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
Week 2
Week 3

Week 1

Mon, July 13: Introduction to Computational Neuroscience and NMA

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 - 0:50	Intro / keynote & tutorial setup	NMA organization, expectations, code of conduct, modeling vs. data
0:50 - 1:25	Lecture & Tutorial 1	Data intro, preprocessing
1:30 - 2:05	Lecture & Tutorial 2	Link of neural data to behavior
2:10 - 2:45	Lecture & Tutorial 3	Tuning (RFs, motor, STA)
3:30 - 4:05	Lecture & Tutorial 4	What it means to "understand" (signal detection)
4:35 - 5:30	Recap, Q&A	Outlook on school
5:30 - 6:00	Professional development	Being a good NMA participant

Tue, July 14: What do models buy us?

Description Introduce different example model types (Marr 1-3, what/how/why) and the kinds of questions they can answer. MRealize how different models map onto different datasets.

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Model classifications
0:50 - 1:25	Lecture & Tutorial 1	Marr 1
1:30 - 2:05	Lecture & Tutorial 2	Marr 2-3
2:10 - 2:45	Lecture & Tutorial 3	"What"
3:30 - 4:05	Lecture & Tutorial 4	"How"/"Why"
4:35 - 5:30	Recap, Q & A	The role of models in discovery
5:30 - 6:00	Professional development	How-to-model guide 1

Wed, July 15: Model fitting

Description Fit models to data, quantify uncertainty, compare models

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Why and how to fit models
0:50 - 1:25	Lecture & Tutorial 1	Fit a model 1 (linear regression)
1:30 - 2:05	Lecture & Tutorial 2	Get error bars
2:10 - 2:45	Lecture & Tutorial 3	Compare models, cross-validation, hyperparameters
3:30 - 4:05	Lecture & Tutorial 4	Fit a model 2 (nonlinear models)
4:35 - 5:30	Recap, Q & A	Critical evaluation of model fitting
5:30 - 6:00	Professional development	How-to-model guide 2

Thu, July 16: Machine learning (ML) - decoding

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 - 0:50	Intro / keynote & tutorial setup	We want to predict (scikit learn)
0:50 - 1:25	Lecture & Tutorial 1	GLMs (temporal filtering models)
1:30 - 2:05	Lecture & Tutorial 2	Linear classifier (SVM)
2:10 - 2:45	Lecture & Tutorial 3	Regularization (L1, L2)
3:30 - 4:05	Lecture & Tutorial 4	Shallow nonlinear classifier (SVM with RBF kernel)
4:35 - 5:30	Recap, Q & A	Promises and pitfalls of ML
5:30 - 6:00	Professional development	How-to-model guide 3

Fri, July 17: Dimensionality reduction / manifolds

Description Concept of dimensionality reduction, ways of doing it, what it means

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Manifolds to understand
0:50 - 1:25	Lecture & Tutorial 1	PCA 1
1:30 - 2:05	Lecture & Tutorial 2	PCA 2 (+CCA/clustering)
2:10 - 2:45	Lecture & Tutorial 3	Signal vs. Noise Manifolds
3:30 - 4:05	Lecture & Tutorial 4	Visualizing high-D nonlinear manifolds (e.g. t-SNE)
4:35 - 5:30	Recap, Q & A	The link between high-dimensional brain signals and low-dimensional behavior
5:30 - 6:00	Professional development	Efficient collaborations

Week 2

Mon, July 20: Bayes

Description Bayesian statistics, modeling of behavior, modeling of neural data, quantifying information

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Uncertainty
0:50 - 1:25	Lecture & Tutorial 1	Bayes rule I (product rule: cue combination)
1:30 - 2:05	Lecture & Tutorial 2	Bayes rule II (Marginalization and nuisance variables)
2:10 - 2:45	Lecture & Tutorial 3	Causal inference & structural models (use as example for marginalization)
3:30 - 4:05	Lecture & Tutorial 4	Bayesian decision theory
4:35 - 5:30	Recap, Q & A	Advanced Bayesian methods
5:30 - 6:00	Professional development	Productivity tools for science

Tue, July 21: Time series 1 (linear systems)

Description How to make estimates over time, how the brain does it

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	World has time
0:50 - 1:25	Lecture & Tutorial 1	Linear systems theory I (ND deterministic)
1:30 - 2:05	Lecture & Tutorial 2	Linear systems theory II (1D stochastic = OU process; ND stocastic = AR(1))
2:10 - 2:45	Lecture & Tutorial 3	Markov process
3:30 - 4:05	Lecture & Tutorial 4	State space model
4:35 - 5:30	Recap, Q & A	Linear systems rule the world
5:30 - 6:00	Professional development	Open source ecosystem, data management & sharing

Wed, July 22: Time series 2 (decision making)

Description How we can make decisions when information comes in over time

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	We need to decide stuff
0:50 - 1:25	Lecture & Tutorial 1	Information theory
1:30 - 2:05	Lecture & Tutorial 2	Sequential Probability Ratio Test (SPRT)
2:10 - 2:45	Lecture & Tutorial 3	Hidden Markov Model inference (DDM)
3:30 - 4:05	Lecture & Tutorial 4	Kalman filter
4:35 - 5:30	Recap, Q & A	Decisions, decisions, decisions ...
5:30 - 6:00	Professional development	Open science (general), replicability & reproducibility

Thu, July 23: Optimal control

Description We need to move gain info and reach goals

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	We want to control our actions...
0:50 - 1:25	Lecture & Tutorial 1	Expected utility / Cost
1:30 - 2:05	Lecture & Tutorial 2	Markov decision process (MDP)
2:10 - 2:45	Lecture & Tutorial 3	LQG control (MDP for linear systems)
3:30 - 4:05	Lecture & Tutorial 4	Motor control (signal-dependent noise, time cost, ...)
4:35 - 5:30	Recap, Q & A	Advanced motor control
5:30 - 6:00	Professional development	Networking at Conferences

Fri, July 24: Reinforcement learning (RL)

Description The setting of reinforcement learning and how it approximates the real world, behavior, and potential brain implementations

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Problem formulations: actor-critic
0:50 - 1:25	Lecture & Tutorial 1	Critic: reward prediction error
1:30 - 2:05	Lecture & Tutorial 2	Exploration (POMDP) vs Exploitation
2:10 - 2:45	Lecture & Tutorial 3	Model-based vs model-free RL
3:30 - 4:05	Lecture & Tutorial 4	Multi-arm bandits: foraging
4:35 - 5:30	Recap, Q & A	RL and the brain
5:30 - 6:00	Professional development	Writing Papers & Grants

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 - 0:50	Intro / keynote & tutorial setup	Real neurons ftw
0:50 - 1:25	Lecture & Tutorial 1	Channels, HH
1:30 - 2:05	Lecture & Tutorial 2	LIF neuron
2:10 - 2:45	Lecture & Tutorial 3	LNP (loses fine timing info)
3:30 - 4:05	Lecture & Tutorial 4	Hebbian plasticity & STDP
4:35 - 5:30	Recap, Q & A	A variety of neuron models
5:30 - 6:00	Professional development	How to find a postdoc

Tue, July 28: What happens in dynamic networks?

Description How single neurons create population dynamics

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Mechanistic models of different types of brain actvivity.
0:50 - 1:25	Lecture & Tutorial 1	Spikes to rates.
1:30 - 2:05	Lecture & Tutorial 2	Wilson-Cowen model (coarse-grained), oscillations & synchrony
2:10 - 2:45	Lecture & Tutorial 3	Attractors & local linearization around fixed points
3:30 - 4:05	Lecture & Tutorial 4	Chaos in rate networks (stimulus dependent chaotic attractor)
4:35 - 5:30	Recap, Q & A	A theory of the whole brain
5:30 - 6:00	Professional development	Early career panel - academia (how to advance through career steps)

Wed, July 29: Causality & networks

Description Ways of discovering causal relations, ways of estimating networks, what we can do with networks

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	Causality - different views
0:50 - 1:25	Lecture & Tutorial 1	Pitfalls of Granger Caausality
1:30 - 2:05	Lecture & Tutorial 2	Centrality
2:10 - 2:45	Lecture & Tutorial 3	Instrumental variables
3:30 - 4:05	Lecture & Tutorial 4	Interventions
4:35 - 5:30	Recap, Q & A	Latters of causality
5:30 - 6:00	Professional development	Computational neuroscience in industry - career panel

Thu, July 30: Deep learning (DL) 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 - 0:50	Intro / keynote & tutorial setup	DL = crucial tool
0:50 - 1:25	Lecture & Tutorial 1	Pytorch intro & model components
1:30 - 2:05	Lecture & Tutorial 2	Training it & inductive bias
2:10 - 2:45	Lecture & Tutorial 3	Convolutional Neural Network
3:30 - 4:05	Lecture & Tutorial 4	Fit to brain (RSA - represenatational similarity analysis)
4:35 - 5:30	Recap, Q & A	Digging deep
5:30 - 6:00	Professional development	Job fair (FRL)

Fri, July 31: Deep learning (DL) 2

Description Deep learning in more advanced settings. Autoencoders for structure discovery, RNNs, and fitting them to brains

Time (Hour)	Lecture	Details
0:00 - 0:50	Intro / keynote & tutorial setup	DL for structure
0:50 - 1:25	Lecture & Tutorial 1	Autoencoders
1:30 - 2:05	Lecture & Tutorial 2	Recurrent Neural Network
2:10 - 2:45	Lecture & Tutorial 3	Transfer learning / generalization
3:30 - 4:05	Lecture & Tutorial 4	Causality
4:35 - 5:30	Recap, Q & A	Digging deeper
5:30 - 6:00	Professional development	NMA wrap-up

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

irischang020 / course-content Goto Github PK

course-content's Introduction