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
Welcome video: See here
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
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
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
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
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
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times TBA
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
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
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
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
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
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times TBA
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
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
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
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
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
- 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)
TBA
Shield:
This work and everything in this repo is licensed under a Creative Commons Attribution 4.0 International License.
