# PsyNeuLink:  A Block Modeling Environment for Cognitive Neuroscience and Computational Psychiatry

> **NIH NIH R21** · PRINCETON UNIVERSITY · 2020 · $197,545

## Abstract

Project Summary
Paralleling the growth of neuroscience research, there has been an explosion in the development of
computationally explicit models of the functions of core brain subsystems. Unfortunately, however, there has
not been a commensurate development of the tools needed to share, validate, and compare such models, or
integrate them into models of system-level function. Such sharing, evaluation, and integration are necessary if
computational modeling efforts are to be useful not only in generating reliable and accurate accounts of how
brain subsystems operate, but also of how they interact to give rise to higher cognitive functions, and how
disruptions of such interactions may give rise to disturbances of mental function observed in psychiatric and
neurological disorders. This proposal seeks to meet this need by developing PsyNeuLink: an open source,
Python-based software environment that makes it easy to create new models, import and/or re-implement
existing ones, integrate these within a single software environment that will facilitate head-to-head comparison
of comparable models, the assembly of complementary models into system-level models, and serve as a
common repository for the documentation and dissemination of such models for both research and didactic
purposes (i.e., publication, education, etc.).
These goals will be pursued under two Specific Aims: 1) Extend the scope of modeling efforts that
PsyNeuLink can accommodate by: i) enhancing its application programmer interface (API) used to add new
components and interfaces to statistical analysis tools and other modeling environments (such as PyTorch,
Emergent and ACT-R; ii) enriching its Library by adding PsyNeuLink implementations of influential models of
neural subsystems; and iii) developing a publicly available workbook of simulation exercises as both an
introduction to PsyNeuLink and for use in Cognitive Neuroscience and Computational Psychiatry curricula. 2)
Accelerate PsyNeuLink by developing a custom compiler that preserves its simplicity and flexibility, while
dramatically increasing its speed, to make it suitable for simulation of large and complex system-level models,
and for parameter estimation, model fitting, and model comparison.
This project will exploit the power and accelerating use of Python, and modern just-in-time compilation
methods to develop a tool designed specifically for the needs of systems-level Cognitive Neuroscience and
Computational Psychiatry. This promises to open up new opportunities for research at the systems-level — a
level of analysis that is crucial both for understanding how human mental function emerges from the interplay
among neural subsystems, and how disturbances of individual neural subsystems impact this interplay,
disruptions of which are almost certainly a critical factor in neurologic and psychiatric disorders.

## Key facts

- **NIH application ID:** 9976610
- **Project number:** 5R21MH117548-02
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** JONATHAN D COHEN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $197,545
- **Award type:** 5
- **Project period:** 2019-07-15 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976610

## Citation

> US National Institutes of Health, RePORTER application 9976610, PsyNeuLink:  A Block Modeling Environment for Cognitive Neuroscience and Computational Psychiatry (5R21MH117548-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9976610. Licensed CC0.

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