Topics in Computer Science: Computational Neuroscience (CSC598,CSC688)

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syllabus

Class Slides, links:
  • Jan 12: Introduction slides: CompneuroIntro_2016.pdf
    • Extra material:
    Dayan and Abbott textbook, Preface.
    Marr's 3 levels: Marr1982.pdf
  • Jan 14: Receptive Fields: ReceptiveFields2016.pdf
    Neural Coding 1: NeuralCoding_2016_1.pdf
    LGN Hubel and Wiesel movie: hw-lgn-400x300.mov
    Cortex V1 Hubel and Wiesel movie: hw-simple-rfs-400x300.mov
    • Extra material:
    Dayan and Abbott Textbook Chapter 1.
  • Jan 19: Neural Coding 2: Population coding: NeuralCoding_2016_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 21: Computer lab: Poisson spike trains Matlab files (including intro to Matlab file): Lab1_Poisson Extra material: Notes on Poisson spiking by David Heeger: David Heeger's Poisson notes
  • Jan 26: Brain Machine Interfaces discussion:
    2006donhogueNature.pdf
    2008schwartzNature.pdf
    Extra reading:
    HochbergBMI2012.pdf
    Extra reading on regression (by Eero Simoncelli and Nathaniel Daw):
    leastSquares.pdf
  • Jan 28: Computer lab: Linear filters and convolution Matlab files: Lab2
  • Feb 2: Spike-triggered (average and covariance) approaches: Compneuro_STC2016.pdf
    Extra reading:
    schwartz06.pdf
    rust05.pdf
  • Feb 4: Computer lab: Spike-triggered Average: Lab3
  • Feb 9: Recent spike-triggered approaches; Neural coding in the song bird: NeuralCoding_3.pdf
    Extra reading:
    Pillow_etal_Nature08
    natureUltraSparse.pdf
    sparseness_reprint.pdf
  • Feb 11: Computer lab: Spike-triggered Covariance: Lab4
  • Feb 16: Natural scenes 1: Efficient coding and information theory: Scenes1_2016.pdf
  • Feb 18: Computer lab: Natural Scenes: Lab_imagesAll
  • Feb 23: Natural scenes 2: Hierarchy and deep networks: Scenes2_2016Class.pdf
    Scenes2_2016Class.pdf
  • Feb 25: Computer lab: Natural Scenes : We continued the previous lab: Lab_imagesAll
  • March 1: Contextual influences in neural processing, perception, and modeling; example of our recent work and note of generative modeling approach: Scenes3_surround2016.pdf
  • March 3: Computer lab: Natural sounds: marginal and joint statistics: WavFilesLab
  • March 8; 10: Spring break.
  • March 15: Discussion: (i) Bhandawat et al. 2007: Sensory processing in the Drosophila antennal lobe increases reliability and separability of ensemble odor representations. (ii) Abbott and Luo 2007: A step toward optimal coding in olfaction.
  • March 17: Computer lab: Project and lab assignment discussions.
  • March 22: Discussion: Krigeskorte 2015: Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.
  • March 24: Computer lab: Introduction to supervised learning: Lab_perceptron
    Lab slides: perceptron_slides.pdf
  • March 29: Introduction to Reinforcement Learning: ReinforcementLearningFinal
  • March 31: Computer lab: Continuation supervised learning; introduction to deep learning.
    Web demo: http://cs.stanford.edu/people/karpathy/convnetjs/index.html
    ipython notebook: https://github.com/google/deepdream/blob/master/dream.ipynb
  • April 5: Discussion: Daw ND, Niv Y, Dayan P (2005). Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat Neurosci. 2005 Dec;8(12):1704-11. (see papers in Class Discussion Papers directory below).
  • April 7: Lab Intro to Reinforcement Learning: Lab_Reinf
  • April 12: Discussion: Eliasmith et al. 2012. A Large-Scale Model of the Functioning Brain. Science 338(6111), 1202-1205.
  • April 14: Lab on content relating to paper discussion: Integrate and Fire: Lab_IntandFire
    Integrate and Fire slides: IntandFire_LablectureFinal.pdf
    IandF_RC_Circuit.pdf
    Raven task code and paper: Lab_RavenTask
  • April 19: Student project presentation. Discussion about the Brain Initiative and computational neuroscience. BRAIN2025_NIH.pdf
  • April 21: Student project presentations.
Class Discussion Papers in this directory: DiscussionPapers

Reading material, extra links, and text books (not required):
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
  • The problem of 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; reinforcement learning
  • 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: Analyzing high dimensional data with Principal Component Analysis; Independent Component Analysis; nonlinear approaches
  • Bayesian approaches
  • Hierarchy in neural systems
  • Machine learning and recent advances; deep learning