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Methods & data analysis

Machine Learning Methods for Brain and Cog Sciences (TBD, Serre):

Covers statistical methods as well as machine learning methods (regularization, Granger causality, SVM, etc) with hands on application to neuroscience data (fMRI, spiking data, LFP, etc).

Quantitative Model Fitting (TBD, Frank):

Model fitting to behavioral data (e.g., accuracy, response time distributions) used to assess latent computations to be regressed against neural data. Covers model selection, maximum likelihood estimates, parameter stability and identifiabilty, hierarchical bayesian parameter estimation across groups and individuals (MCMC, EM), frequentist and bayesian statistics on inferred parameters, and applications to neural data. Prominent examples in reinforcement learning, decision making, inhibition and memory models, but tools extended to any domain. Programming in matlab and/or python.

    Academic Resources at Brown
  1. Recommended coursework for undergraduate students
  2. Faculty with computational neuroscience research
    Recommended Courses on Computational Modeling
  1. Methods & programming
  2. Biostats & bioengineering
  3. Methods & data analysis
  4. Computational vision
  5. Computational cognitive science / neuroscience
  6. Neuroscience
  7. Mathematical & Computations