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Using deep neural networks to evaluate object vision tasks in rats.

Kasper Vinken^1,2, Hans Op de Beeck³

¹Department of Ophthalmology, Children's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.

Plos Computational Biology

|March 2, 2021

Summary

Rodent models in visual neuroscience primarily process information at lower levels. This study found rodent object vision relies on early convolutional layers, not complex representations seen in primates.

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A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

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Area of Science:

Neuroscience
Computational Neuroscience
Computer Vision

Background:

Rodents are increasingly used in visual neuroscience research.
Evidence suggests rodents may perform higher-level visual processing, like object recognition.

Purpose of the Study:

To quantitatively assess rodent object recognition capabilities.
To compare rodent visual processing with deep neural networks (DNNs).

Main Methods:

Compared rodent behavioral and neural data with DNNs.
Utilized convolutional neural networks (CNNs) with varying layer depths.
Analyzed performance on rodent object vision tasks.

Main Results:

Rodent visual task performance is explained by low to mid-level CNN layers.
Higher-level layers, crucial for primate object recognition, were not necessary.
Challenged the assumption that higher performance correlates with more abstract representations.

Conclusions:

Rodent object vision processing appears less complex than previously assumed.
DNNs offer a framework for understanding animal model visual processing.
Further research is needed to quantify representations in animal models.