Towards more biologically plausible deep learning and visual processing

Over the last decade, we have witnessed tremendous successes of Artificial Neural Networks (ANNs) on solving a wide range of Al tasks. However, there is considerably less development in understanding the biological neural networks in primate cortex. In this thesis, I try to bridge the gap between artificial and biological neural networks. I argue that it would be beneficial to build ANNs that are both biologically-plausible and well-performing, since they may serve as models for the brain and guide neuroscience research. On the other hand, developing a biology-compatible framework for ANNs makes it possible to borrow ideas from neuroscience to improve the performance of AI systems. I discuss several aspects of modern ANNs that can be made more consistent with biology: (1) the backpropagation learning algorithm (2) ultra-deep neural networks (e.g., ResNet, He et al., 2016) for visual processing (3) Batch Normalization (Ioffe and Szegedy, 2015). For each of the three aspects, I propose biologically-plausible modifications of the ANN models to make them more implementable by the brain while maintaining (or even improving) their performance.

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

2017

Author dc:creator

• Liao, Qianli

Advisor dc:contributor.advisor

• Tomaso A. Poggio.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111920

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111920