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: hw-lgn-400×300.mov
Cortex V1 Hubel and Wiesel movie: hw-simple-rfs-400×300.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:
Extra reading on regression (by Eero Simoncelli and Nathaniel Daw):
- Feb 7: Lab: Spike-triggered average (STA):
- Feb 12: Spike-triggered (average and covariance) approaches: STCnew.pdf
- Assignment 1 (due Tuesday, March 5): CompNeuro2019_midassignment.pdf
- Feb 14: Computer lab: Spike-triggered 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
- 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 Large-Scale Model of the Functioning Brain: eliasmith2012.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 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, spike-triggered 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