Understanding Information Processing Mechanisms for Face Categorizations in Deep Neural Networks

In this chapter a brief review is given of computational systems that are motivated by information processing in the brain, an area that is often called neurocomputing or artificial neural networks. While this is now a well studied and documented area, specific emphasis is given to a subclass of such models, called continuous attractor neural networks, which are beginning to emerge in a wide context of biologically inspired computing. The frequent appearance of such models in biologically motivated studies of brain functions gives some indication that this model might capture important information processing mechanisms used in the brain, either directly or indirectly. Most of this chapter is dedicated to an introduction to this basic model and some extensions that might be important for their application, either as a model of brain processing, or in technical applications. Direct technical applications are only emerging slowly, but some examples of promising directions are highlighted in this chapter.

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Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

10.1101/2020.02.08.940189 ◽

2020 ◽

Cited By ~ 1

Author(s):

Kasper Vinken ◽

Hans Op de Beeck

Keyword(s):

Neural Networks ◽

Information Processing ◽

Object Recognition ◽

Deep Neural Networks ◽

Convincing Evidence ◽

Complex Object ◽

Visual Neuroscience ◽

Wide Range ◽

Invariant Object Recognition ◽

Fully Connected

AbstractIn the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but high-profile papers have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture the richness of information processing in primates through a succession of convolutional and fully connected layers. We find that rodent object vision can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most complex representations – which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.

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Faculty Opinions recommendation of Deep neural networks: A new framework for modeling biological vision and brain information processing.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726501805.793534417 ◽

2017 ◽

Author(s):

Odelia Schwartz

Keyword(s):

Neural Networks ◽

Information Processing ◽

Deep Neural Networks ◽

Biological Vision ◽

New Framework

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Using deep neural networks to evaluate object vision tasks in rats

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008714 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1008714

Author(s):

Kasper Vinken ◽

Hans Op de Beeck

Keyword(s):

Neural Networks ◽

Information Processing ◽

Object Recognition ◽

Deep Neural Networks ◽

Convincing Evidence ◽

Complex Object ◽

Visual Neuroscience ◽

Wide Range ◽

Invariant Object Recognition ◽

Properties Of Information

In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but a number of studies have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture hallmark properties of information processing in primates through a succession of convolutional and fully connected layers. We find that performance rodent object vision tasks can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most abstract representations–which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.

Download Full-text