Available for Download and Use

Princeton Full Correlation Matrix Analysis (FCMA) Toolbox

► Released in 2013, the FCMA Toolbox uses high-performance computing (HPC) for exhaustively analyzing and classifying distributed patterns of correlation in fMRI data.

Virtual Reality MATLAB Engine (ViRMEn)

► Released in 2013, ViRMEn is a MATLAB-based software package for designing and running virtual reality experiments on animals.

Princeton Functional Normalization Toolbox

The Functional Normalization Toolbox aims to align the functional neuroanatomy of individual brains based on the patterns of neural activity while watching a movie. Instead of basing alignment on functionally defined areas, whose location is defined as the center of mass or the local maximum response, the alignment is based on patterns of response as they are distributed spatially both within and across cortical areas. The method is implemented in the two-dimensional manifold of an inflated, spherical cortical surface. The method, although developed using movie data, generalizes successfully to data obtained with another cognitive activation paradigm, viewing static images of objects and faces, and improves group statistics in that experiment as measured by a standard general linear model (GLM) analysis.
► Go to Functional Normalization Toolbox for more information.

Princeton Multi-Voxel Pattern Analysis (MVPA) Toolbox

Released in 2006, the MVPA (Multi-Voxel Pattern Analysis) Toolbox is a set of Matlab tools to facilitate multi-voxel pattern analysis of fMRI neuroimaging data. The aim is to create a set of open source functions in a widely-used language to facilitate exploration of multi-vowel pattern analysis techniques and to reduce the 'startup costs' for knowledgeable users eager to apply pattern classification algorithms to their imaging data. By developing the toolbox in the Matlab environment, users are able to take advantage of the vast array of existing functions. The data structures used and generated by the toolbox are designed to facilitate exploration and further script development.
► Go to Princeton MVPA Toolbox for more information.


This is a Python-based, open source software package, developed in collaboration with Intel Labs, that supports the application of advanced methods from machine learning and high-performance computing to the analysis of neuroimaging data. It is tightly 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), among other methods.
► Go to brainiak.org for more information.


This is a Python-based, open source simulation environment, designed for use by neuroscientists, psychologists and others interested in building system-level models of the computational mechanisms underlying brain function and its expression in psychological processes and behavior, and in exploring their relationship to developments in research on machine learning and artificial intelligence. It allows components to be constructed that implement various, possibly disparate functions, at potentially different levels of analysis and/or timescale of operation, and integrate these into a coherent modeling environment that can be used to simulate and study their interaction. PsyNeuLink is written in Python, is open source, and meant to be extended.
► Go to psyneulink.org for more information.