Class Slides, links:
- Aug 22: Introduction slides: CompneuroIntro_2017.pdf
Extra material:
Dayan and Abbott textbook, Preface.
Marr's 3 levels: Marr1982.pdf
- Aug 24: Computer lab: Intro Matlab tutorial: matlabIntroShort.m in MatlabFiles directory
- Aug 29: Receptive Fields: ReceptiveFields2017.pdf
Neural Coding 1: NeuralCoding_2017_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.
- Aug 31: Computer lab: : Lab1_Poisson
- Sept 5 Neural Coding 2: Population coding: NeuralCoding_2017_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
- Sept 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
Extra videos:
BMI_Video1
BMI_Video2
- Sept 28: Computer lab: Linear filters and convolution Matlab files: Lab2
Extra note on unrolling recursion and making convolution explicit (by Luis Gonzalo Sanchez Giraldo):
unrolling_recursion_Lab2.pdf
- October 3: Spike-triggered (average and covariance) approaches: STCnew.pdf
Extra reading:
schwartz06.pdf
rust05.pdf
- Oct 5: Computer lab: Spike-triggered Average: Lab3
- Oct 10: Recent spike-triggered approaches; Neural coding in the song bird: NeuralCoding_3_2017.pdf
Extra reading:
Pillow_etal_Nature08
natureUltraSparse.pdf
sparseness_reprint.pdf
- October 12: Computer lab: Spike-triggered Covariance: Lab4
Assignment 1 (due Thursday, November 2; Extension: now due November 9):
CompNeuro2017_assign1.pdf
- October 17: Natural scenes 1: Efficient coding and information theory:
Scenes1_2017.pdf
- October 19: Computer lab: Natural Scenes: Lab_imagesAll
- October 24: Natural scenes 2: Hierarchy and deep networks:
Scenes2_2017Class.pdf
- October 26: Computer lab: Sound mixing: WavFilesLab
- October 31: Discussion: Kriegeskorte 2015: Deep Neural Networks:
A New Framework for Modeling Biological Vision and Brain Information Processing:
Kriegeskorte2015.pdf
Kriegeskorte_discussion2017.pdf
- November 2: Computer lab: Neural networks 1 (Perceptron): Perceptron2017
- November 7: 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
- November 9: Continuation of Neural Networks (cross_entropy_demo) and project group discussions. Perceptron2017
- November 14: Introduction to Reinforcement Learning:
ReinforcementLearningFinal2017
- November 16: Computer lab: Deep neural networks: NNET_matlab
- November 28: Discussion on Reinforcement Learning:
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.
dawnivd2005.pdf
See also recent alphaGo papers:
Silver2017.pdf;
Silver2016.pdf
- November 30: Lab Intro to Reinforcement Learning:
LabReinf2017
- December 5: Eye movements and salience
- December 7: Lab: Project group discussions.
- December 12: Discussion on A Large-Scale Model of the
Functioning Brain:
eliasmith2012.pdf
EliasmithDiscussion.pdf
CompneuroFinalDiscussion_2017.pdf
Extra material (optional):
Raven task code and paper:
Lab_RavenTask
Integrate and Fire:
Lab_IntandFire
- December 14: no class (reading day)
- December 19: Project presentations.
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
|