Towards Biologically Plausible Deep Neural Networks

Human intelligence has long been a source of inspirations for developing artificial intelligence. Computational principles and components discovered in the human brain have been successfully applied to artificial systems. These artificial systems are not only useful for engineering tasks but also serve as computational models of the brain, connecting theories to empirical data. Conversely, artificial intelligence, deep neural networks in particular, has contributed to advancing the understanding of the brain. Deep neural networks when trained adequately can reproduce behavioral and neural data better than previously developed models. Here we present studies that contribute to this interplay between natural and artificial intelligence. We first investigate invariance, a key computational principle that enables robust visual recognition and efficient generalization to new visual concepts, in human vision. Based on the experimental results, we propose deep neural network architectures that support the observed human behavioral properties in invariant recognition tasks. Next, we introduce a comparison framework for deep neural networks, where ground-truth targets are known, such that interpretations from the comparison can be validated. We explore whether deep neural networks with high functional similarity measures can provide reliable insights into the architectural building blocks of the brain.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Han, Yena

Advisor dc:contributor.advisor

• Poggio, Tomaso A.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/155069

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/155069