Computational Neuroscience (CSC598,CSC688)
Class Slides, links, tutorial code:
 Jan 15: Introduction slides: CompneuroIntro_2019.pdf

 Extra material:

 Dayan and Abbott textbook, Preface.
 Jan 17: Computer lab: Intro Matlab tutorial: matlabIntroShort.m in MatlabFiles directory
Marr’s 3 levels: Marr1982.pdf  Jan 22: Receptive Fields: ReceptiveFields2019.pdf
Neural Coding 1: NeuralCoding_2019_1.pdf
LGN Hubel and Wiesel movie: hwlgn400×300.mov
Cortex V1 Hubel and Wiesel movie: hwsimplerfs400×300.mov
Extra material: Dayan and Abbott Textbook Chapter 1.  Jan 24: Computer lab: : Lab1_Poisson
Extra material: Notes on Poisson spiking by David Heeger: David Heeger’s Poisson notes  Jan 29 Neural Coding 2: Population coding: NeuralCoding_2019_2.pdf
Extra material for learning further:
Dayan and Abbott Textbook Chapter 3.
Geometric view of linear algebra by Eero Simoncelli: Eero Simoncelli’s geometric Linear Algebra  Jan 31: Computer lab: Linear filters and convolution Matlab files: convolution_tutorial_part1_solved.mconvolution_tutorial_part2_solved.m
 Feb 5: Brain Machine Interfaces discussion:
BMI_2019.pdf
Papers:
2006donhogueNature.pdf
2008schwartzNature.pdf
Extra reading:
HochbergBMI2012.pdf
Extra reading on regression (by Eero Simoncelli and Nathaniel Daw):
leastSquares.pdf
Extra videos:
BMI_Video1
BMI_Video2  Feb 7: Lab: Spiketriggered average (STA):
Lab_STA  Feb 12: Spiketriggered (average and covariance) approaches: STCnew.pdf
Extra reading:
schwartz06.pdf
 Assignment 1 (due Tuesday, March 5): CompNeuro2019_midassignment.pdf
 Feb 14: Computer lab: Spiketriggered Covariance: Lab4
 Feb 19: Talk and demo on deep neural networks. Guest lecturer: Dr. Luis Gonzalo Sanchez Giraldo.
 Feb 21: Talk on fMRI approaches. Guest lecturer: Dr. Jason S. Nomi: fMRI_talk.pdf
 Feb 26: Natural scenes and cortical visual processing. Scenes1_2019.pdf
 Feb 28: Computer lab: Natural sounds: marginal and joint statistics: WavFilesLab
 March 5: Natural scenes 2: Hierarchy and deep networks: Scenes2_2019Class.pdf
 March 7: Computer lab: Natural Scenes: Lab_imagesAll
 March 19: Discussion: Kriegeskorte 2015: Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing: Kriegeskorte2015.pdf
Kriegeskorte_discussion2019.pdf  March 21: Computer lab: Neural networks 1 (Perceptron): perceptron_slides2019
 March 26: Discussion: (i) Bhandawat et al. 2007: Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations: BhandawatWilson2007.pdf
(ii) Abbott and Luo 2007: A step toward optimal coding in olfaction: Abbott2007.pdf  March 28: Lab on deep convolutional neural networks. Google colab notebook by Md Nasir Uddin Laskar: cnn_fashion_keras.ipynb
Please save a copy before using the notebook.  April 2: Introduction to Reinforcement Learning: ReinforcementLearningFinal2019
 April 4: No class: Neural Engineering Symposium.
 April 9: Spatial context, salience, and eye movements: Salience_Eyemovements2019.pdf
 April 11: Lab Intro to Reinforcement Learning: LabReinf2019
Mainly discussed group projects.  April 16: Discussion on A LargeScale Model of the Functioning Brain: eliasmith2012.pdf
EliasmithDiscussion.pdf
Extra material (optional; we won’t have time to do): Raven task code and paper: Lab_RavenTask
Integrate and Fire: Lab_IntandFire  April 18: Guest lecture by Prof Abhishek Prasad. In Dooly classroom.
 April 23; 25: Final group presentations. In Dooly classroom.Reading material, extra links, and textbooks (not required):
 Suggested textbook in the field: Dayan and Abbott: Theoretical Neuroscience.
 Natural Image Statistics book: Hyvarinen book on Natural Image Statistics.
 Matlab references:
Matlab Primer
Matlab help from Mathworks
Matlab tutorial from Mathworks  Math notes: Eero Simoncelli’s Linear algebra geometric review
 Neuroscience textbooks:
Principles of Neuroscience by Kandel
Instant Notes in Neuroscience
Matlab for Neuroscientists
Topics covered: The course will include some main topic areas in computational neuroscience, along with computational tools for modeling and analyzing neural systems. This will be complemented by some Matlab computer tutorials and labs.
 What do we want to know about the brain, and how can computation help?
 Types of neural modeling: What (Descriptive), How (Mechanistic), Why (Interpretive)
 Levels of modeling and biological data: micro to macro: from single neurons, to circuits and systems, to perception and behavior
 Neural coding
 Neural population Coding
 Brain Machine Interfaces
 Information theory and neural coding
 Example neural system: The visual system
 Other example neural systems: Motor; olfaction in the fly; songbird learning; attention; memory
 Estimating descriptive neural models from data: regression, spiketriggered covariance
 Spike Train models
 Neural circuit models
 Neural processing of natural stimuli
 Finding correlations and higher order dependencies in data with unsupervised learning: Analyzing high dimensional data with Principal Component Analysis; Independent Component Analysis; nonlinear approaches
 Hierarchy in neural systems
 Recent advances in machine learning and deep learning
 Reinforcement learning