Syllabus: SyllabusCompNeuro_Spring2021.pdf
Extra material:
Dayan and Abbott textbook, Preface.
Marr's 3 levels: Marr1982.pdf
Class Slides, links, tutorial code:
- Jan 26: Introduction slides: CompneuroIntro_2021.pdf
Extra material:
Dayan and Abbott textbook, Preface.
Marr's 3 levels: Marr1982.pdf
- Jan 28: Computer lab: Intro Matlab tutorial: matlabIntroShort.m in MatlabFiles directory
- Feb 2:
Neural Coding 1: NeuralCoding_2021_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.
- Feb 4: Computer lab: Individual lab files: Lab1_Poisson
Full tar with all lab files: Lab1_Poisson.tar.gz
Slides: Poisson_lab_intro.pdf
Extra material:
Notes on Poisson spiking by David Heeger: David Heeger's Poisson notes
- Feb 9 Neural Coding 2: Population coding: NeuralCoding_2021_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
- Feb 11: Computer lab: Spike-triggered Average:
Full tar with all lab files: Lab2_STA.tar.gz
Individual lab files: Lab2_STA
- Feb 16: Spike-triggered (average and covariance) approaches: STCnew.pdf
Extra (optional) reading:
schwartz06.pdf
- Feb 17: Computer lab: Spike-triggered Average
continued from last time.
Start Spike-triggered Covariance:
Lab4_STC.tar.gz
- Feb 23:
Natural scenes and cortical visual processing. Scenes1.pdf
- Feb 25:
These are the individual files for the STC lab 4 we did. I've added a function mean2, which should solve that issue for those who did not have it built in.
Lab4 STC Individual files
- March 2: Natural scenes 2: Hierarchy and intro to deep networks:
Scenes2_2021.pdf
- March 5: Computer lab: Natural sounds statistics and separating speech sounds:
WavFilesLab
- March 9: Discussion: Kriegeskorte 2015: Deep Neural Networks:
A New Framework for Modeling Biological Vision and Brain Information Processing:
Kriegeskorte2015.pdf
Kriegeskorte_discussion.pdf
- March 11: Computer lab: Neural networks 1 (Perceptron):
Individual Matlab files: Perceptron_Matlabfiles
Tar file of all Matlab files: PerceptronMatlab.tar.gz
Slides: Perceptron_slides.pdf
- March 16: Spatial context, salience, and eye movements:
Salience_Eyemovements2021.pdf
The slides include one movie file from Laurent Itti's lab:
natScenes.mpg
- March 18: Lab on deep convolutional neural networks. Google colab notebook by Md Nasir Uddin Laskar:
cnn_fashion_keras.ipynb
Please save a copy (to your drive) before using the notebook.
- March 23: Introduction to Reinforcement Learning:
ReinforcementLearningFinal2021
- March 25: Lab Intro to Reinforcement Learning:
ReinfLab.tar.gz
reinfLab2021.m
reinfFull2021.m
- March 30: Talk on fMRI approaches. Guest lecturer: Dr. Jason S. Nomi
- April 1: Hippocampus modeling. Guest lecturer: Xu Pan
- April 6: Discussion on A Large-Scale Model of the
Functioning Brain:
eliasmith2012.pdf
EliasmithDiscussion.pdf
- April 8:
This lab relates to the Eliasmith paper discussion we had.
Integrate and Fire lab:
intAndFireTutorial.m
IntandFire_Lablecture2021.pdf
- April 13: Brain Machine Interfaces discussion:
BMI_2021.pdf
Papers I will go through:
2006donhogueNature.pdf
2008schwartzNature.pdf
HochbergBMI2012.pdf
Extra reading on regression (by Eero Simoncelli and Nathaniel Daw):
leastSquares.pdf
Extra videos:
BMI_Video1
BMI_Video2
- April 15:
This lab relates to the Eliasmith paper discussion we had on memory binding.
Tar file of Raven task code and paper, including some intro code that I made a while ago for binding and unbinding into memory:
Lab_RavenTask.tar.gz
Individual files of Raven task code and paper, including some intro code that I made a while ago for binding and unbinding into memory:
Lab_RavenTask
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
- 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
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