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Weight Perturbations for Simulating Virtual Lesions in a Convolutional Neural Network

Joe MacInnes Orcid Logo, Natalia Zhozhikashvili, Matteo Feurra

Lecture Notes in Computer Science, Pages: 221 - 234

Swansea University Author: Joe MacInnes Orcid Logo

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DOI (Published version): 10.1007/978-3-031-67285-9_16

Abstract

Convolutional Neural Networks (CNNs) match human performance in many visual tasks like the classification of images, however they may not simulate the underlying biological processes. We implemented a CNN to try replicate results from an object inversion experiment with Transcranial Magnetic Stimula...

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Published in: Lecture Notes in Computer Science
ISBN: 9783031672842 9783031672859
ISSN: 0302-9743 1611-3349
Published: Cham Springer Nature Switzerland 2024
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URI: http://cronfa.swan.ac.uk/Record/cronfa69412
Abstract: Convolutional Neural Networks (CNNs) match human performance in many visual tasks like the classification of images, however they may not simulate the underlying biological processes. We implemented a CNN to try replicate results from an object inversion experiment with Transcranial Magnetic Stimulation (TMS). After training on upright faces, the CNN model went through three stages of testing: checking (1) for overall accuracy, (2) for the presence of the Face Inversion Effect (FIE) and (3) for an FIE reduction after weight perturbations. Results of the model were compared with human performance in an analogous experiment, where disruption of the extrastriate cortex (the Occipital Face Area (OFA) and the control Occipital Place Area (OPA)) was performed using TMS. The resulting model (1) showed a level of accuracy similar to humans, but (2) did not show the FIE, but rather showed a general object inverted effect. Disruption with TMS (3) led to a reduction in the FIE, however disruption of model layers only led to reduction of the general object inverted effect. Thus, CNNs were observed to successfully simulate some results of objects recognition in general, but are unable to simulate the specific mechanisms of modularity and face processing. CNNs are certainly a useful metaphor for human visual processing, but it’s important to understand the limits of that metaphor if they are to be used as models in medicine and neuroscience.
College: Faculty of Science and Engineering
Start Page: 221
End Page: 234

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