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

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Neuromorphic Signal Filter for Robot Sensoring.

Luis M García-Sebastián¹, Victor H Ponce-Ponce¹, Humberto Sossa¹

¹Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, México.

Frontiers in Neurorobotics

|June 30, 2022

Summary

This study presents a novel neuromorphic filter using a leaky integrate and fire (LIF) neural model to significantly reduce noise in sensor signals. The filter achieves real-time, energy-efficient noise reduction for improved robot control and smart sensor applications.

Keywords:

CMOS filter low-frequency neuromorphic sensoring

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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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Area of Science:

Neuroscience
Robotics Engineering
Signal Processing

Background:

Noise in sensor input signals is a primary limitation for effective robot control.
Developing energy-efficient, real-time noise reduction techniques is crucial for advanced robotics and Industry 4.0 applications.

Purpose of the Study:

To introduce a novel neuromorphic filter model for significant noise reduction in sensor signals.
To demonstrate a new neural decoding approach for recovering primary signal information.
To enable energy-efficient, real-time signal processing for smart sensors.

Main Methods:

Implementation of a neuromorphic filter based on the leaky integrate and fire (LIF) neural model.
Development of a neural decoding method utilizing neuron-cell spiking frequency.
Simulation of the filter's performance in white noise environments.

Main Results:

The proposed neuromorphic filter effectively rejects white noise while preserving the original signal.
Significant noise reduction was achieved with minimal information loss.
The filter operates in near real-time with low energy consumption.

Conclusions:

The novel LIF-based neuromorphic filter offers a robust solution for noise management in sensor devices.
The filter's design is compatible with CMOS technology, facilitating the development of low-power smart sensors.
This technology has broad applications in robotics, the automotive industry, and other Industry 4.0 sectors.