CoSMo 2014 - Revision history

Gunnar at 14:45, 6 February 2018

2018-02-06T14:45:18Z

← Older revision		Revision as of 14:45, 6 February 2018
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	[[File:CoSMo2014small.jpg \| thumb \| CoSMo logo]]		[[File:CoSMo2014small.jpg \| thumb \| CoSMo logo]]


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	== Introduction - overview of sensory-motor control ==		== Introduction - overview of sensory-motor control ==
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	[[Media:Carmena_bci_model.pdf \| Jimenez, Heliot and Carmena 2009]] <br>		[[Media:Carmena_bci_model.pdf \| Jimenez, Heliot and Carmena 2009]] <br>
	[[Media:Hsiao_2011.pdf \| Hsiao, Fettiplace and Darbandi 2011]] <br>		[[Media:Hsiao_2011.pdf \| Hsiao, Fettiplace and Darbandi 2011]] <br>

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	*[[Media:Wei_2010.pdf \| Wei 10]] -- movement in differing force fields <br>		*[[Media:Wei_2010.pdf \| Wei 10]] -- movement in differing force fields <br>
	*[[Media:Young_2009.pdf \| Young]] -- movement time stayed the same, but distance changed; fast, medium, slow reaches. <br>		*[[Media:Young_2009.pdf \| Young]] -- movement time stayed the same, but distance changed; fast, medium, slow reaches. <br>

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	'''Afternoon tutorial 2''' <br>		'''Afternoon tutorial 2''' <br>
	[[Media:Kalmanfilter_HW.pptx \| Kalman filter assignment]] <br>		[[Media:Kalmanfilter_HW.pptx \| Kalman filter assignment]] <br>

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	'''Afternoon tutorials 2''' <br>		'''Afternoon tutorials 2''' <br>
	[[Media:CoSMo_2014_lab.pdf \| Tutorial instructions]] and [[Media:DIRECT_model.zip \| Matlab code]] <br>		[[Media:CoSMo_2014_lab.pdf \| Tutorial instructions]] and [[Media:DIRECT_model.zip \| Matlab code]] <br>

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	RIC data set: ask Levi Hargrove <br>		RIC data set: ask Levi Hargrove <br>
	[[Media:Ninapro_Data.zip \| Ninapro data]] <br>		[[Media:Ninapro_Data.zip \| Ninapro data]] <br>

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	2) Estimate the prior from feedback as a function of the number of learning trials		2) Estimate the prior from feedback as a function of the number of learning trials
	3) How accurate do we expect the prior to be after 1010 trials? a) with perfect memory b) with imperfect memory where you forget? For part b, you will need a kalman filter.		3) How accurate do we expect the prior to be after 1010 trials? a) with perfect memory b) with imperfect memory where you forget? For part b, you will need a kalman filter.

			----

	== Computational neuroscience in industry ==		== Computational neuroscience in industry ==
	''Aug 14, 4pm'' <br>		''Aug 14, 4pm'' <br>
	Lecturers: Siddharth Dani, Rahul Gupta, Jadin Jackson, Ashutosh Chaturvedi ([http://www.medtronic.com/ Medtronic])		Lecturers: Siddharth Dani, Rahul Gupta, Jadin Jackson, Ashutosh Chaturvedi ([http://www.medtronic.com/ Medtronic])

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	== Computational Neuroimaging ==		== Computational Neuroimaging ==
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	[http://ctnsrv.uwaterloo.ca/vandermeerlab/doku.php?id=analysis:cosmo2014 Day 2 tutorial and Data ]<br>		[http://ctnsrv.uwaterloo.ca/vandermeerlab/doku.php?id=analysis:cosmo2014 Day 2 tutorial and Data ]<br>
	Log in using cosmo2014		Log in using cosmo2014

			----

	== Surveys for CoSMo 2014 ==		== Surveys for CoSMo 2014 ==
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	<br>		<br>

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	== Final Project Presentations - Aug 15, 4:30-5:30pm and 7-11pm ==		== Final Project Presentations - Aug 15, 4:30-5:30pm and 7-11pm ==

Mike at 00:25, 31 January 2018

2018-01-31T00:25:41Z

← Older revision		Revision as of 00:25, 31 January 2018
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	You can get the DREAM project from Gunnar on a USB drive. 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 [https://crcns.org/join_form?came_from=http%3A//crcns.org/data-sets/movements/dream/downloading-dream create] an account on CRCNS to be able to download the project files.		You can get the DREAM project from Gunnar on a USB drive. 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 [https://crcns.org/join_form?came_from=http%3A//crcns.org/data-sets/movements/dream/downloading-dream create] an account on CRCNS to be able to download the project files.
	<!--[[File:AllDream.zip]]-->		<!--[[File:AllDream.zip]]-->
	If you want "all" of DREAM (models, tools, and documentation), click here: [http://~~klab~~.~~smpp.northwestern.edu~~/wiki/images~~/d/d2~~/AllDream.zip AllDream.zip] <br>		If you want "all" of DREAM (models, tools, and documentation), click here: [http://compneurosci.com/wiki/images/AllDream.zip AllDream.zip] <br>

	:If you're familiar with svn and would like info/credentials for code in the repository, contact [[Ben Walker]]		:If you're familiar with svn and would like info/credentials for code in the repository, contact [[Ben Walker]]

Mike: 89 revisions imported

2018-01-23T17:40:19Z

89 revisions imported

New page

This page contains course materials for the [http://www.compneurosci.com/CoSMo/program.html CoSMo 2014] summer school.
[[File:CoSMo2014small.jpg | thumb | CoSMo logo]]

== Introduction - overview of sensory-motor control ==
''Aug 4-5'' 
Lecturers: Gunnar Blohm, Kurt Thoroughman, Paul Schrater

[[Media:dayan_abbott_ch1fig.ppt | Thoroughman slides 1]] 
[[Media:dayan_abbott_ch2fig.ppt | Thoroughman slides 2]] 
[[Media:Thoroughman_notes_mon.pdf | Thoroughman notes]] 
[[Media:SensoryMotorBlohm2014part1.pdf | Blohm slides part 1]] 
[[Media:Cosmo_tuesday_thoroughman_notes.pdf | Thoroughman notes]] 
[[Media:ECOG_BCI_Bundy.pdf | Thoroughman slides 1]] 
[[Media:GNS2012THACHbg.pdf | Thoroughman slides 2]] 
[[Media:GNS2012THACHcbl.pdf | Thoroughman slides 3]] 
[[Media:GNS2012THACHmc.pdf | Thoroughman slides 4]] 
[[Media:SensoryMotorBlohm2014part2.pdf | Blohm slides part 2]] 
[[Media:InterpretingImagingStudies2011.pdf | Thoroughman - Interpreting imaging studies]] 
[[Media:HowtoModel2014.pdf | How to model]] 

'''Afternoon tutorials''' 
Do Iterations, Fibonacci Numbers and Matrices in [http://www.mathworks.com/moler/exm/chapters.html Moler's Matlab tutorial] 
Reproduce figure 1 from [[Media:Pouget_snyder_2000.pdf‎ | Pouget & Snyder paper]]: [[Media:one_d_gabor_cosine_scr.m | an example Matlab code]] 
[[Media:tutorial_BayesianDecoding2014.pdf | Bayesian tutorial]] and related [[Media:DatasetReal.mat | Data set]] 
[[Media:Bayes_problem_and_solution_Cosmo2014.pdf | Light bulb tutorial]] and related [[Media:BME590Q_Bayes.mat | data set]]. Feel free to also consult [[Media:dayan_abbott_chap3.pdf | Dayan & Abbott chapter 3]] 
[[Media:Light_estimation.m | A solution for the light bulb tutorial...]] 

'''Additional documents''' 
[[Media:poggio_bizzi_nature_04.pdf | Poggio & Bizzi 2004 paper]] 
[[Media:eve_viewpoint_nn2000.pdf | Eve Marder's viewpoint on modelling]] 
[[Media:Carmena_bci_model.pdf | Jimenez, Heliot and Carmena 2009]] 
[[Media:Hsiao_2011.pdf | Hsiao, Fettiplace and Darbandi 2011]] 

== DREAM database - Introduction to the data and model sharing initiative ==
''Aug 4 (evening)''

Lecturer: Gunnar Blohm

You can get the DREAM project from Gunnar on a USB drive. 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 [https://crcns.org/join_form?came_from=http%3A//crcns.org/data-sets/movements/dream/downloading-dream create] an account on CRCNS to be able to download the project files.

If you want "all" of DREAM (models, tools, and documentation), click here: [http://klab.smpp.northwestern.edu/wiki/images/d/d2/AllDream.zip AllDream.zip] 

:If you're familiar with svn and would like info/credentials for code in the repository, contact [[Ben Walker]]

Here's the latest version of [[Media:LoadDreamPaths.m | LoadDreamPaths.m]]. (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 (click on the link to access the related publication).
*[[Media:Burns_2010.pdf | Burns]] -- reaching with head tilt and left/right visual perturbations 
*[[Media:Corbett_2013.pdf | Corbett]] -- reach trajectory predictions based on EMG and gaze movements 
*[[Media:Fernandes_2012_e1_published.pdf | Fernandes]] -- reaching with uncertain and rotated midpoint feedback 
*[[Media:Flint_2012.pdf | Flint]] -- decoding of reaching movements from local field potentials 
*[[Media:Kording_2004.pdf | Kording]] -- reaching with uncertain midpoint feedback 
*[[Media:Mattar_and_Ostry_2007.pdf | Mattar 07]] -- generalizing from one, two or multi targets to another direction 
*[[Media:Mattar_and_Ostry.pdf | Mattar 10]] -- reaching to a distance (short/long), generalizing to the other one (long/short) 
*[[Media:Somatosensory_Plasticity_and_Motor_Learning.pdf | Ostry]] -- move in force field, get an estimation of where the hand is 
*[[Media:Scott_2001.pdf | Scott]] -- monkey (no spike), center out: even and not evenly distributed targets, also a forward/back 
*[[Media:Stevenson2011.pdf | Stevenson]] -- center out, monkey with neural time stamps 
*[[Media:Thoroughman_2005.pdf | Thoroughman]] -- reach adaptation to perturbations with different complexity 
*[[Media:Vahdat_2011.pdf | Vahdat]] -- movement in force field with FMRI scans pre/post learning 
*[[Media:Wei_Relevance_Error_Motor_Adapt_2008.pdf | Wei 08]] -- visual perturbations, cursor shown only at target 
*[[Media:Wei_2010.pdf | Wei 10]] -- movement in differing force fields 
*[[Media:Young_2009.pdf | Young]] -- movement time stayed the same, but distance changed; fast, medium, slow reaches. 

== Motor control & learning ==
''Aug 6-7'' 
Lecturers: Reza Shadmehr, Adrian Haith, Alaa Ahmed

[[Media:CoSMo_2104_AMH.pdf | Haith lecture]] 
[[Media:AhmedCoSMo2014.pdf | Ahmed lecture]] 
Reza's slides cannot be posted due to copyright/embargo rules. If you would like a copy of them, please ask Gunnar.

'''Afternoon tutorial 1''' 
[[Media:ControlAssignment2014.pdf | Instructions]] 
[[Media:SaccadeOpt.m | Saccade code]] 

'''Afternoon tutorial 2''' 
[[Media:Kalmanfilter_HW.pptx | Kalman filter assignment]] 

== Sensory-motor transformations ==
''Aug 8-9'' 
Lecturers: Andrea Green and Paul Cisek

[[Media:COSMO_oculomotor_class_notes_rGreen.pdf | Green lecture]] 
[[Media:CoSMo2014-Cisek.pdf | Cisek lecture]] 

'''Afternoon tutorials 1''' 
[[Media:GreenLabCoSMo2014.zip | Tutorial files]] 

'''Afternoon tutorials 2''' 
[[Media:CoSMo_2014_lab.pdf | Tutorial instructions]] and [[Media:DIRECT_model.zip | Matlab code]] 

== Prosthetics ==
''Aug 11-12'' 
Lecturers: Jon Sensinger and Levi Hargrove

[[Media:Hargrove_Presentation_1.pdf | Hargrove lecture 1]] 
[[Media:Hargrove_Part2.pdf | Hargrove lecture 2]] 

'''Afternoon tutorial 1''' 
[[Media:SensingerLabFilesCoSMo2014.zip | Tutorial files]] 

'''Afternoon 2 tutorials''' 
[[Media:InstructionsHargroveCoSMo2014.docx | Tutorial instructions]] and [[Media:Lda.m | code]] 
RIC data set: ask Levi Hargrove 
[[Media:Ninapro_Data.zip | Ninapro data]] 

== The Bayesian Brain ==
''Aug 13-14'' 
Lecturers: Adam Johnson, Paul Schrater
 
[[Media:COSMOpaul2014.pdf | Schrater slides]] 
[[Media:2014_CoSMo_v02.pptx | Johnson slides]] 

'''Afternoon tutorials 1''' 
[[Media:Cosmo2014SchraterDay1.zip | Tutorial files]] (instructions inside the *.m files) he
There are 3 problems to work on today.
 
'''Afternoon tutorials 2''' 
[[Media:cosmo_exercise.pdf | Learning tutorial instructions]]
 
[[Media:Modellearning03.m | Matlab file]] 

"Problem 1"
In the first, start with AttractProjectGoals.m. This file has two other files, face imanalysis.mat and faceimgui.m
The project explores the question "Where do cues come from?" in cue combination. Normally there are clever
guesses by experimenters, but in less studied domains little is known about the information subjects use to infer
properties. In this project we treat a toy version of a real problem - what are the cues to facial attractiveness?
Here I have taken a database of images together with 1-10 rating scale attractiveness ratings, and done an initial
unsupervised dimensionality reduction of the images. Your job is to characterize the cues to attractiveness ratings given
the low dimensional image representation via a simple data analysis. Each dimension is a potential cue to attractiveness.
Your goal is to characterize P(cue_j|attractiveness). Use the faceimgui to interactively view the relationship
between the cues and the face images. Load the face images use the faceimananalysis.mat file.

Then the challenge is to remove the cue independence assumptions and do the analysis again. The second part will require you to estimate the joint probability of the cues P(cue1,cue2,cue3,...|attractiveness). One possibility is to assume multivariate gaussian.

"Problem 2"
In the second problem, you will work through the explaining away model described in the class for image size and touch. The instructions are in the ExplainingAway.m file.

"Problem 3"
In the third problem, we will explore simple Bayes by analyzing data from the Dream database, Kording 2004.
Go to http://crcns.org/data-sets/movements/dream/data-sets/Kording_2004/ and download the paper and the dataset.
In this tutorial, we will predict data from Koerding and Wolpert 2004, then fit the actual data.

In the experiments, they have subjects move a cursor to a target at 0cm with a random lateral shift between the hand position and the cursor. The cursor's position is rendered with 2 levels of blur or occluded, increasing the uncertainty about target position, predicting an increased reliance on the prior.

On each trial, there is a true shift, xtrue, given by the cursor offset. There is also a noisy estimate of the cursor position, "sensed".
From these subjects form an estimate of the cursor position from data and prior knowledge.

Assuming that reach endpoints reflect the best estimate of the target location, we can compare predictions and reach endpoints.

x_hat = argmax P(xtrue|xsensed), which for Gaussians is the mean of P(xtrue|xsensed)
By Bayes P(xtrue|xsensed) = P(xsensed| xtrue)P(xtrue)/P(xsensed)
where P(xsensed) = Int_{xtrue} P(xsensed| xtrue)P(xtrue) dxtrue

We need to form the prior P(xtrue) and the likelihood P(xsensed| xtrue)
Using the following parameters, for today your goal is to produce simulations that replicate the prediction graphs in the paper.
mu_prior = .01; sd_prior = 0.5/100;

xtrue = [-0.015 -0.01 -0.005 0 0.005 0.01 0.015];
sd_smallblur = 0.1/100;
sd_largeblur = 1/100;

Use Bayes rule to derive the formula relating the cursor perturbation and prior knowledge. Then compute predictions for each of the four conditions.

SIMULATE THE OBSERVER FOR ONE CONDITION
We will assume the observer has 1010 trials of feedback to estimate the prior. Then the subject's estimates of the prior's parameters are probably approximately correct.
mu_prior_hat is approximately mu_prior;
sd_prior_hat approximately sd_prior;

For 300 trials, draw the following random variables:
cue location: cp (randomly draw by the experimenter from a gaussian with mean and std = prior)
sensed position: sp (gaussian distributed with mean = cp and additional sensory noise std = .15/100)
hand position estimate: Bayes estimate combining sensed and prior.

Treat this as data, and analyze it like in the Kording paper for ONE condition.

Challenge: Relax the assumption that subject's have learned the prior accurately.
1) Simulate data as above.
2) Estimate the prior from feedback as a function of the number of learning trials
3) How accurate do we expect the prior to be after 1010 trials? a) with perfect memory b) with imperfect memory where you forget? For part b, you will need a kalman filter.

== Computational neuroscience in industry ==
''Aug 14, 4pm'' 
Lecturers: Siddharth Dani, Rahul Gupta, Jadin Jackson, Ashutosh Chaturvedi ([http://www.medtronic.com/ Medtronic])

== Computational Neuroimaging ==
''Aug 15-16'' 
Lecturers: Thilo Womelsdorf, Matthijs van der Meer

[[Media: Womelsdorf_CosMo_Lecture_1_Prelude.pdf | Womelsdorf_CosMo_Lecture_1_Prelude.pdf ]] 
[[Media: Womelsdorf_CosMo_Lecture_2_Synchronization.pdf | Womelsdorf_CosMo_Lecture_2_Synchronization.pdf ]] 
[[Media: Womelsdorf_CosMo_Lecture_3_ConnectivityMeasures.pdf | Womelsdorf_CosMo_Lecture_3_ConnectivityMeasures.pdf ]] 
[[Media: Womelsdorf_CosMo_Lecture_4_Fieldtrip_Tutorials.pdf | Womelsdorf_CosMo_Lecture_4_Fieldtrip_Tutorials.pdf ]] 
[[Media: MvdM_CoSMo2014.pdf | van der Meer lecture]] 

'''Afternoon tutorials''' 
[[Media: Womelsdorf_CosMo_Overview.pdf | Day 1 tutorial ]] 
[http://ctnsrv.uwaterloo.ca/vandermeerlab/doku.php?id=analysis:cosmo2014 Day 2 tutorial and Data ] 
Log in using cosmo2014

== Surveys for CoSMo 2014 ==
[https://www.surveymonkey.com/s/B7DCLGJ CoSMo overall] 
 
[https://www.surveymonkey.com/s/PXC2DMQ Introduction Unit] 
[https://www.surveymonkey.com/s/PNBCNYJ Motor Control Unit] 
[https://www.surveymonkey.com/s/PN5VHV7 Sensory-motor Unit] 
[https://www.surveymonkey.com/s/PFL57MM Prosthetics Unit] 
[https://www.surveymonkey.com/s/D7F5XK6 Bayesian Brain Unit ] 
[https://www.surveymonkey.com/s/D7P2J9R Computational Neuroimaging Unit] 

 

== Final Project Presentations - Aug 15, 4:30-5:30pm and 7-11pm ==

'''Presentation schedule''' 

4:30pm - ''The Kalman touch'' 
Predicting force with expectation: a haptic softness task 
Group: Alexandra Lezkan, Elisabeth Rounis, Stefanie Mueller, Simone Toma, Ali Borji 

5:00pm - ''Rolling heads'' 
Multisensory integration in the perception of verticality 
Group: Jorge Otero-Millan, Shany Grossman, Parisa Abedi, Anouk de Brouwer 

'''5:30pm - 7:00pm - dinner break''' 

7:00pm - ''Arousing Decisions'' 
Group: Taraz, Windy, Dominic, Giuseppe, Giovanni 

7:30pm - ''Triple Threat a.k.a. The SuperModel(ers) a.k.a. The CoSMo-nauts'' 
Group: Reva Johnson, Amit Shah, Sean Barton, Chad Heley, Xing Chen 

8:00pm - ''Tic Toc Pong'' 
Adaptation to Delay while Playing Pong: Time or State Representation? 
Group: Romy Bakker, Jemina Fasola, Guy Avraham, Raz Leib 

8:30pm - ''Group X'' 
The Effects of Motor Behaviors on the Temporal Ventriloquist Aftereffect 
Group: Jacob Matthews, Brian Odegaard, Meytar Zemer 

9:00pm - ''Bayesic Kalman Sense'' 
Group: Emily Lawrence, General Lee, Gelsy Torres-Oviedo 

9:30pm - ''Followers of Titipat'' 
Group: Titipat A., Vynn H., Carly S., Pablo I. 

10:00pm - ''On Fire'' 
Two Balls: The Mapping of Arbitrary Multimodal Stimuli 
Group: Chin-Hsuan Lin, Michael Olshansky, and Salvatore Fara 

10:30pm - ''HuStLa’z'' 
There and Back Again: Vestibular Adaptation in Outer Space 
Group: Josh Cashaback, Ethan Oblak, Melodie Tian, Qianli Yang 

'''Please rate project presentations [https://www.surveymonkey.com/s/93B79HH HERE]'''