[CS] Today! ASSISTANT PROFESSOR of STATISTICS *CANDIDATE JOB TALK* Wednesday, March 12 at 4:00 in the ASH Auditorium

[Paula Harmon]

Ethan Meyers, Postdoctoral Associate in the Center for Brains, Minds and 
Machines at MIT
"Using population decoding to understand how learning a new task changes 
neural processing"

_Abstract_: New machine-learning-based statistical methods are 
revolutionizing the way data is analyzed in a broad range of fields. In 
this talk I will discuss new methods that I developed which can 'decode' 
what types of information are contained in the activity of populations 
of neurons. To illustrate the power of this technique, I will describe a 
study where we examined how the information in the prefrontal cortex 
(PFC) changes after macaque monkeys learned to perform a new task. Given 
that primates are continually learning new tasks, understanding how new 
information is integrated into neural systems is fundamental for 
understanding how the brain enables complex behaviors.

Questions we addressed in this study include: 1) Does learning a new 
task change the amount of information about basic visual features of a 
stimulus or does it only change the amount of information about more 
complex task-related variables? 2) Does the new information arise due to 
the emergence of a few highly selective neurons, or is information 
evenly distributed across the population? 3) Do neurons become 
specialized to process only one type of information or can the same 
neuron carry multiple types of information? 4) Is the new information 
contained in a dynamic population code, or is there one stationary 
pattern of neural activity that contains the new information? and 5) Are 
there differences in the information content between dorsal and ventral 
PFC, and does learning affect these two brain regions equally? Future 
directions for how these methods can be expanded to give additional 
insight into neural processing, and how the methods can be applied to 
other areas outside of neuroscience, will also be discussed.

_Biography_: Ethan Meyers is a postdoctoral associate in the Center for 
Brains, Minds and Machines at MIT. His research focuses on creating 
machine-learning-based statistical data analysis methods that are useful 
for analyzing high dimensional neural signals. Through collaborations 
with experimental neuroscientists, his work has given new insight into 
how information is stored in working memory, how attention influences 
neural coding, and how new information is incorporated into neural 
activity. Ethan received his Ph.D. in computational neuroscience from 
MIT where he was a NDSEG Fellow, and his BA in computer science from 
Oberlin College where he graduated with high honors.

-- 
Paula Harmon, Administrative Assistant
School of Cognitive Science
Hampshire College
893 West Street Amherst, MA 01002
phone: 413.559.5502
fax: 413.559.5438
http://cs.hampshire.edu
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