Also see Tutorials for notes from the initial bootcamp lectures.
When adding files, try to insert a link into the table using the link button rather than the file button (creates a bulky box instead of nice link).
First you'll need to upload the file, using the file button. Instead of selecting your new file(s), close the upload window. Use the link button to insert a link to your file - the "Page Name" box will find your file by filename.

Week 1:
Lecture Notes
Day 1 – Math/Biophysics
Biophysics bootcamp
M. Fee
Linear Algebra & Diff Eq & SVD Bootcamp
M. Goldman
Linear algebra for feedforward systems
Linear algebra slides
Differential equation notes
Principal component analysis (PCA) notes
MATLAB tutorial
Day 2 – Math/Biophysics
Nonlinear dynamics and bifurcations
B. Ermentrout

Ion channels, conductance-based models
J. Huguenard

Passive cable theory
M. Fee

XPP & channels tutorial
B. Ermentrout
XPP tutorial

Backyard Brains
G. Gage

Week 2:
Lecture Notes
Day 3 – Biophysics
Active Dendrites
B. Mel

Slides: Spatial Dendritic Computation
Probability, info measures, latent variables tutorial
U. Eden

Probability intro: PPT, PDF
Day 4 – Coding
Intro to Coding, Adaptation & Biophysics
A. Fairhall
Analysis of Neuronal Spike Trains - 2016 Neuron
Slides: Neural coding and adaptation
Retinal Coding & Circuitry with Synapse Dynamics
S. Baccus
Hennig 2013 Frontiers Comp Neuro
Jadzinsky Baccus 13 visual transformations
Kastner Baccus 11 coordinated encoding
Kastner Baccus 14 computation review
Kastner Baccus 2013 Predictive sensitization
ozuysal baccus 12 LNK adaptation model
Slides: Adaptation and Prediction from Synaptic Dynamics in Retinal Circuits
Generalized Linear Models
U. Eden

Generalized linear models: PPT, PDF
Day 5 – Statistical learning, data analysis
Tutorial Classifiers & Probablistic Data Analysis
S. Solla

Slides: Probabilistic data analysis and classification
High-Dimensional Statistics with Compressed Sensing
S. Ganguli

Lecture slides
Unified Framework for Machine Learning & Statistics
S. Ganguli
Notes from Surya's lecture on matrix factorization perspectives

Day 6 – Statistical learning, data analysis
Analyzing Brain Wide Recording During Behavior
J. Fitzgerald

From whole-brain data to functional circuit models
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

Gütig 2016: A novel approach to the tempotron problem


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 pdf

Week 3:
Lecture Notes
Day 8 – Hippocampal coding & circuits
Grid Cell Experiments
I. Fiete

Grid Cells Dynamics and Coding
I. Fiete

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

Day 10 – Cortical circuits; learning
Balanced Networks
H. Sompolinsky

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
Predictive Coding Framework for Dynamical Systems
S. Deneve

Day 12 – Reinforcement learning
FORCE learning
L. Abbott

Topic of Choice
L. Abbott


Week 4:
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 1, part 2)
Deriving Single Neuron Function & Plasticity
D. Chklovskii
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