006
PubMed
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Progress in brain research·2007

We introduce a continuous attractor network model to explain neural processes like sensory discrimination and motor control. This model integrates motor and sensory variables for accurate predictions and discrimination.

Area of Science:

  • Computational Neuroscience
  • Cognitive Neuroscience
  • Neural Network Modeling

Background:

  • Several neural processes involve dynamic integration of motor and sensory variables.
  • Previous high-level modeling studies have explored these principles.
  • Understanding the underlying neural mechanisms remains a key challenge.

Purpose of the Study:

  • To present a continuous attractor network model.
  • To hypothesize mechanisms for neural processes like velocity tuning, sensory discrimination, and motor control.
  • To explore biologically plausible neural dynamics at a population level.

Main Methods:

  • Developing a continuous attractor network model.
  • Extending classical neural field models.

Related Experiment Videos

  • Analyzing model interactions with external inputs.

    • Main Results:

    • The model demonstrates dynamic properties for updating internal representations using external commands.
    • Interactions between the model and external inputs reveal interesting properties.
    • The model offers insights into neural processes underlying sensory-motor integration.

    Conclusions:

    • The proposed continuous attractor network model provides a biologically plausible framework.
    • The model advances understanding of neural dynamics in sensory-motor transformations.
    • This work contributes to a better comprehension of brain mechanisms for prediction and discrimination.