Computational modeling in jupyter notebooks

Want to learn more about computational modeling? Check out these PsyNeuLink tutorials (works best if you open them in Colaboratory): 

• Using PsyNeuLink
• Hebbian learning
• Temporal difference learning
• Building blocks of neural networks
• Backpropagation

PsyNeuLink is an integrated language and toolkit for creating cognitive models. It makes cognitive modeling more accessible to beginners and easier to use for experts by providing standard building blocks (Drift Diffusion Models, Neural Nets, etc.) and the means to connect them together in a single environment. PsyNeuLink is designed to make the user think about computation in a "mind/brain-like" way while imposing minimal constraint on the type of models that can be implemented.

PsyNeuLink jupyter notebooks were written by Lena Rosendahl, Laura Bustamante, Justin Junge, Jonathan Cohen, and Abigail Novick Hoskin.

Hierarchical drift diffusion modeling

Interested in hierarchical drift diffusion modeling? Step 1: download HDDM, a wonderful python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Step 2: Read through my super basic tutorial that goes through how to load your data, specify your model, find your starting parameters, run your samples, and save your results.

Analyzing fMRI data

Want a cool python toolbox to analyze your fMRI data? Try Brainiak, a python toolbox developed by Princeton and Intel. BrainIAK is integrated with SciKit-Learn, and includes modules for Full Correlation Matrix Analysis (FCMA), Multi-voxel Pattern Analysis (MVPA), a suite of methods for Shared Response Modeling (SRM), Topographic Factor Analysis (TFA), and Bayesian-derived methods for Representational Similarity Analysis (RSA). I wrote a whole brain searchlight tutorial for Brainiak that you can run even if you don't have brain data yet.