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

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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.