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Related Experiment Video

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Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
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Dynamic Neural Fields with Intrinsic Plasticity.

Claudius Strub1,2, Gregor Schöner1, Florentin Wörgötter2

  • 1Autonomous Robotics Lab, Institut für Neuroinformatik, Ruhr-UniversitätBochum, Germany.

Frontiers in Computational Neuroscience
|September 16, 2017
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Summary
This summary is machine-generated.

This study introduces intrinsic plasticity (IP) for autonomous adaptation of dynamic neural fields (DNFs). This method tunes DNF parameters without prior input knowledge, simplifying cognitive process modeling.

Keywords:
adaptationdynamic neural fieldsdynamicsintrinsic plasticity

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Area of Science:

  • Computational neuroscience
  • Cognitive modeling
  • Dynamical systems theory

Background:

  • Dynamic neural fields (DNFs) model large neural networks using mean field theory.
  • DNFs are crucial in dynamic field theory for sensorimotor embedding in cognitive architectures.
  • Manual parameter tuning for DNFs is time-consuming and requires expertise, especially with unknown input distributions.

Purpose of the Study:

  • To propose an autonomous adaptation mechanism for DNF parameters using intrinsic plasticity (IP).
  • To enable pre-defined DNF output statistics irrespective of input distribution knowledge.
  • To compensate for dynamic changes in input distributions.

Main Methods:

  • Introduced input and output measures for DNFs.
  • Implemented a hyperparameter to define desired output distribution.
  • Utilized intrinsic plasticity (IP) for online adaptation of DNF resting level and gain.

Main Results:

  • Demonstrated autonomous adaptation of DNF parameters through IP.
  • Showcased the ability to pre-define output statistics without prior input distribution data.
  • Validated the approach's capability to handle changing input distributions.

Conclusions:

  • Intrinsic plasticity offers an effective method for autonomous DNF parameter tuning.
  • This approach simplifies the application of DNFs in cognitive architectures, particularly for real-world sensory processing.
  • The method enhances the robustness and adaptability of neural field models.