Using Artificial-Intelligence-Driven Deep Neural Networks to Uncover Principles of Brain Representation and Organization

Yamins, Daniel

Title:

Using Artificial-Intelligence-Driven Deep Neural Networks to Uncover Principles of Brain Representation and Organization

dc.contributor.author	Yamins, Daniel
dc.contributor.corporatename	Georgia Institute of Technology. Neural Engineering Center	en_US
dc.contributor.corporatename	Stanford University. Dept. of Psychology	en_US
dc.date.accessioned	2017-10-19T16:05:40Z
dc.date.available	2017-10-19T16:05:40Z
dc.date.issued	2017-10-11
dc.description	Presented on October 11, 2017 at 3:30 p.m. in the Parker H. Petit Institute for Bioengineering and Biosciences, room 1128.	en_US
dc.description	Dan Yamins is a computational neuroscientist at Stanford University, where I'm an assistant professor of Psychology and Computer Science, and a faculty scholar at the Stanford Neurosciences Institute. I work on science and technology challenges at the intersection of neuroscience, artificial intelligence, psychology and large-scale data analysis.	en_US
dc.description	Runtime: 74:32 minutes	en_US
dc.description.abstract	Human behavior is founded on the ability to identify meaningful entities in complex noisy data streams that constantly bombard the senses. For example, in vision, retinal input is transformed into rich object-based scenes; in audition, sound waves are transformed into words and sentences. In this talk, I will describe my work using computational models to help uncover how sensory cortex accomplishes these enormous computational feats. The core observation underlying my work is that optimizing neural networks to solve challenging real-world artificial intelligence (AI) tasks can yield predictive models of the cortical neurons that support these tasks. I will first describe how we leveraged recent advances in AI to train a neural network that approaches human-level performance on a challenging visual object recognition task. Critically, even though this network was not explicitly fit to neural data, it is nonetheless predictive of neural response patterns of neurons in multiple areas of the visual pathway, including higher cortical areas that have long resisted modeling attempts. Intriguingly, an analogous approach turns out be helpful for studying audition, where we recently found that neural networks optimized for word recognition and speaker identification tasks naturally predict responses in human auditory cortex to a wide spectrum of natural sound stimuli, and help differentiate poorly understood non-primary auditory cortical regions. Together, these findings suggest the beginnings of a general approach to understanding sensory processing the brain. I'll give an overview of these results, explain how they fit into the historical trajectory of AI and computational neuroscience, and discuss future questions of great interest that may benefit from a similar approach.	en_US
dc.format.extent	74:32 minutes
dc.identifier.uri	http://hdl.handle.net/1853/58841
dc.language.iso	en_US	en_US
dc.relation.ispartofseries	KAVLI Brain Forum
dc.subject	Artificial intelligence	en_US
dc.subject	Auditory cortex	en_US
dc.subject	Computational neuroscience	en_US
dc.subject	Visual cortex	en_US
dc.title	Using Artificial-Intelligence-Driven Deep Neural Networks to Uncover Principles of Brain Representation and Organization	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Neural Engineering Center
local.relation.ispartofseries	Kavli Brain Forum
relation.isOrgUnitOfPublication	c2e26044-257b-4ef6-8634-100dd836a06c
relation.isSeriesOfPublication	edd29ba5-8370-407b-93c7-2d9c1bcae0cb