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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

Implementations of artificial neural networks using current-mode pulse width modulation technique.

E I El-Masry1, H K Yang, M A Yakout

  • 1King Fahd Univ. of Pet. and Miner., Dhahran.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

A novel current-mode pulse width modulation (CM-PWM) technique efficiently implements analog artificial neural networks (ANNs). This method minimizes silicon area, enabling large-scale neural systems with programmable and modular designs.

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

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Published on: March 2, 2015

Area of Science:

  • Electronics Engineering
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Artificial neural networks (ANNs) require efficient weighted summation operations (WSO).
  • Implementing ANNs often involves complex circuitry and large silicon footprints.
  • Existing methods may lack flexibility in gain adjustment and modularity.

Purpose of the Study:

  • To present a current-mode pulse width modulation (CM-PWM) technique for analog ANN implementation.
  • To demonstrate the efficiency and advantages of CM-PWM for WSO and sigmoidal transformations.
  • To introduce modular building blocks for scalable ANN design.

Main Methods:

  • Utilized nonlinear transconductance amplifiers within current integrators for inherent sigmoidal transformation.
  • Developed current-mode CMOS circuits for CM-PWM ANN realization.
  • Designed and simulated four modular components for constructing ANNs.

Main Results:

  • Achieved minimum silicon area, making the technique suitable for very large-scale neural systems.
  • Demonstrated easy programmability and electronically adjustable neuron gains.
  • Successfully implemented discrete-time cellular neural networks (DT-CNNs) using the CM-PWM technique.

Conclusions:

  • The CM-PWM technique offers an efficient, compact, and flexible approach to implementing analog ANNs.
  • The presented modular design facilitates the creation of scalable and adaptable neural systems.
  • CM-PWM is a viable method for realizing complex neural network architectures, including DT-CNNs.