From Bayesian Behavior Lab
This page contains course materials for the CoSMo 2012 summer school.
To upload files, you must log in with an account that has upload access. Here are some upload instructions or you can go straight to the upload file page.
Contact Ben Walker if you need assistance.
Lecturers: Gunnar Blohm, Philip Sabes
Blohm lecture 1
Blohm lecture 2
Sabes Lecture 1
Sabes Lecture 2
Afternoon Tutorial Session 1
GradientDescent: View as .txt file or download the .m file
NNet_toolboxR2010: View as .txt file or download the .m file
RFanalysis: View as .txt file or download the .m file
Afternoon Tutorial Session 2
LQR and Kalman Filtering
Download the solution .m file: LQG_Solution.m
Introduction to the data and model sharing initiative
Aug 7 (evening)
Lecturer: Konrad Körding
The DREAM project should be on the USB drives of a few different students. If you haven't yet, find someone who has the data and get it from them. DREAM can also be downloaded piece-wise (data sets, models, tools, and documentation) from CRCNS: http://crcns.org/data-sets/movements/dream/downloading-dream. You will need to create an account on CRCNS to be able to download the project files.
- If you're familiar with svn and would like info/credentials for code in the repository, contact Ben Walker
Update: Here's the fixed version of LoadDreamPaths.m. (The originally distributed version had Windows directory formatting using '/' and would not work for *nix. This script should work for all OSes.)
Here is a description of data sets currently in Dream. Dream is growing, but this list is accurate as of the time of the summer school.
Lecturers: Mike Landy, Paul Schrater
Reading: introductory chapter on cue integration from Sensory Cue Integration
Entire syllabus and exercises
Landy Lecture 1a
Landy Lecture 1b
Landy Lecture 2
Schrater Lecture 1
Schrater Lecture 2
Afternoon Tutorial Session 1
Basic Bayes with Koerding and Wolpert
Tutorial Matlabfile 1
based on Tutorial paper reference
and needs the following data DataSet
Matlab code and other interesting stuff,
Stimuli, part 1
Stimuli, part 2. Note that the stimuli need to be in subdirectory "stimuli" (relative to the directory with the experimental code), and you will need the MGL toolbox for running psychophysical experiments: http://gru.brain.riken.jp/doku.php/mgl/download
You'll need to set the screen variable "scr" to 0 (to run in a window) or 1 (to run full-screen on your primary screen).
Motion Integration Tutorial
Matlab files to implement Weiss et al, 2002
Afternoon Problem Session 2
Try to do 2 out of 3 problems.
Download this file and simulate Size/Distance Explaining away from Battaglia et al, 2010
Simulate the Kalman filter model in Haith et. al, 2008 The kalman filter has been implemented for you the matlab filter code , and the simulation model is described in this paper the paper . You will need this as well more matlab functions
Problem 3: This problem explores the discovery of possible cues to facial attractiveness.
Download the following files: the data needed , the instructions , and a gui to view the faces and dimensions
Learning, adaptation and generalization
Lecturers: John Krakauer, David Ostry, Konrad Körding
Ostry Lecture 1
Ostry Lecture 2
Krakauer Part 1
Krakauer Part 2
Tutorial: Simple introduction to Bayesian approaches
Advanced neural data analysis:
A guide to Paper_Writing_101
Neuromechanics & spinal cord
Lecturers: Sandro Mussa-Ivaldi, Jason Kutch
Tutorial: Motor Primitives
by: Alejandro Melendez-Calderon, Ali Farshchian
Tutorial: Day 2
Databaser and redundancy analysis code
Databaser and redundancy analysis code part 2
Robotics & brain-machine interfaces
Lecturers: Brenna Argall, Byron Yu, Lee Miller
Yu Lecture 1
Yu Lecture 2
Brain-machine interface exercises
and here is the time chapter from a Book Konrad is co-writing with Weiji Ma and Dan Goldreich.
password is lowercase and describes the statistics that Konrad likes to do.
Lecturers: Rhodri Cusack, Alex Wade, Scott Grafton
Cusack Lecture One
Cusack Lecture Two: part 1, part 2
Grafton MRI networks
Grafton MRI GLM
Wade lecture - day 1
Wade lecture - day 2
The secret of becoming a successful scientist
Aug 17 (evening)
Lecturer: Luís Amaral
Aug 16 - 17
Students: Upload .ppt files and any other (.m, .mat, .zip, etc.) files you'd like to share. (Upload instructions at the top of the page.) Let others know about your work! Please include group member names on this wiki with the link.
Thursday night groups:
Variability in Motor Learning (Abstract) by Farnaz Abdollahi, Katie Bankieris, Keturah Bixby, Moria Fisher, Ryan Morehead *winner student presentation competition*
Temporal influence on subject reaching strategies in Kording & Wolpert (2004) (Abstract) by Matt Balcarras, Irene Tamagnone, Leonie Oostwoud Wijdenes, Andrew Brennan, Deborah Barany, Yashar Zeighami.
Predicting Cognitive Function in Older Adults by Niousha Bolandzadeh & Nicole Salowitz
Modeling Skilled Habitual Learning by Maria Bengtson and Joseph DeSouza
Friday night groups:
Kiwii Platform: Using Kinect and Wii Board to Probe Visual and Postural Effects on Balance. (Abstract) by Kahori Kita, John Rocamora, Yoshiyuki Sato, Frank Schumann, Scott Yang
Using the Wii Remote to investigate model-based and model-free learning of visuo-motor rotations with the wrist by Alvin Chin, Kasey Hemington, Luca Lonini and Angelina Paolozza
Comparison of Decoding and Encoding Methods for Motor Cortical Spiking Data by Mikael Lindahl, Alexander Rajan, Stefan Habenschuss, John Butcher, Naama Kadmon
Deciding when to cut your losses (Abstract) by Matt Cieslak, Tobias Kluth, Maren Stiels, Daniel Wood
The relationship between activity of neurons recorded simultaneously in primary motor cortex. by Maxym Myroshnychenko, Joan Deffeyes, Azadeh Yazdan, Diana Mitchell
Advanced fMRI analyses and Kalman filter application by Sara Fabbri, Heather McGregor, Simona Monaco, and Na Jin Seo