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LECTURE VIDEOS ARE HERE:
Week 1:
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| Lectures |
Lecturer |
Reading |
Lecture Notes |
| Day 1 – Math/Biophysics |
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| Biophysics bootcamp |
M. Fee |
http://goldmanlab.faculty.ucdavis.edu/tutorials/ |
MCN Biophysics lecture 2017.pptx
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| Linear Algebra & Diff Eq & SVD Bootcamp |
M. Goldman |
Linear algebra for feedforward systems |
LinearAlgebra_MCN2017_3.pptx |
| MATLAB tutorial |
TAs |
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MATLABtutorial.zip |
| Day 2 – Math/Biophysics |
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| Nonlinear dynamics and bifurcations |
B. Ermentrout |
XPPtutorial2017.zip |
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| Ion channels, conductance-based models |
D. Johnston |
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MCN-2.pdf
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| Passive cable theory |
M. Fee |
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Passive cable theory lecture 2017.pptx
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| XPP & channels tutorial |
B. Ermentrout |
XPPtutorial2017.zip
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odes.zip
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| Backyard Brains |
G. Gage |
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Slides |
| Day 3 – Biophysics/Coding |
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| Active Dendrites |
B. Mel |
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17. Mel Lecture.pdf
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| Probability, info measures, latent variables tutorial |
U. Eden |
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Probability Intro.pptx
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| Intro to Coding, Adaptation & Biophysics |
A. Fairhall |
Analysis of Neuronal Spike Trains |
MCN 2017 Fairhall.pptx
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| Day 4 – Coding/Probabilistic Data Analysis |
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| Retinal Coding & Circuitry with Synapse Dynamics |
S. Baccus |
hennig 2013 short term plasticity models.pdf
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Baccus MCN 2017.pptx
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ozuysal baccus 12 LNK adaptation model.pdf
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McIntosh Maheswaranathan 2016 deep retina.pdf
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Baccus 06 auditory adaptation.pdf
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van hateren 93 early vision.pdf
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| Generalized Linear Models |
J. Pillow |
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pillow_GLMs_MCN2017.pdf
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| Tutorial Classifiers & Probabilistic Data Analysis |
S. Solla |
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SOLLA Lecture1 MCN2017 0803.pdf
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| Day 5 – Statistical learning, data analysis |
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| High-Dimensional Statistics with Compressed Sensing |
S. Ganguli |
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17.08.Woodshole.pdf
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| Unified Framework for Machine Learning & Statistics |
S. Ganguli |
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SuryaNotes.pdf
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| Week 2: |
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| Lectures |
Lecturer |
Reading |
Lecture Notes |
| Day 6 – Circuits |
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| Linear Network Theory Tutorial |
M. Goldman |
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MCNTalk_LinearNetTheory&Integration17.pptx
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| Evidence Accumulation |
C. Brody |
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t170807-MBL-accum_of_evidence_PWM.pdf
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| Nonlinear Networks & Neural Integration |
M. Goldman |
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MCNTalk_LinearNetTheory&Integration17.pptx
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| Day 7 – Hippocampal coding and circuits |
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| Hippocampal & EC Circuitry |
L. Frank |
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FrankWoodsHole2017.pdf
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| Grid Cell Experiments |
I. Fiete |
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| Grid Cells Dynamics and Coding |
I. Fiete |
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| Day 8 – Circuits |
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| Balanced Networks |
H. Sompolinsky |
hs_les houches_chapter-9.pdf
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| 2. Balanced dynamics in realistic models of cortical circuits:Landau, Itamar D., et al. "The impact of structural heterogeneity on excitation-inhibition balance in cortical networks." Neuron 92.5 (2016): 1106-1121.3. Emergence of E-I balance from robust learningRubin, Ran, L. F. Abbott, and Haim Sompolinsky. "Balanced Excitation and Inhibition are Required for High-Capacity, Noise-Robust Neuronal Selectivity." arXiv preprint arXiv:1705.01502(2017). |
barral_nns_2016_synaptic_scaling_EI_alance.pdf
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| Neural Manifolds and Invariant Perception |
H. Sompolinsky |
Some theoretical results on simple perceptual manifolds: Chung, SueYeon, Daniel D. Lee, and Haim Sompolinsky. "Linear readout of object manifolds." Physical Review E 93.6 (2016): 060301. |
| Day 9 – Learning |
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| Intro to Learning Theory |
S. Solla |
TishbyLevinSolla89.pdf
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SOLLA Lecture2 MCN2017 0810.pdf
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TishbyLevinSolla90.pdf
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BaldiHornik89.pdf
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HintonSalakhudtkinov2006.pdf
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TenenbaumSilvaLangford00.pdf
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GallegoPerichMillerSolla17.pdf
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| FORCE learning |
L. Abbott |
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WH17(1).pdf
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| Topic of Choice |
L. Abbott |
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WH17(2).pdf
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| Day 10 – Plasticity |
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| Plasticity |
S. Fusi |
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WoodsHole2017_final90minutes.ppt
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| Intrinsic Plasticity |
C. Savin |
Savin2010ICA.pdf
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MCN-IP.pdf |
Savin2014Memory.pdf
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| Normative derivation for IP, Monk et al, 2016 and Suppl.Info. |
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neurocentricmemory.pdf
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| Week 3: |
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| Lectures |
Lecturer |
Reading |
Lecture Notes |
| Day 11 – Plasticity, Predictive Coding |
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| Adaptive Nonlinear Control and Dynamic System Identification |
J. J. Slotine |
Gaussian networks for direct adaptive control |
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| On the Adaptive Control of Robot Manipulators |
| Applied nonlinear control |
| A spiking neural model of adaptive arm control |
| Learning arbitrary dynamics in efficient, balanced spiking networks using local plasticity rules |
| Lecture Series |
| Reinforcement Learning |
J. Berke |
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| Day 12 – Reinforcement Learning |
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| Reinforcement Learning |
M. Lengyel |
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reinflearn.pdf
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| Cognitive Learning, Hierarchical Bayes Models |
J. Tenenbaum |
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| Day 13 – Motor/Reinforcement Learning |
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| Birdsong dynamics & learning |
M. Fee |
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| Day 14 – Higher Cognitive Models/Bridging Scales |
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| Cognitive Mapping |
J. Gallant |
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| Deriving Single Neuron Function & Plasticity |
D. Chklovskii |
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WoodsHole17.pdf
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| Day 15 – Large-Scale Modeling |
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| Large-Scale Cortical Modeling |
X. J. Wang |
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mcn2017_xjwang.pptx
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| Whole Brain Modeling |
C. Eliasmith |
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SPA overview woods hole 2017.pdf
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| Week 4: |
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| Lectures |
Lecturer |
Reading |
Lecture Notes |
| Day 16 – Perspectives on Computational Neuroscience |
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| From Bedside to Computational Neuroscience |
E. Brown |
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| Topic of Choice (Parkinson's) |
N. Kopell |
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woodshole-PD17-8-19-F.ppt
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| Day 17 – Perspectives on Computational Neuroscience |
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| Topic of Choice |
E. Marder |
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MCNatMBL 22Aug 2017 final for Wiki.pptx
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| Topic of Choice (Sleep Spindles/Memory Consolidation) |
T. Sejnowski |
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spikes.huh.MBL.17.pdf
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