# Computational Core

> **NIH NIH P50** · UNIVERSITY OF MINNESOTA · 2021 · $301,625

## Abstract

PROJECT SUMMARY: COMPUTATIONAL CORE
The purpose of the COMPUTATIONAL CORE is to provide services for infrastructure and conceptual support
to integrate the four Projects that form our Center. To do this, the Core will ensure that the trial-by-trial
behavioral data collected from the four Projects (from the computationally informative DPX and Bandit tasks)
are analyzed in a uniform and compatible manner, so that findings across Projects can be compared and
modeled. It will also supply overall statistical support to ensure that statistical analyses are done appropriately.
The Core consists of a Service Aim and a Modeling Aim. In the Service Aim, we propose to apply causal
discovery analyses, a recently developed toolkit of mathematical algorithms that can infer explanatory
relationships between co-occurring data parameters. Causal discovery analyses serve as a powerful
inferential toolbox that can be applied to all of the PROJECTS independently, facilitating the generation of new
hypotheses within and across PROJECTS. In the Modeling Aim, the Core will provide conceptual and
modeling support for the theoretical underpinning of state representation dysfunctions relevant to psychosis.
As noted in the OVERALL RESEARCH STRATEGY, we take a central computational perspective to translate
across species and methodologies, using theoretical constructs to bridge the gap between neural dysfunction
and observable manifestations of that dysfunction. To this end, the Core will integrate two existing models: an
Algorithmic-Level Model that translates attractor dynamics to behavior, and a Neurophysiology-Level Model
that translates neuronal properties to attractor dynamics. Our goal is to examine how neuronal-scale effects,
such as those seen in the non-human animal projects (PROJECTS 1 & 2) translate into observable behavioral
and neurophysiological effects in healthy and clinical populations, such as those seen in the human projects
(PROJECTS 3 & 4), by merging these two models into an integrated model crossing levels, that can provide
mechanistic explanatory power for how neurophysiological effects produce attractor dynamics that lead to
behavioral outcomes.

## Key facts

- **NIH application ID:** 10123010
- **Project number:** 5P50MH119569-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** A DAVID REDISH
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $301,625
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10123010

## Citation

> US National Institutes of Health, RePORTER application 10123010, Computational Core (5P50MH119569-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10123010. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*