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What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

Himanshu Akolkar¹, Cedric Meyer, Zavier Clady

¹iCub Facility, Istituto Italiano di Tecnologia, Genoa 16163, Italy Himanshu.Akolkar@iit.it.

Neural Computation

|January 21, 2015

Summary

This summary is machine-generated.

High temporal precision in spike-based pattern recognition, particularly from event-based neuromorphic sensors, significantly enhances information processing and object recognition accuracy compared to conventional methods.

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Area of Science:

Computational Neuroscience
Artificial Intelligence
Computer Vision

Background:

Traditional spike-based algorithms often use image-derived spike timings, which can be artificial and contradict biological visual processing.
Neuromorphic event-based visual sensors offer asynchronous, high temporal resolution data acquisition.

Purpose of the Study:

To quantify the impact of spike timing precision on pattern recognition algorithms.
To compare information acquisition from neuromorphic sensors versus conventional frame-based methods.

Main Methods:

Utilized spiking output from neuromorphic event-based visual sensors.
Employed information theory to analyze class separability at different temporal resolutions.
Conducted experiments on real-world data to assess information loss correlated with temporal precision.

Main Results:

Reduced temporal precision in spike timing significantly degrades recognition task performance, especially at conventional machine vision frequencies (30-60 Hz).
High temporal acquisition from neuromorphic sensors provides up to 70% more information than conventional frame-based acquisition, increasing object class separability.
Information loss is directly correlated with temporal precision.

Conclusions:

Neuromorphic asynchronous visual sensors offer significant advantages for practical applications and theoretical studies in visual computation.
Precise spike timing sequences, mimicking retinal processing, are crucial for efficient neuro-inspired visual computations.