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Related Concept Videos

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Related Experiment Video

Updated: Jul 7, 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

A pulsed neural network capable of universal approximation.

N E Cotter1, O N Mian

  • 1Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT.

IEEE Transactions on Neural Networks
|January 1, 1992
PubMed
Summary
This summary is machine-generated.

This study introduces a pulsed cerebellar model articulation controller (CMAC) network. Simulation results show its viability for training using a least mean square algorithm.

Related Experiment Videos

Last Updated: Jul 7, 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
  • * Artificial intelligence
  • * Neural network modeling

Background:

  • * The Cerebellar Model Articulation Controller (CMAC) is a widely recognized adaptive control system.
  • * Traditional CMAC models often rely on discrete or continuous value representations.
  • * There is a need for alternative CMAC architectures that can process temporal information efficiently.

Purpose of the Study:

  • * To introduce and describe a novel pulsed network version of the Cerebellar Model Articulation Controller (CMAC).
  • * To investigate the functional approximation capabilities of this pulsed CMAC network.
  • * To demonstrate the effectiveness of training the pulsed CMAC using a least mean square algorithm.

Main Methods:

  • * Development of a pulsed neural network architecture based on CMAC principles.
  • * Mathematical formulation of the network's output pulse timing as a function of input intervals.
  • * Application of a least mean square (LMS) algorithm for network training.

Main Results:

  • * The pulsed CMAC network produces output pulses whose timing is dependent on input pulse intervals.
  • * The network demonstrated the ability to approximate bounded measurable functions within causality constraints.
  • * Simulation results confirmed the successful training of the pulsed CMAC using the LMS algorithm.

Conclusions:

  • * The pulsed CMAC network offers a viable alternative for adaptive control and function approximation.
  • * The temporal processing capability of the pulsed network expands the applicability of CMAC models.
  • * The study validates the use of LMS for training this novel pulsed CMAC architecture.