NOTE: From March 24, we will have classes on Zoom at the regular class time.
I have sent out an email with the link to the zoom location. Notes and
links to the code will be posted on the class website as usual.
Class Slides, links, tutorial code:
- Jan 14: Introduction slides: CompneuroIntro_2020.pdf
Extra material:
Dayan and Abbott textbook, Preface.
Marr's 3 levels: Marr1982.pdf
- Jan 16: Computer lab: Intro Matlab tutorial: matlabIntroShort.m in MatlabFiles directory
- Jan 22:
Neural Coding 1: NeuralCoding_2020_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 23: Computer lab: Lab1_Poisson
Extra material:
Notes on Poisson spiking by David Heeger: David Heeger's Poisson notes
- Jan 28 Neural Coding 2: Population coding: NeuralCoding_2020_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 30: Lab: Spike-triggered average (STA):
Lab2_STA
- Feb 4: Spike-triggered (average and covariance) approaches: STCnew.pdf
Extra reading:
schwartz06.pdf
- Feb 6: Computer lab: Spike-triggered Covariance: Lab4
- Feb 11:
Natural scenes and cortical visual processing. Scenes1.pdf
Assignment 1 (now due Thursday, March 26; Note NEW change due to extended Spring Break):
CompNeuro2020_midassignment.pdf
- Feb 13: Computer lab: Natural sounds statistics and separating speech sounds:
WavFilesLab
- February 18: Natural scenes 2: Hierarchy and intro to deep networks:
Scenes2_2020Class.pdf
- February 20: Computer lab: Natural Scenes: Lab_imagesAll
- February 25: Discussion: Kriegeskorte 2015: Deep Neural Networks:
A New Framework for Modeling Biological Vision and Brain Information Processing:
Kriegeskorte2015.pdf
Kriegeskorte_discussion.pdf
- February 27: Discuss assignment, and initial discussion on class projects.
- March 3: Talk on fMRI approaches. Guest lecturer: Dr. Jason S. Nomi:
Nomi_CStalk_3_3_20.pdf
- March 5: Hippocampus modeling. Guest lecturer: Xu Pan
- March 10; 12; 17; 19. Extended Spring break.
- March 24: Spatial context, salience, and eye movements:
Salience_Eyemovements2020.pdf
The slides include one movie file from Laurent Itti's lab:
natScenes.mpg
- March 26: Computer lab: Neural networks 1 (Perceptron): Perceptron
Perceptron_2020.pdf
- April 2: Introduction to Reinforcement Learning:
ReinforcementLearningFinal2020
- April 2: 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.
- April 7: Brain Machine Interfaces discussion:
BMI_2020_class.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 9: Lab Intro to Reinforcement Learning:
LabReinf
ReinforcementLearningLab
- April 14: Discussion on A Large-Scale Model of the
Functioning Brain:
eliasmith2012.pdf
EliasmithDiscussion.pdf
- April 16:
This lab relates to the Eliasmith paper discussion we had.
Integrate and Fire lab:
Lab_IntandFire
IntandFire_Lablecture2020.pdf
Extra material (optional; not required in the lab):
Raven task code and paper, including some intro code that I made a while ago for binding and unbinding into memory:
Lab_RavenTask
- April 21: Guest lecture: Prof Liz Losin, University of Miami, Psychology.
Losin_lecture.pdf
Losin_et_al-2020.pdf
Geuter_et_al-2020.pdf
MediationToolbox
- April 23: Lab. Discuss final projects.
- April 28: Hierarchical processing in olfaction, and in the songbird.
OlfactDiscuss_2020Class.pdf
- April 30: Summary, future directions, discussion.
CompneuroFinalDiscussion_2020.pdf
Upcoming:
- May 5: Presentation of final projects.
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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