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: ~5min talk, ~15min hands-on (repeated)
Day structure: Opening keynote, 2.5h lecture/tutorial modules, 1/2h group discussion, 1h interpretation lecture + live Q&As (what did we learn today, what does it mean, underlying philosophy). There will also be many networking activities!
Note for visitors from China: This repository contains many links to YouTube and Google Colab. We have a version of the repository with those same videos posted on bilibili, and the Google Colab links replaced with links to Aliyun. Please visit the China Accessible Neuromatch Course-Content
Prerequisites: 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 | "What"/"How" models |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorials 3 + nano-lectures | "Why" model & discussion |
| 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 to the process of building models.
| Time (Hour) | Lecture | Details |
|---|---|---|
| 0:00-0:30* | Intro / keynote & tutorial setup | How to approach modeling |
| 0:30-0:45 | Pod Q&A | Lecture discussion with pod TA |
| 0:50-2:05 | Tutorial 1 + nano-lectures | Framing the question |
| 2:05-2:25 | Discussion 1 | Discussion with pod TA |
| 2:25-3:25 | Big break | BREAK |
| 3:25-4:40 | Tutorial 2 + nano-lectures | Model implementation and testing |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, the modeling process |
| 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 | Introduction to GLMs and regularization |
| 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 | GLMs for encoding and decoding |
| 4:40-5:00 | Discussion 2 | Discussion with pod TA |
| 5:05-5:35 | Outro | Recap session, Promises and pitfalls of ML for Neuroscience |
| 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 (orthonormal basis), PCA 2 (eigenvalues) |
| 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 | MNIST with PCA, MNIST with 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: cue combination and marginalization |
| 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 | Bayesian Decision Theory & Causal inference |
| 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) and Markov process |
| 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 | Linear systems theory II (1D stochastic = OU process; ND stocastic = AR(1)) and 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 | Reduced neuron models and transfer of 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 | Short-term plasticity of synapses and 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 | 2D dynamical systems, 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, Balanced Amplification & Inhibition-stabilized network |
| 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 | Pittfalls of Granger and 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 and interventions |
| 4:40 - 5:00 | Discussion 2 | Discussion with pod TA |
| 5:05 - 5:35 | Outro | Recap session, Ladders 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 | Variational autoencorders and uses in Neuroscience |
| 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 | NMA wrap-up |
| 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.
