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Lecture Notes and Readings

Page history last edited by baccus@... 4 years, 2 months ago


Week 1:

Lectures
Lecturer
Reading
Lecture Notes
Day 1 – Math/Biophysics
Biophysics bootcamp
M. Fee
http://goldmanlab.faculty.ucdavis.edu/tutorials/
MCN Biophysics lecture 2016.ppt
Linear Algebra & Diff Eq & SVD Bootcamp
M. Goldman
Linear algebra for feedforward systems
LinearAlgebra_2016updated.ppt
Goldman_DiffEqns_MCNTutorial.pdf
Goldman_PCA_MCNTutorial.pdf
MATLAB tutorial
TAs
 
MATLABtutorial.zip
Day 2 – Math/Biophysics
Nonlinear dynamics and bifurcations
B. Ermentrout
 
 
Ion channels, conductance-based models
J. Huguenard
 
mbl_mcn_2016_John.ppt
Passive cable theory
M. Fee
 
Passive cable theory lecture 2016.ppt
XPP & channels tutorial
B. Ermentrout
XPPtutorial2017.zip
odes.zip
 
Backyard Brains
G. Gage
 
 



Week 2:

Lectures
Lecturer
Reading
Lecture Notes
Day 3 – Biophysics
Active Dendrites
B. Mel
 
Mel - 16. Woods Hole Lecture.pdf
Probability, info measures, latent variables tutorial
U. Eden
 
Probability intro: PPT, pdf
Day 4 – Coding
Intro to Coding, Adaptation & Biophysics
A. Fairhall
ALFK_Neuron_Primer2016.pdf
Fairhall - MCN 2016.pdf
Retinal Coding & Circuitry with Synapse Dynamics
S. Baccus
Hennig 2013 Frontiers Comp Neuro.pdf
Jadzinsky Baccus 13 visual transformations.pdf
Kastner Baccus 11 coordinated encoding.pdf
Kastner Baccus 14 computation review.pdf
Kastner Baccus 2013 Predictive sensitization.pdf
ozuysal baccus 12 LNK adaptation model.pdf
Baccus MCN 2016.pptx
Generalized Linear Models
U. Eden
 
Generalized linear models: PPT, pdf
Day 5 – Statistical learning, data analysis
Tutorial Classifiers & Probablistic Data Analysis
S. Solla
 
SOLLA Lecture1 MCN 2016.pdf
High-Dimensional Statistics with Compressed Sensing
S. Ganguli
 
Ganguli_16.08.Woodshole.pdf
Unified Framework for Machine Learning & Statistics
S. Ganguli
SuryaNotes.pdf
 
Day 6 – Statistical learning, data analysis
Analyzing Brain Wide Recording During Behavior
J. Fitzgerald
 
2016-08-03-WholeBrainMCN_v2.pdf
Tempotron
H. Sompolinsky
Gutig R, Sompolinsky H (2006). The tempotron: a neuron that learns spike timing-based decisions. Nature Neuroscience

Rubin R, Monasson R, and Sompolinsky H (2010). Theory of spike timing-based neural classifiers. Physical review letters

Gütig R, Sompolinsky H (2009).Time-warp-invariant neuronal processing

Tempotron_References_List
 
Linear Network Theory Tutorial
M. Goldman
Written notes: Integrators and linear network theory
Slides: Linear network theory, nonlinear networks and integration
Day 7 – Circuits
Evidence Accumulation
C. Brody
 
Lecture slides
Nonlinear Networks & Neural Integration
M. Goldman
Fisher et al 2013 - Integrator modeling framework
Lim and Goldman 2013 - Balanced microcircuitry
(slides continue in ppt file from previous lecture)
Hippocampal & EC Circuitry
L. Frank
Awake replay review
Hippocampus lecture


Week 3:

Lectures
Lecturer
Reading
Lecture Notes
Day 8 – Hippocampal coding & circuits
Grid Cell Experiments
I. Fiete
 
Lecture_Slides
Grid Cells Dynamics and Coding
I. Fiete
 
Lecture_Slides
Day 9 – Oscillations
Multi-Taper Spectral Analysis
E. Brown
Babadi and Brown, IEEE, TBME 2014 - contains the principal technical material on spectral analysis of oscillations

Purdon et al. PNAS 2013 and Cimenser et al. PNAS 2011 - contain examples to discuss (alpha oscillations).
Cornelissen et al. eLife 2015 - contains example to discuss (bootstrap).
Purdon et al. Anesthesiology, 2015 is a general overview of oscillation associated with anesthetic drugs.
Some of the modeling work for the alpha oscillation that Nancy will cover is in Ching et al. PNAS 2010.
Slides: Spectral Analysis

Notes on Bootstrap Method
Circuit Mechanisms and Function of Oscillations
N. Kopell
 
Lecture Slides
Central Pattern Generation
E. Marder
 
Lecture_Slides
Day 10 – Cortical circuits; learning
Balanced Networks
H. Sompolinsky
Lecture_Notes
 
Intro to Learning Theory
S. Solla
 
Slides: Learning theory
Day 11 – Plasticity, predictive coding
Hebbian & Homeostatic Chalk Talk
K. Miller

- erwin-miller98-ori-and-ocdom-devel.pdf
kaschube-etal-wolf10-science.pdf
kdm-lecture-notes-on-wolf-kaschube-analysis.pdf

kdm-news-and-views-on-kaschube-etal10.pdf
miller-mackay94-constraints.pdf
miller96-dev-models-review.pdf

Lecture Notes
Slides
Predictive Coding Framework for Dynamical Systems
S. Deneve
 
Lecture_Slides
Day 12 – Reinforcement learning
FORCE learning
L. Abbott
 
Abbott_WH16a.pdf
Topic of Choice
L. Abbott
 
Abbott_WH16b.pdf



Week 4:

Lectures
Lecturer
Reading
Lecture Notes
Day 13 – Motor/Reinforcement Learning
Reinforcement Learning
P. Dayan
 
Lecture notes - reinforcement learning
Motor Control
D. Wolpert
Wolpert et al 2011, NRN
Orban & Wolpert 2011 CONB
Motor control
Day 14 – Basal ganglia learning/Dynamics
Birdsong dynamics & learning
M. Fee
 
Fee MCN 2016 Learning.pdf
Fee MCN 2016 Sequence Generation.pdf
Day 15 – Natural images, higher coding
Density Models of Natural Images & Implications
E. Simoncelli
 
Slides: Image statistics and efficient sensory coding
Cognitive Mapping
J. Gallant
 
Lectures slides
Day 16 – Principles of learning
Cognitive Learning, Hierarchical Bayes Models
J. Tenenbaum
 
Lectures slides:
PPT (part 1part 2)
PDF
Deriving Single Neuron Function & Plasticity
D. Chklovskii
PehlevanHuChklovskii15
PehlevanChklovskii14
PehlevanChklovskii15
Lecture slides
Day 17 – Cortical Microcircuitry
Cortical Interneurons
M. Geffen
Aizenberg 2015
Natan 2015
Lecture slides
Pedagogical Role of Interneurons
T. Sejnowski
Jadi & Sejnowski - Regulating Cortical Oscillations 2014
Jadi & Sejnowski - Cortical Oscillations Arise from Contextual Interactions 2014
Lecture slides 

 

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