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Related Experiment Video

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

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Published on: March 2, 2015

A programmable analog neural network processor.

W A Fisher¹, R J Fujimoto, R C Smithson

¹Lockheed Palo Alto Res. Lab., CA.

IEEE Transactions on Neural Networks

|January 1, 1991

Summary

This summary is machine-generated.

A novel analog neural network breadboard with 256 neurons was developed for real-time adaptive mirror control. This system successfully demonstrated online neural network training to modify control laws without interrupting operation.

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Area of Science:

Neuromorphic engineering
Analog computing
Adaptive control systems

Background:

Traditional control systems face challenges in real-time adaptation.
Analog neural networks offer potential for high-speed, low-power computation.
Adaptive optics require sophisticated control for dynamic environments.

Purpose of the Study:

To construct and evaluate a novel analog neural network breadboard.
To demonstrate the feasibility of real-time adaptive mirror control using this hardware.
To validate the ability to modify control laws online via neural network training.

Main Methods:

Construction of a 256-neuron analog neural network breadboard with 2048 programmable synaptic weights.
Integration of custom synapse chips on a reconfigurable neuron board.
Application of the breadboard to control 21 actuators of an adaptive mirror system.

Main Results:

The analog neural network breadboard achieved an analog bandwidth of 90 kHz.
The system demonstrated real-time control of an adaptive mirror with a 5 ms step-response setting time.
Neural network training successfully modified the control law of the high-speed analog loop without system interruption.

Conclusions:

Analog neural network hardware is suitable for high-speed adaptive control tasks.
Online learning capabilities enable dynamic modification of control strategies in real-time.
This approach offers a pathway for more responsive and adaptive optical systems.