Repository logo
 

Neuralator 5000: exploring and enhancing the BOLD5000 fMRI dataset to improve the robustness of artificial neural networks

Simple item page
dc.contributor.authorPickard, William Augustus, author
dc.contributor.authorBlanchard, Nathaniel, advisor
dc.contributor.authorAnderson, Chuck, committee member
dc.contributor.authorThomas, Michael, committee member
dc.date.accessioned2024-01-01T11:24:19Z
dc.date.available2024-01-01T11:24:19Z
dc.date.issued2023
dc.description.abstractArtificial neural networks (ANNs) originally drew their inspiration from biological constructs. Despite the rapid development of ANNs and their seeming divergence from their biological roots, research using representational similarity analysis (RSA) shows a connection between the internal representations of artificial and biological neural networks. To further investigate this connection, human subject functional magnetic resonance imaging (fMRI) studies using stimuli drawn from common ANN training datasets are being compiled. One such dataset is the BOLD5000, which is composed of fMRI data from four subjects who were presented with stimuli selected from the ImageNet, Common Objects in Context (COCO), and Scene UNderstanding (SUN) datasets. An important area where this data can be fruitful is in improving ANN model robustness. This work seeks to enhance the BOLD5000 dataset and make it more accessible for future ANN research by re-segmenting the data from the second release of the BOLD5000 into new ROIs using the vcAtlas and visfAtlas visual cortex atlases, generating representational dissimilarity matrices (RDMs) for all ROIs, and providing a new, biologically-inspired set of supercategory labels specific to the ImageNet dataset. To demonstrate the utility of these new BOLD5000 derivatives, I compare human fMRI data to RDMs derived from the activations of four prominent vision ANNs: AlexNet, ResNet-50, MobileNetV2, and EfficientNet B0. The results of this analysis show that the old, less-advanced AlexNet has a higher neuro-similarity than the much more recent, and technically better-performing models. These results are further confirmed through the use of Fiedler vector analysis on the RDMs, which shows a reduction in the separability of the internal representations of the biologically inspired supercategories.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierPickard_colostate_0053N_18127.pdf
dc.identifier.urihttps://hdl.handle.net/10217/237371
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectfMRI
dc.subjectmachine vision
dc.subjectrepresentational similarity analysis
dc.subjectmachine learning
dc.subjectartificial intelligence
dc.subjectneural networks
dc.titleNeuralator 5000: exploring and enhancing the BOLD5000 fMRI dataset to improve the robustness of artificial neural networks
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Pickard_colostate_0053N_18127.pdf
Size:
1.87 MB
Format:
Adobe Portable Document Format
Download

Collections

2020-
Theses and Dissertations