Blohm Lab Wiki - User contributions [en-ca]

Learning Resources

2021-09-29T14:09:33Z

Matthew:

== Bayesian Nonparametrics ==
* [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]
* [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).
* [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.
* [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&hl=en&oi=ao Any of the papers] of Radford Neal.
* The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]
* [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]
* [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli
* [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience
* [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]
* [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2
* [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij

== Information Theory ==
* [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience]
* [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.
* [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]

== Machine Learning ==
==== Books ====
* [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control]
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://probml.github.io/pml-book/ Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.
* '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''
* [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]
* [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt & Recht
* [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts & Yaida

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities].
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
* [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.
* [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler

== MATLAB ==
* [https://www.coursera.org/learn/matlab Intro to programming] with Matlab
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] ("The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.")
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]
* [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts

== Python ==
* [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch
* [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python
* [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]
* [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]
* [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]
* [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]
* [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]
* [https://statsthinking21.github.io/statsthinking21-core-site/ "Statistical Thinking for the 21st Century"] free online book by Russell A. Poldrack
* [http://www.stat.columbia.edu/~gelman/book/ "Bayesian Data Analysis"] book by Andrew Gelman et al. with examples in Python and R
* [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]
* [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free
* [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free
* [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free

== Neuroimaging analyses ==
* [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]

Learning Resources

2019-06-06T22:47:32Z

Matthew:

Learning Resources

2018-12-11T20:38:41Z

Matthew: Bayesian nonparametrics resources

Learning Resources

2018-10-26T15:43:01Z

Matthew:

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Information Theory ==
* [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience]
* [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities].
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Software

2018-10-05T16:04:38Z

Matthew:

== General ==
* [https://git-scm.com/ Git] (free) - version control software and [[Media:Canada-git-presentation_Dmytro.pdf | Dmytro's tutorial]]
* [http://www.ghisler.com/ Total Commander] (free) - file manager
* [https://winscp.net/eng/index.php WinSCP] (free) - file transfer tool
* [https://www.mendeley.com/ Mendeley reference manager] (free) - manage references & automatic bibliography generator for MSWord, LibreOffice and BibTeX
* [http://www.seamonkey-project.org/ SeaMonkey] (free) - internet application suite including web editor

== Text Editors ==
* [http://www.texniccenter.org/ TeXnicCenter] (free) - LateX editor
* [https://atom.io/ Atom] - Customizable text editor by GitHub. Allows for real-time collaboration (!) and has not-too-clumsy Git integration.
* [http://spacemacs.org/ Spacemacs] - A distribution of Emacs, at the deep end of customizable text editors. Emacs has many "modes"; the most useful is probably [https://orgmode.org/ Org mode], which is a kind of outliner+agenda that can be used to rapidly create LaTeX/Beamer files, and for [https://hal.archives-ouvertes.fr/hal-00591455 literate programming] (think Jupyter), among [http://ehneilsen.net/notebook/orgExamples/org-examples.html other uses]. Compared to plain Emacs, Spacemacs starts with a better configuration that is more modular and easier to customize, and allows for the (optional) use of Vim keybindings.

== Imaging Analysis ==
* [http://www.fil.ion.ucl.ac.uk/spm/ SPM] (free) - general purpose (fMRI oriented)
* [http://www.lcs.poli.usp.br/~baccala/pdc/CRCBrainConnectivity/AsympPDC/ PDC] (free) - partially directed coherence analysis
* [http://www.fieldtriptoolbox.org/ FieldTrip] (free) - general purpose (EEG/MEG oriented)
* [http://cheynelab.utoronto.ca/brainwave Brainwave] (free) and [http://cheynelab.utoronto.ca/httpdoc/Brainwave_v3.1_Documentation_6October_2015_final.pdf documentation] - MEG analysis toolbox
* [http://brainvis.wustl.edu/wiki/index.php/Caret:Download CARET] (free) - anatomical reconstruction toolkit for structural and anatomical data ([http://prefrontal.org/blog/2009/04/using-caret-for-fmri-visualization/ simple tutorial])
* [http://neurosynth.org/ Atlas for identifying brain regions / function] based on fMRI meta-analyses

== Machine Learning ==
* [http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html Kevin Murphy's Kalman filter toolbox] (free)
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html libSVM] - Support Vector Machine Library
*[https://github.com/merwan/ml-class Andrew Ng's ML Programming Exercises]

== Vector Graphics ==
* [http://www.coreldraw.com/ca/ CorelDraw] - ask Gunnar for installation CD
* [https://inkscape.org/en/ Inkscape] (free)

== Statistics ==
* [http://statcheck.io/ statcheck] - Use this to check papers for errors in statistical reporting!
* SPSS - general purpose
* [https://www.r-project.org/ R] (free) - general purpose with [http://r4ds.had.co.nz/ great online book]!
* [http://psignifit.sourceforge.net/ psignifit] (free) - psychometric function fitting

Learning Resources

2018-04-20T14:46:54Z

Matthew: /* Machine Learning */

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Learning Resources

2018-04-20T14:38:12Z

Matthew: /* Online Resources */

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Learning Resources

2018-04-20T14:37:58Z

Matthew: /* Online Resources */

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Software

2018-04-17T23:27:55Z

Matthew: Text editor section.

== General ==
* [https://git-scm.com/ Git] (free) - version control software and [[Media:Canada-git-presentation_Dmytro.pdf | Dmytro's tutorial]]
* [http://www.ghisler.com/ Total Commander] (free) - file manager
* [https://winscp.net/eng/index.php WinSCP] (free) - file transfer tool
* [https://www.mendeley.com/ Mendeley reference manager] (free) - manage references & automatic bibliography generator for MSWord, LibreOffice and BibTeX
* [http://www.seamonkey-project.org/ SeaMonkey] (free) - internet application suite including web editor

== Text Editors ==
* [http://www.texniccenter.org/ TeXnicCenter] (free) - LateX editor
* [https://atom.io/ Atom] - Customizable text editor by GitHub. Allows for real-time collaboration (!) and has not-too-clumsy Git integration.
* [http://spacemacs.org/ Spacemacs] - A distribution of Emacs, at the deep end of customizable text editors. Emacs has many "modes"; the most useful is probably [https://orgmode.org/ Org mode], which is a kind of outliner+agenda that can be used to rapidly create LaTeX/Beamer files, and for [https://hal.archives-ouvertes.fr/hal-00591455 literate programming] (think Jupyter), among [http://ehneilsen.net/notebook/orgExamples/org-examples.html other uses]. Compared to plain Emacs, Spacemacs starts with a better configuration that is more modular and easier to customize, and allows for the (optional) use of Vim keybindings.

== Imaging Analysis ==
* [http://www.fil.ion.ucl.ac.uk/spm/ SPM] (free) - general purpose (fMRI oriented)
* [http://www.lcs.poli.usp.br/~baccala/pdc/CRCBrainConnectivity/AsympPDC/ PDC] (free) - partially directed coherence analysis
* [http://www.fieldtriptoolbox.org/ FieldTrip] (free) - general purpose (EEG/MEG oriented)
* [http://cheynelab.utoronto.ca/brainwave Brainwave] (free) and [http://cheynelab.utoronto.ca/httpdoc/Brainwave_v3.1_Documentation_6October_2015_final.pdf documentation] - MEG analysis toolbox
* [http://brainvis.wustl.edu/wiki/index.php/Caret:Download CARET] (free) - anatomical reconstruction toolkit for structural and anatomical data ([http://prefrontal.org/blog/2009/04/using-caret-for-fmri-visualization/ simple tutorial])
* [http://neurosynth.org/ Atlas for identifying brain regions / function] based on fMRI meta-analyses

== Machine Learning ==
* [http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html Kevin Murphy's Kalman filter toolbox] (free)
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html libSVM] - Support Vector Machine Library
*[https://github.com/merwan/ml-class Andrew Ng's ML Programming Exercises]

== Vector Graphics ==
* [http://www.coreldraw.com/ca/ CorelDraw] - ask Gunnar for installation CD
* [https://inkscape.org/en/ Inkscape] (free)

== Statistics ==
* SPSS - general purpose
* [https://www.r-project.org/ R] (free) - general purpose with [http://r4ds.had.co.nz/ great online book]!
* [http://psignifit.sourceforge.net/ psignifit] (free) - psychometric function fitting

Main Page

2018-04-09T14:11:39Z

Matthew:

Welcome to the [http://www.compneurosci.com/ Blohm lab] wiki. This is where we will post useful information about lab activities, links to software, tutorials etc.

[[File:retreat-2018.jpg|400px|thumb|left|At our January 2018 retreat to Sutton, QC.]]

File:Retreat-2018.jpg

2018-04-09T14:09:51Z

Matthew:

IRTG

2018-04-06T17:11:42Z

Matthew: /* Professional Development Workshops */

== Professional Development Workshops ==
* [Apr 6, 2018] [[Media:IRTG_PD.pptx]]

IRTG

2018-04-06T17:11:03Z

Matthew: Created page with "== Professional Development Workshops == * Media:IRTG_PD.pptx"

== Professional Development Workshops ==
* [[Media:IRTG_PD.pptx]]

MediaWiki:Sidebar

2018-04-06T17:10:09Z

Matthew:

Journal Club

2018-04-04T21:25:05Z

Matthew:

== Articles ==
* [Aug 30, 2017] Shmuelof and Krakauer, 2011. Are we ready for a natural history of motor learning? [[Media:Journal_Club_Aug_30_2017.pdf]] [http://www.sciencedirect.com/science/article/pii/S0896627311009299 Paper]
* [Sept 11, 2017] Thura and Cisek, 2017. The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment. [[File:JC_Sept_11.pdf]]
[https://www.ncbi.nlm.nih.gov/pubmed/28823728 Paper]
* [Jan 11, 2018] Munafò et al., 2017. A manifesto for reproducible science. [https://www.nature.com/articles/s41562-016-0021]
* [March 8, 2018] Castañón et al., 2018 (preprint). Human noise blindness drives suboptimal cognitive inference. [https://www.biorxiv.org/content/early/2018/02/19/268045]
* [March 15, 2018] Odegaard et al., 2017. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. [http://www.pnas.org/content/115/7/E1588.long]
* [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [http://science.sciencemag.org/content/358/6364/793]

== Tutorials ==
* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Monte Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

== Proposed ==
* ...

MediaWiki:Sidebar

2018-04-04T19:17:38Z

Matthew:

Main Page

2018-04-04T19:14:44Z

Matthew:

Welcome to the [http://www.compneurosci.com/ Blohm lab] wiki. This is where we will post useful information about lab activities, links to software, tutorials etc.

CoSMo

2018-04-04T19:13:44Z

Matthew: Created page with "Here are links to the current collection of CoSMo tutorials and teaching material. * [http://compneurosci.com/wiki/index.php/CoSMo_2012 CoSMo 2012] * [http://compneurosci.com/..."

Here are links to the current collection of CoSMo tutorials and teaching material.
* [http://compneurosci.com/wiki/index.php/CoSMo_2012 CoSMo 2012]
* [http://compneurosci.com/wiki/index.php/CoSMo_2013 CoSMo 2013]
* [http://compneurosci.com/wiki/index.php/CoSMo_2014 CoSMo 2014]
* [http://compneurosci.com/wiki/index.php/CoSMo_2015 CoSMo 2015]
* [http://compneurosci.com/wiki/index.php/CoSMo_2016 CoSMo 2016]
* [http://compneurosci.com/wiki/index.php/CoSMo_2017 CoSMo 2017]

Lab Info

2018-04-04T19:12:57Z

Matthew:

== Equipment ==
Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.
----
== Rules and Practices ==
==== Experimental labs ====
* Handle equipment with care! There is about $1M worth of equipment in the lab!
* Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.
* Experiments are only to be carried out after explicit approval of your supervisor!
* Please book experimental sessions on the lab Google calendar!
* After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!
* Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!
* Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out.
* Do not handle food / beverages around lab equipment other than the computers.

==== Research ====
* Write an abstract BEFORE even starting a project!
* Always adhere to the highest ethical and research standards in the field. You are responsible for your research!
* Back up your data frequently! Always imagine you could lose all of your data / work today!
* Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it
* Communicate often with your supervisor!

==== Data / recording / analysis organization ====
* Always back up all data!
* Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...
* Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.
* write clean analysis code! You might want to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)

==== Paper writing ====
* (Re-) write the abstract first!
* Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)
* Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!
* NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!

=== Conferences ===
* Conferences are a reward of good work!
* The goal is for everyone to attend at least 1 conference / year.
* The condition to attend a conference is that you must have NEW material to present!
* Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!
* Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded.
* Conferences are an amazing learning experience! Take fully advantage of them!

Lab Info

2018-04-04T19:12:34Z

Matthew:

Lab Info

2018-04-04T19:12:22Z

Matthew:

== Equipment ==
Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.

== Rules and Practices ==
==== Experimental labs ====
* Handle equipment with care! There is about $1M worth of equipment in the lab!
* Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.
* Experiments are only to be carried out after explicit approval of your supervisor!
* Please book experimental sessions on the lab Google calendar!
* After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!
* Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!
* Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out.
* Do not handle food / beverages around lab equipment other than the computers.
----

==== Research ====
* Write an abstract BEFORE even starting a project!
* Always adhere to the highest ethical and research standards in the field. You are responsible for your research!
* Back up your data frequently! Always imagine you could lose all of your data / work today!
* Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it
* Communicate often with your supervisor!

==== Data / recording / analysis organization ====
* Always back up all data!
* Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...
* Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.
* write clean analysis code! You might want to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)

==== Paper writing ====
* (Re-) write the abstract first!
* Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)
* Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!
* NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!

=== Conferences ===
* Conferences are a reward of good work!
* The goal is for everyone to attend at least 1 conference / year.
* The condition to attend a conference is that you must have NEW material to present!
* Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!
* Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded.
* Conferences are an amazing learning experience! Take fully advantage of them!

Other Resources

2018-04-04T19:12:08Z

Matthew: Created page with "== How to do science == * [http://compneurosci.com/wiki/index.php/Paper_Writing_101 Konrad's paper writing 101] * Konrad's [http://biorxiv.org/content/early/2016/12/14/088278?..."

== How to do science ==
* [http://compneurosci.com/wiki/index.php/Paper_Writing_101 Konrad's paper writing 101]
* Konrad's [http://biorxiv.org/content/early/2016/12/14/088278?utm_content=buffer8d04a&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer 10 simple rules for structuring papers] advice: read this before you start writing!!!
* [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!
* [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications
* [http://neuronline.sfn.org/Articles/Professional-Development/2016/Tricks-of-the-Trade-How-to-Peer-Review-a-Manuscript How to peer review a manuscript]: Webinar and resources from SfN Neuronline.
* [http://neuronline.sfn.org/Articles/Career-Advice/2017/Tricks-of-the-Trade-Modelling-Papers-Resources How To Review Modelling Papers]: A webinar + resources from SfN Neuronline. The webinar date has passed, but if you 'register' there is an email link that will lead you to the archived video.
* [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4284499/ The pleasure of publishing (Malhotra & Marder, 2015)]: Elife editors' opinions on what makes an effective manuscript.
----

== How to be successful ==
* [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer Ed Boyden's advice on how to think]
* [http://www.wikihow.com/Think Developing better thought processes]
* [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]
* [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]
* [http://www.cell.com/neuron/pdf/S0896-6273(15)00331-1.pdf Hitchhikers guide to a Career in Neuroscience]
* [http://www.programmerfu.com/2017/04/20/fast-is-slow-slow-is-smooth-smooth-is-fast.html Slow is smooth and smooth is fast]
* [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.8391&rep=rep1&type=pdf Survival Skills for Graduate School and Beyond] (Fischer and Zigmond, 1998; still very applicable)
* [https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/EWD637.html 3 golden rules of scientific success] by Dijkstra
----

== Work-life balance & mental health ==
As an undergraduate, graduate student or postdoc, as exciting a time as this is, you will face stress, frustration and deception. Your passion for science can suck up all your time and wack your physical and mental health out of balance. This can lead to a downward spiral out of which comes no good. Yes, grad school / postdoc work is hard, don't let it destroy you. So here are a few tips to avoid that in the first place.
* Get enough exercise and sleep!
* [http://www.nextscientist.com/work-life-balance-in-academia/ The Happy PhD zone]: how to maintain a work-life balance in academia
* [http://thegradstudentway.com/blog/?p=76#.WRZWlGfSkvc 6 Ways To Survive Grad School and Achieve Work-Life Balance]
* [https://www.mcgill.ca/gradsupervision/supervisees/work-life Official McGill University guidelines] on work-life balance
* [https://www.bustle.com/articles/87409-graduate-school-is-hard-so-here-are-8-simple-ways-to-maintain-your-sanity How to maintain your sanity] during grad school
* [https://psychcentral.com/lib/12-tips-for-surviving-and-thriving-in-grad-school/ Grad school survival tips]
* [http://www.nature.com/nature/journal/v545/n7654/full/nj7654-375a.html?WT.mc_id=FBK_NatureNews&sf79769979=1 Break or burn out]: a nice article in Nature
*[https://www.psychologytoday.com/files/attachments/1035/arts-foster-scientific-success.pdf Creative outlets and Scientific Success] Scientists are not more likely to have creative outlets, but the most successful ones are

Software

2018-04-04T19:11:37Z

Matthew:

== General ==
* [http://www.ghisler.com/ Total Commander] (free) - file manager
* [https://winscp.net/eng/index.php WinSCP] (free) - file transfer tool
* [http://www.texniccenter.org/ TeXnicCenter] (free) - LateX editor
* [https://www.mendeley.com/ Mendeley reference manager] (free) - manage references & automatic bibliography generator for MSWord, LibreOffice and BibTeX
* [http://www.seamonkey-project.org/ SeaMonkey] (free) - internet application suite including web editor
* [https://git-scm.com/ Git] (free) - version control software and [[Media:Canada-git-presentation_Dmytro.pdf | Dmytro's tutorial]]

== Imaging Analysis ==
* [http://www.fil.ion.ucl.ac.uk/spm/ SPM] (free) - general purpose (fMRI oriented)
* [http://www.lcs.poli.usp.br/~baccala/pdc/CRCBrainConnectivity/AsympPDC/ PDC] (free) - partially directed coherence analysis
* [http://www.fieldtriptoolbox.org/ FieldTrip] (free) - general purpose (EEG/MEG oriented)
* [http://cheynelab.utoronto.ca/brainwave Brainwave] (free) and [http://cheynelab.utoronto.ca/httpdoc/Brainwave_v3.1_Documentation_6October_2015_final.pdf documentation] - MEG analysis toolbox
* [http://brainvis.wustl.edu/wiki/index.php/Caret:Download CARET] (free) - anatomical reconstruction toolkit for structural and anatomical data ([http://prefrontal.org/blog/2009/04/using-caret-for-fmri-visualization/ simple tutorial])
* [http://neurosynth.org/ Atlas for identifying brain regions / function] based on fMRI meta-analyses

== Machine Learning ==
* [http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html Kevin Murphy's Kalman filter toolbox] (free)
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html libSVM] - Support Vector Machine Library
*[https://github.com/merwan/ml-class Andrew Ng's ML Programming Exercises]

== Vector Graphics ==
* [http://www.coreldraw.com/ca/ CorelDraw] - ask Gunnar for installation CD
* [https://inkscape.org/en/ Inkscape] (free)

== Statistics ==
* SPSS - general purpose
* [https://www.r-project.org/ R] (free) - general purpose with [http://r4ds.had.co.nz/ great online book]!
* [http://psignifit.sourceforge.net/ psignifit] (free) - psychometric function fitting

Journal Club

2018-04-04T19:10:53Z

Matthew:

== Articles ==
* [Aug 30, 2017] Shmuelof and Krakauer, 2011. Are we ready for a natural history of motor learning? [[Media:Journal_Club_Aug_30_2017.pdf]] [http://www.sciencedirect.com/science/article/pii/S0896627311009299 Paper]
* [Sept 11, 2017] Thura and Cisek, 2017. The Basal Ganglia Do Not Select Reach Targets but Control the Urgency of Commitment. [[File:JC_Sept_11.pdf]]
[https://www.ncbi.nlm.nih.gov/pubmed/28823728 Paper]
* [Jan 11, 2018] Munafò et al., 2017. A manifesto for reproducible science. [https://www.nature.com/articles/s41562-016-0021]
* [March 8, 2018] Castañón et al., 2018 (preprint). Human noise blindness drives suboptimal cognitive inference. [https://www.biorxiv.org/content/early/2018/02/19/268045]
* [March 15, 2018] Odegaard et al., 2017. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. [http://www.pnas.org/content/115/7/E1588.long]
* [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [http://science.sciencemag.org/content/358/6364/793]

== Tutorials ==
* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

== Proposed ==
* ...

Learning Resources

2018-04-04T19:10:35Z

Matthew:

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
==== Books ====
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).

==== Online Resources ====
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Learning Resources

2018-04-04T19:10:14Z

Matthew: Created page with " == General Computational Neuroscience == * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lect..."

== General Computational Neuroscience ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]

== Machine Learning ==
=== Books ===
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).

=== Online Resources ==
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.

=== Journal Club Tutorials ===
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]

== MATLAB ==
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

== Statistics ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats book]
* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation]
* how to do [[Media:Repeated_ANOVA_MATLAB.pdf | repeated measures ANOVA]] in Matlab (by Parisa)
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]

Lab Info

2018-04-04T19:09:38Z

Matthew: Created page with "== Equipment == Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment. == Rules and Practices == ==== Experimental labs ==== * Handle equi..."

== Equipment ==
Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.

== Rules and Practices ==
==== Experimental labs ====
* Handle equipment with care! There is about $1M worth of equipment in the lab!
* Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.
* Experiments are only to be carried out after explicit approval of your supervisor!
* Please book experimental sessions on the lab Google calendar!
* After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!
* Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!
* Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out.
* Do not handle food / beverages around lab equipment other than the computers.

==== Research ====
* Write an abstract BEFORE even starting a project!
* Always adhere to the highest ethical and research standards in the field. You are responsible for your research!
* Back up your data frequently! Always imagine you could lose all of your data / work today!
* Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it
* Communicate often with your supervisor!

==== Data / recording / analysis organization ====
* Always back up all data!
* Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...
* Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.
* write clean analysis code! You might want to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)

==== Paper writing ====
* (Re-) write the abstract first!
* Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)
* Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!
* NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!

=== Conferences ===
* Conferences are a reward of good work!
* The goal is for everyone to attend at least 1 conference / year.
* The condition to attend a conference is that you must have NEW material to present!
* Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!
* Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded.
* Conferences are an amazing learning experience! Take fully advantage of them!

Journal Club

2018-04-04T19:07:21Z

Matthew:

== Articles ==
* [Aug 30, 2017] Shmuelof and Krakauer, 2011. Are we ready for a natural history of motor learning? [[Media:Journal_Club_Aug_30_2017.pdf]] [http://www.sciencedirect.com/science/article/pii/S0896627311009299 Paper]
* [Sept 11, 2017] Thura and Cisek, 2017. The Basal Ganglia Do Not Select Reach
Targets but Control the Urgency of Commitment. [[File:JC_Sept_11.pdf]]
[https://www.ncbi.nlm.nih.gov/pubmed/28823728 Paper]
* [Jan 11, 2018] Munafò et al., 2017. A manifesto for reproducible science. [https://www.nature.com/articles/s41562-016-0021]
* [March 8, 2018] Castañón et al., 2018 (preprint). Human noise blindness drives suboptimal cognitive inference. [https://www.biorxiv.org/content/early/2018/02/19/268045]
* [March 15, 2018] Odegaard et al., 2017. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. [http://www.pnas.org/content/115/7/E1588.long]
* [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [http://science.sciencemag.org/content/358/6364/793]

== Tutorials ==
* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

== Proposed ==
* ...

Journal Club

2018-04-04T19:06:57Z

Matthew:

== Articles ==
* [Aug 30, 2017] Shmuelof and Krakauer, 2011. Are we ready for a natural history of motor learning? [[Media:Journal_Club_Aug_30_2017.pdf]] [http://www.sciencedirect.com/science/article/pii/S0896627311009299 Paper]
* [Sept 11, 2017] Thura and Cisek, 2017. The Basal Ganglia Do Not Select Reach
Targets but Control the Urgency of Commitment. [[File:JC_Sept_11.pdf]]
[https://www.ncbi.nlm.nih.gov/pubmed/28823728 Paper]
* [Jan 11, 2018] Munafò et al., 2017. A manifesto for reproducible science. [https://www.nature.com/articles/s41562-016-0021]
* [March 8, 2018] Castañón et al., 2018 (preprint). Human noise blindness drives suboptimal cognitive inference. [https://www.biorxiv.org/content/early/2018/02/19/268045]
* [March 15, 2018] Odegaard et al., 2017. Superior colliculus neuronal ensemble activity signals optimal rather than subjective confidence. [http://www.pnas.org/content/115/7/E1588.long]
* [March 22, 2018] Dasgupta, Stevens, Navlakha, 2017. A neural algorithm for a fundamental computing problem. [science.sciencemag.org/content/358/6364/793]

== Tutorials ==
* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

== Proposed ==
* ...

MediaWiki:Sidebar

2018-04-04T19:06:32Z

Matthew:

Blohm Lab Wiki:Matlab

2018-04-04T19:03:02Z

Matthew: Matthew moved page Matlab to Blohm Lab Wiki:Matlab without leaving a redirect

A series of Matlab resources and tutorials

== Tutorials ==

* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]

----

== Matlab resources ==
(see also [[Software]] for more Matlab-related stuff!)

Software

2018-04-04T19:01:01Z

Matthew:

This page contains useful information about research software used in the lab.

== General ==
* [http://www.ghisler.com/ Total Commander] (free) - file manager
* [https://winscp.net/eng/index.php WinSCP] (free) - file transfer tool
* [http://www.texniccenter.org/ TeXnicCenter] (free) - LateX editor
* [https://www.mendeley.com/ Mendeley reference manager] (free) - manage references & automatic bibliography generator for MSWord, LibreOffice and BibTeX
* [http://www.seamonkey-project.org/ SeaMonkey] (free) - internet application suite including web editor
* [https://git-scm.com/ Git] (free) - version control software and [[Media:Canada-git-presentation_Dmytro.pdf | Dmytro's tutorial]]

== Imaging Analysis ==
* [http://www.fil.ion.ucl.ac.uk/spm/ SPM] (free) - general purpose (fMRI oriented)
* [http://www.lcs.poli.usp.br/~baccala/pdc/CRCBrainConnectivity/AsympPDC/ PDC] (free) - partially directed coherence analysis
* [http://www.fieldtriptoolbox.org/ FieldTrip] (free) - general purpose (EEG/MEG oriented)
* [http://cheynelab.utoronto.ca/brainwave Brainwave] (free) and [http://cheynelab.utoronto.ca/httpdoc/Brainwave_v3.1_Documentation_6October_2015_final.pdf documentation] - MEG analysis toolbox
* [http://brainvis.wustl.edu/wiki/index.php/Caret:Download CARET] (free) - anatomical reconstruction toolkit for structural and anatomical data ([http://prefrontal.org/blog/2009/04/using-caret-for-fmri-visualization/ simple tutorial])
* [http://neurosynth.org/ Atlas for identifying brain regions / function] based on fMRI meta-analyses

== Machine Learning ==
* [http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html Kevin Murphy's Kalman filter toolbox] (free)
*[http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html libSVM] - Support Vector Machine Library
*[https://github.com/merwan/ml-class Andrew Ng's ML Programming Exercises]

== Vector Graphics ==
* [http://www.coreldraw.com/ca/ CorelDraw] - ask Gunnar for installation CD
* [https://inkscape.org/en/ Inkscape] (free)

== Statistics ==
* SPSS - general purpose
* [https://www.r-project.org/ R] (free) - general purpose with [http://r4ds.had.co.nz/ great online book]!
* [http://psignifit.sourceforge.net/ psignifit] (free) - psychometric function fitting

Machine learning

2017-10-11T20:14:00Z

Matthew:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).

----

== Online Machine Learning Resources ==
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.

Machine learning

2017-10-11T20:13:05Z

Matthew:

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book], also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book] which has less focus on mathematical foundations and more on applications (in R).

----

== Online Machine Learning Resources ==
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.

Machine learning

2017-10-10T13:55:24Z

Matthew: /* Online Machine Learning Resources */

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]

----

== Online Machine Learning Resources ==
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.

Machine learning

2017-09-12T16:22:24Z

Matthew: /* Machine learning books */

Our Journal Club slides about ML will be posted here.

* [[Media:MLintro.pdf | Machine learning introduction]] - Gunnar
* [[Media:LinearRegression.pdf | Linear Regression]] - Sisi
* [[Media:Linear_Classification.pdf | Linear Classification]] - Parisa
* [[Media:MCMC.pdf | Markov Chain Montre Carlo]] - Gunnar
* [[Media:Intro to PCA and ICA.pdf | Introduction to PCA and ICA]] - Cindy
* [[Media:EM_algorithm.pdf | Expectation Maximization]] - Brandon and Jonathan
* [[Media:HMM-parisa.pdf | Hidden Markov Models]] - Parisa
* [[Media:KalmanFilter.pdf | Kalman filter]] - Josh
* [[Media:ML-ANNs.pdf | Rate-based networks & error back-propagation learning]] - Scott
* [[Media:Support Vector Machines (SVM).pdf | Support Vector Machines]] - Jerry
* [[Media:Deep_Belief_Network_Home_(2).pdf | Deep belief networks ]] - Tiger

----

== Machine learning books ==

* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]

----

== Online Machine Learning Resources ==
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)]
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]
* [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop