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Neuromatch academy syllabus (preliminary)
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(July 2020)
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Objectives
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Microstructure~10min talk, ~20min tutorial (repeated)
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Day structure4h methods, 1h interpretation (what did we learn today, what does it mean, underlying philosophy, 1h professional development/ meta-science)
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There will also be many networking activities! (interactive track)
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Prerequisiteshttps://github.com/NeuromatchAcademy/precourse
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ThemeGoal & use in neuroContent
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Week 1: Signals
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July 13Intro to Neuro/ School in a day 1 datasetWe take one dataset, and ask questions about it. These questions will foreshadow the whole summer schoolWe have brain data: spikesBasic processingSimple spike analysesWhat would we like to know?How do we "understand"?What will this tell us?
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July 14Encoding/ All the datasetsIntroduce fMRI dataset, EEG dataset, behavioral data, and ECog dataset, plot tuning, understand what it meansWhy tuningRFsfMRIEEGModel fitting
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July 15Model fittingFit models to data, quantify uncertainty, compare models, why to prefer models over others, GLMsWhy fitFit a modelGet error barsCompare modelsCertaintyGLMsNonconvex
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July 16Dim reduction/ ManifoldsConcept of dimensionality reduction, ways of doing it, what it meansManifolds to understandPCARSAGPFAjPCAICAclustering
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July 17spikes, LFPs, fMRI, signals and their relationsThe various kinds of data and their relations, how to think about them, commonalities, differencesIndirect measurementsHHSpikingSimulationsmean fieldAdditionsCurrents
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Week 2: Statistics, Behavior, and NeuronsBiological Neural NetworksLinking observed statistics of brain's activity to neural hardware. Understanding the origin of oscillations/synchrony etc. which have consequences for both decoding and encodingNotion of excitation and inhibition balanceOscillations and synchronyFucntional networks: Neural Engg. Framework/Efficient Coding
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July 20BayesBayesian statistics, modeling of behavior, modeling of neural data, quantifying informationUncertaintyBayes rule (cue combination)Naive BayesCausal inferenceStructural modelsGraphical modelsInfo theory
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July 21Time seriesHow to make estimates over time, how the brain does itWorld has timeKalman filterAR(1)State spaceMarkov / POMDP
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July 22Decision making -> Optimal controlHow we can make decisions when information comes in over time, how to do so optimallyDecisionsDDMBayesian decision theory + SPRTConfidenceOptimal control
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July23Causality/ NetworksWays of discovering causal relations, ways of estimating networks, what we can do with networksCausalityGranger. DCMCentrality etcNetwork generatorsExplainable models
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July 24real neuronsThe things neurons are made of, channels, morphologies, neuromodulators, and plasticityReal neurons ftwPlasticityMorphologyNeuromodulatorsRelation to models
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Week 3: ML + advanced approaches
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July 27ML (decoding)Introduction to machine learning. The commonly used approaches, how to avoid false positives, how to do it wellWe want to predictxvalLogistic regressionscikit learnxgboostRandom forestknn
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July 28DL D1The concept of ANNs, how to train them,what they are made out of, convnets, and how to fit them to brainsDL = crucial toolPytorch introBuilding a modelTraining itComponentsConvnetsFit to brain
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July 29DL D2Deep learning in more advanced settings. Autoencoders for structure discovery, RNNs, and fitting them to brainsDL for structureAutoencodersT-SNEVAERNNFit to brainRealistic backprop
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July 30RLThe setting of reinforcement learning and how it approximates the real world, behavior, and potential brain implementationsProblem formulationsSolve something trivialDeep RLBio Implementation
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July 31Video based pose tracking and wrapupThe need to model and measure nontrivial behaviors, video based pose tracking as enabler, understanding behavior and global wrap up for the summer schoolWhat is it for?Understand the codeUnderstand how it is trainedDomain adaptationUnderstanding the resulting trackingWrapup
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Professional Development
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Professional Development
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Being a good Neuromatch school participant
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Writing Papers & Grants
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Networking at Conferences
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Negotiation + startups
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Early career panel - academia (how to advance through career steps)
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How to find a postdocNetworking (anytime)
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Efficient collaborationsMeet a prof about your group's project
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Comp neuro- in industry - career panelMeet a prof about your career
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Meet a prof about your own project
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Meta-scienceMeet other participants interested in similar topics
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How-to-model (3x 1h tutorial sessions!)Meet a group of likeminded people
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Productivity tools for scienceMeet people that are local to you (same city, country)
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Open source ecosystem, data management & sharing
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Open science (general), replicability & reproducibility
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