NeuromatchAcademy/course-content
Summer course content for Neuromatch Academy
| repo name | NeuromatchAcademy/course-content |
| repo link | https://github.com/NeuromatchAcademy/course-content |
| homepage | |
| language | Jupyter Notebook |
| size (curr.) | 10120 kB |
| stars (curr.) | 226 |
| created | 2020-05-10 |
| license | Creative Commons Attribution 4.0 International |
NeuroMatch Academy (NMA) syllabus
July 13-31, 2020
Objectives: Introduce traditional and emerging computational neuroscience tools, their complementarity, and what they can tell us about the brain. A main focus is on modeling choices, model creation, model evaluation and understanding how they relate to biological questions.
Tutorial microstructure: ~10min talk, ~20min tutorial (repeated)
Day structure: Opening keynote, 3h lecture/tutorial modules, 1h interpretation (what did we learn today, what does it mean, underlying philosophy, 1h professional development/ meta-science, evening group projects (for interactive track). There will also be many networking activities!
Prerequisites: See here
Course outline
Week 1
Mon, July 13: Model Types
Description Introduce different example model types (Marr 1-3, what/how/why) and the kinds of questions they can answer. Realize how different models map onto different datasets.
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | Model classifications |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | Marr 1-3 |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | “What”/“How”/“Why” |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, The role of models in discovery |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Tue, July 14: Modeling Practice
Description Introduction of datasets (spikes, EEG, fMRI + behavior), and questions about them. These questions will foreshadow the whole summer school.
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | NMA organization, expectations, code of conduct, modeling vs. data |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | Data into, preprocessing, link of neural data to behavior |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Tuning (RFs, motor, STA), What is means to “understand” (signal detection) |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Wed, July 15: Model fitting
Description Fit models to data, quantify uncertainty, compare models
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | Why and how to fit models |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | Fit a model 1 (linear regression), Get error bars |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Compare models, cross-validation, hyperparameters, Fit a model 2 (nonlinear models) |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, Critical evaluation of model fitting |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Thu, July 16: Machine Learning
Description Introduction to machine learning. The commonly used approaches, how to avoid false positives, how to do it well
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | We want to predict (scikit learn) |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | GLMs (temporal filtering models), Linear classifier (SVM) |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Regularization (L1, L2), Shallow nonlinear classifier (SVM with RBF kernel) |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, Promises and pitfalls of ML |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Fri, July 17: Dimensionality Reduction
Description Concept of dimensionality reduction, ways of doing it, what it means
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | Manifolds to understand |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | PCA 1, PCA 2 (+CCA/clustering) |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Signal vs. Noise Manifolds, Visualizing high-D nonlinear manifolds (e.g. t-SNE) |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, The link between high-dimensional brain signals and low-dimensional behavior |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Sat/Sun, July 18/19: Professional development & Social
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times TBA
Week 2
Mon, July 20: Bayesian Statistics
Description Bayesian statistics, modeling of behavior, modeling of neural data, quantifying information
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | Uncertainty |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | Bayes rule I (product rule: cue combination), Bayes rule II (Marginalization and nuisance variables) |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Causal inference & structural models (use as example for marginalization), Bayesian decision theory |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, Advanced Bayesian methods |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Tue, July 21: Linear Systems
Description How to make estimates over time, how the brain does it
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | World has time |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorials 1 & 2 + nano-lectures | Linear systems theory I (ND deterministic), Linear systems theory II (1D stochastic = OU process; ND stocastic = AR(1)) |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 & 4 + nano-lectures | Markov process, State space model |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, Linear systems rule the world |
| 5:35-6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Wed, July 22: Decision Making
Description How we can make decisions when information comes in over time
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | We need to decide stuff |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Information theory, Sequential Probability Ratio Test (SPRT) |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Hidden Markov Model inference (DDM), Kalman filter |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Decisions, decisions, decisions … |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Thu, July 23: Optimal Control
Description We need to move gain info and reach goals
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | We want to control our actions… |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Expected utility / Cost, Markov decision process (MDP) |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | LQG control (MDP for linear systems), Motor control (signal-dependent noise, time cost, …) |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Advanced motor control |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Fri, July 24: Reinforcement Learning
Description The setting of reinforcement learning and how it approximates the real world, behavior, and potential brain implementations
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | Problem formulations: actor-critic |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Critic: reward prediction error, Exploration (POMDP) vs exploitation |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Model-based vs model-free RL, Multi-arm bandits: foraging |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, RL and the brain |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Sat/Sun, July 24/25: Professional development & Social
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times TBA
Week 3
Mon, July 27: Real Neurons
Description The things neurons are made of, channels, morphologies, neuromodulators, and plasticity
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | Real neurons ftw |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Channels, HH, LIF neuron |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | LNP (loses fine timing info), Hebbian plasticity & STDP |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, A variety of neuron models |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Tue, July 28: Dynamic Networks
Description How single neurons create population dynamics
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | Mechanistic models of different types of brain actvivity |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Spikes to rates, Wilson-Cowen model (coarse-grained), oscillations & synchrony |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Attractors & local linearization around fixed points, Chaos in rate networks (stimulus dependent chaotic attractor) |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, A theory of the whole brain |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Wed, July 29: Network Causality
Description Ways of discovering causal relations, ways of estimating networks, what we can do with networks
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | Causality - different views |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Pitfalls of Granger Causality, Centrality |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Instrumental variables, Interventions |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Latters of causality |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Thu, July 30: Deep learning 1
Description The concept of ANNs, how to train them,what they are made out of, convnets, and how to fit them to brains
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | DL = crucial tool |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Pytorch intro & model components, Training it & inductive bias |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Convolutional Neural Network, Fit to brain (RSA - represenatational similarity analysis) |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Digging deep |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Fri, July 31: Deep learning 2
Description Deep learning in more advanced settings. Autoencoders for structure discovery, RNNs, and fitting them to brains
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00 - 0:30* | Intro / keynote & tutorial setup | DL for structure |
| 0:30 - 0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50 - 2:05 | Tutorials 1 & 2 + nano-lectures | Autoencoders, Recurrent Neural Network |
| 2:05 - 2:25 | Discussion 1 | Discussion with pod TA |
| 2:25 - 3:25 | Big break | BREAK |
| 3:25 - 4:40 | Tutorials 3 & 4 + nano-lectures | Transfer learning / generalization, Causality |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Digging deeper |
| 5:35 - 6:00 | Q&A | Q&A with lecturers/Mentors |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day
Networking (throughout) - interactive track only
- Meet a prof about your group’s project
- Meet a prof about your career
- Meet a prof about your own project
- Meet other participants interested in similar topics
- Meet a group of likeminded people
- Meet people that are local to you (same city, country)
Group projects (throughout) - interactive track only
TBA
Shield: 
This work and everything in this repo is licensed under a Creative Commons Attribution 4.0 International License.
