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Related Experiment Video

Updated: Nov 21, 2025

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

Published on: October 20, 2019

Image reconstruction through a multimode fiber with a simple neural network architecture.

Changyan Zhu¹, Eng Aik Chan², You Wang¹

¹Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 637371, Singapore.

Scientific Reports

|January 14, 2021

Summary

A simpler dense neural network reconstructs images from multimode fibers (MMFs) as well as complex convolutional neural networks (CNNs). This dense network requires less training time and computing power for high-fidelity MMF image decoding.

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Area of Science:

Optical Engineering
Machine Learning
Computational Imaging

Background:

Multimode fibers (MMFs) offer potential for advanced imaging applications like endoscopy.
Decoding complex speckle patterns in MMFs due to mode-mixing and modal dispersion remains a significant challenge.

Purpose of the Study:

To evaluate the efficacy of a simpler neural network architecture for MMF image reconstruction.
To compare the performance of dense neural networks against convolutional neural networks (CNNs) in MMF imaging.

Main Methods:

Training a single hidden layer dense neural network for MMF image reconstruction.
Comparing the image reconstruction fidelity, training time, and resource requirements against established CNN models.
Assessing the long-term stability and accuracy of trained models over a week.

Main Results:

The dense neural network achieved image reconstruction fidelity comparable to CNNs.
The dense network demonstrated superior performance in terms of reduced training time and computational resource demands.
Both dense and CNN models accurately reconstructed MMF images for up to a week post-training.

Conclusions:

A single hidden layer dense neural network is a viable and efficient alternative to CNNs for MMF image reconstruction.
This simpler architecture offers significant advantages in training efficiency and resource utilization for MMF imaging applications.