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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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Bayesian neuromorphic imaging for single-photon LiDAR.

Dan Yao, Germán Mora-Martín, Istvan Gyongy

|November 14, 2024

Summary

This summary is machine-generated.

This study introduces a Bayesian method for single photon avalanche diode (SPAD) arrays to function as event cameras, detecting scene changes by analyzing direct time-of-flight data for intensity and depth variations.

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Area of Science:

Neuromorphic Engineering
Computer Vision
LiDAR Technology

Background:

Event cameras offer sparse, asynchronous data streams capturing light flux changes.
Single Photon Avalanche Diode (SPAD) arrays are sensitive photon detectors.
Direct Time-of-Flight (dToF) provides depth information.

Purpose of the Study:

To adapt SPAD arrays for pseudo event-based sensing using a Bayesian approach.
To generate sparse event streams indicating scene changes (intensity and depth).
To enable active neuromorphic 3D imaging with reduced data redundancy.

Main Methods:

A Bayesian changepoint detection strategy applied to dToF sequences from SPAD arrays.
Online Bayesian inference for simultaneous changepoint detection and parameter estimation per pixel.
Generation of asynchronous intensity and depth change event streams.

Main Results:

Demonstrated feasibility of using SPAD arrays as pseudo event cameras.
Successfully generated intensity and depth change event streams from dToF data.
Algorithm produces intensity and depth estimates as a byproduct.

Conclusions:

The proposed Bayesian approach enables SPAD arrays to perform event-based sensing for neuromorphic 3D imaging.
This method effectively detects changes in scene intensity and/or depth.
Integration with Single-Photon LiDAR (SPL) systems offers advantages in data reduction and depth change reporting.