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, 3h15min lecture/live Q&A/tutorial modules, 40min group discussion, 55min interpretation lecture (Outro) + live Q&As (what did we learn today, what does it mean, underlying philosophy). There will also be many networking activities (professional development sessions, yoga, social hangouts etc)! Details below.
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
Group projects are offered for the interactive track only and will be running during all 3 weeks of NMA!
Description Two-day workshop for absolute Python beginners. Learn essential Python skills for course tutorials and practice by coding a neuronal simulation.
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 |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Model classifications |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | "What"/"How" models |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | "Why" model & discussion |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, The role of models in discovery |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Introduction to the process of building models.
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | How to approach modeling |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Framing the question |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | Model implementation and testing |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, the modeling process |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Fit models to data, quantify uncertainty, compare models
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Why and how to fit models |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Fit a model 1 (linear regression), Get error bars |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Compare models, cross-validation, hyperparameters, Fit a model 2 (nonlinear models) |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Critical evaluation of model fitting |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Introduction to machine learning. The commonly used approaches, how to avoid false positives, how to do it well
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | We want to predict (scikit learn) |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Introduction to GLMs and predicting neural responses |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Logistic regression, regularization, and decoding neural activity |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Promises and pitfalls of ML for Neuroscience |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Concept of dimensionality reduction, ways of doing it, what it means
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Manifolds to understand |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | PCA 1 (orthonormal basis), PCA 2 (eigenvalues) |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | MNIST with PCA, MNIST with t-SNE |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, The link between high-dimensional brain signals and low-dimensional behavior |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Weekend 1: Professional Development & Social
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times are available on the Neurostars Calendar
Description Bayesian statistics, modeling of behavior, modeling of neural data, quantifying information
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Uncertainty |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Bayes rule: cue combination and marginalization |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Bayesian Decision Theory & Causal inference |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Advanced Bayesian methods |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description How to make estimates over time, how the brain does it
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | World has time |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Linear systems theory I (ND deterministic) and Markov process |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | Linear systems theory II (1D stochastic = OU process; ND stocastic = AR(1)) and State space model |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Linear systems rule the world |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description How we can make decisions when information comes in over time
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | We need to decide stuff |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Information theory, Sequential Probability Ratio Test (SPRT) |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Hidden Markov Model inference (DDM), Kalman filter |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Decisions, decisions, decisions ... |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description We need to move gain info and reach goals
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | We want to control our actions... |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Expected utility / Cost, Markov decision process (MDP) |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | LQG control (MDP for linear systems), Motor control (signal-dependent noise, time cost, ...) |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Advanced motor control |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description The setting of reinforcement learning and how it approximates the real world, behavior, and potential brain implementations
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Problem formulations: actor-critic |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Critic: reward prediction error, Exploration (POMDP) vs exploitation |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | Model-based vs model-free RL, Multi-arm bandits: foraging |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, RL and the brain |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Weekend 2: Professional Development & Social
Description Professional development sessions and social activities will be offered on the weekend. More information, including exact times are available on the Neurostars Calendar
Description The things neurons are made of, channels, morphologies, neuromodulators, and plasticity
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Real neurons ftw |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Reduced neuron models and transfer of synchrony |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Short-term plasticity of synapses and Hebbian plasticity & STDP |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, A variety of neuron models |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description How single neurons create population dynamics
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Mechanistic models of different types of brain actvivity |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | 2D dynamical systems, Wilson-Cowen model (coarse-grained), oscillations & synchrony |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | Attractors & local linearization around fixed points, Balanced Amplification & Inhibition-stabilized network |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, A theory of the whole brain |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Ways of discovering causal relations, ways of estimating networks, what we can do with networks
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | Causality - different views |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Pittfalls of Granger and Centrality |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Instrumental Variables and interventions |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Ladders of causality |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
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 |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | DL = crucial tool |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Pytorch intro & model components, Training it & inductive bias |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK incl. 20min yoga session |
| 2:55-4:10 | Tutorials + nano-lectures II | Convolutional Neural Network, Fit to brain (RSA - represenatational similarity analysis) |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Digging deep |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
Description Deep learning in more advanced settings. Autoencoders for structure discovery, RNNs, and fitting them to brains
| Time (Hour) | Lecture | Details |
|---|---|---|
| -0:30-0:00* | Intro / keynote & tutorial setup | DL for structure |
| 0:00-0:15 | Pod Q&A | Lecture discussion with pod TA |
| 0:20-1:35 | Tutorials + nano-lectures I | Variational autoencorders and uses in Neuroscience |
| 1:35-1:55 | Discussion I | Discussion with pod TA |
| 1:55-2:55 | Big break | BREAK |
| 2:55-4:10 | Tutorials + nano-lectures II | NMA wrap-up |
| 4:10-4:30 | Discussion II | Discussion with pod TA |
| 4:35-5:05** | Outro | Recap session, Digging deeper |
| 5:05-6:05 | Q&A | Q&A with lecturers/Mentors |
| 6:05-7:05 | Social hour | Social hour |
* the Intro / keynote will be watched asynchronously, which means that you can watch this lecture before the start of the day ** please watch the Outro on your own after having completed the tutorials and before the Q&A session
- 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)
These are hour-long Crowdcast events with a panel of speakers that will discuss a topic and answer audience questions. For exact times in the relevant time-zones, see the Neurostars Calendar. They will be live streamed and recorded for later watching. Some sessions are duplicated to ensure coverage for students in different time-zones.
| Session | Speakers | Description |
|---|---|---|
| 2.1: Data management | Dimitri Yatsenko (DataJoint), Edgar Walker (DataJoint), Ben Dichter (NWB) | Software tools for efficient management, analysis, and sharing of experimental data. |
| 2.2: Data management | Russ Poldrack (Stanford) | Efficient data management and sharing strategies focusing on fMRI data. |
| 3.1 Open Science | Nici Pfeiffer (COS), Mark Musen (Stanford) | Discussion of tools to improve accessibility and transparency of scientific data and analysis. |
| 4.1 Visualization | Colin Prahl (Artist), Amy Robinson Stirling (Eyewire) | Presentation on scientific visualization, ranging from scientific data visulaziation to visualization in scientific communication and science-inspired art. |
| 5.1 Publishing | Brett Mensh | The art and logic of writing peer-reviewed journal papers. |
| 6.1 Philosophy | Lisa Mirachi (U Penn) | What is the difference between “what a cognitive agent does” and “what processes happen inside an agent”? Lisa Miracchi, philosophy professor at UPenn, talks about how this distinction relates to selecting models. |
| 6.2 Philosophy | Lisa Mirachi (U Penn) | What is the difference between “the content of representations” and “the content of perceptions and thoughts”? Lisa Miracchi, philosophy professor at UPenn, talks about Marr’s Computational Level and how these questions relate to what robots might internally represent |
| Session | Speakers | Description |
|---|---|---|
| 3.2 Open Science | Nici Pfeiffer (COS), Mark Musen (Stanford) | Discussion of tools to improve accessibility and transparency of scientific data and analysis. |
| 7.1 Job fair | Facebook Reality Labs | Research careers at FRL, a division of Facebook focused on creating the future of virtual and augmented reality. |
| 7.2 Job fair | Facebook Reality Labs | Research careers at FRL, a division of Facebook focused on creating the future of virtual and augmented reality. |
| 8.1 Careers in industry | Becky Clarkson (Apple), Kyle Frankovich (Insight Data Science), Kachi Odoememe (APL) | Research careers in industry. |
| 8.2 Careers in industry | Feryal MP Behbahani (DeepMind), Sophie Kenny (Vpixx), Graeme Moffat | Research careers in industry. |
| 9.1 Careers in academia | Sripati Panditaradhyula Arun (IISc), Minhae Kwon (Soongsil University), Taro Toyoizumi (RIKEN) | Research careers in academia. |
Shield:
This work and everything in this repo is licensed under a Creative Commons Attribution 4.0 International License.
