Home

PsyNeuLink

PsyNeuLink is a block modeling system for cognitive neuroscience.
It is open source, and meant to be extended

Contributors

Jonathan D. Cohen, Princeton Neuroscience Institute, Princeton University
Peter Johnson, Princeton Neuroscience Institute, Princeton University
Bryn Keller, Intel Labs, Intel Corporation
Sebastian Musslick, Princeton Neuroscience Institute, Princeton University
Aida Piccato, Princeton University
Amitai Shenhav, Cognitive, Linguistic, & Psychological Sciences, Brown University
Michael Shvartsman, Princeton Neuroscience Institute, Princeton University
Ted Willke , Intel Labs, Intel Corporation

Purpose

To provide an environment for implementing models of mind/brain function
that is modular, customizable and extensible.  It does this in a manner that:
 - is computationally general
 - adheres as closely as possible to the insights and design principles that have been learned in CS
     (e.g., function-based, object-oriented, programming)
 - expresses (the smallest number of) "commitments" that reflect general principles of how
     the brain/mind is organized and function, without committing to any particular model or theory
 - expresses these commitments in a form that is powerful, easy to use, and familiar to cognitive neuroscientitsts
 - allows models to be simply and flexibly implemented, using a minimum of coding that provides 
     seamless interaction among disparate components that can vary in their:
     - time-scale of operation
     - granularity of representation and function
- encourages users to think about processing in a "mind/brain-like" way,
     while imposing as few constraints as possible on what they can implement or ask their model to do
- provides a standard environment for model comparison, sharing, and integration  

Functional Architecture

 - System 
     Set of (potentially interacting) processes, that can be managed by a “budget” of control

     - Process 
         Function that takes an input, processes it through an ordered list of mechanisms (and projections)
         and generates an output

         - Mechanism 
             Function that converts an input state representation into an output state representation
             Parameters determine its operation, under the influence of projections

         - Projection 
             Function that takes a source of in, possibly transforms it, and uses it to
             determine the operation of a mechanism;  three primary types:

             + Mapping
                 Takes the output of sender mechanism, possibly transforms it,
                     and provides it as the input to a receiver mechanism

             + ControlSignal
                 Takes an allocation (scalar), possibly transforms it,
                 and uses it to modulate the parameter(s) of a mechanism's function

             + Learning
                  Takes an error signal (scalar or vector), possibly transforms it,
                  and uses it to modulate the parameter of a projection function
                 
             [+ GatingSignal — Not yet implemented
                 Takes a source, possibly transforms it, and uses it to
                 modulate the input or output state of a mechanism