Pickard, William Augustus, authorBlanchard, Nathaniel, advisorAnderson, Chuck, committee memberThomas, Michael, committee member2024-01-012024-01-012023https://hdl.handle.net/10217/237371Artificial 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.born digitalmasters thesesengCopyright 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.fMRImachine visionrepresentational similarity analysismachine learningartificial intelligenceneural networksText