# CRCNS: US-French Research Proposal: Principles of Inference through Neural Dynamics

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2020 · $184,715

## Abstract

Recurrent interactions between neurons generate dynamic patterns of activity that serve as a substrate for
behaviorally relevant computations. However, we do not yet have a principled framework for relating neural
dynamics to neural computations. We have recently synthesized a theory that explains how low-rank
recurrent neural networks may serve as a building block for computations. Our overarching goal is to integrate
insights from this theory with behavior and electrophysiology in awake, behaving monkeys to establish a
principled framework relating neural dynamics to neural computations. The project will start with reverse
engineering low-rank network models that capture cortical dynamics in simple timing tasks. We then move
systematically toward progressively higher rank network models that can perform timing tasks with
progressively more sophisticated computational demands such as probabilistic inference of time intervals.
We aim to create models that simultaneously succeed in performing task-relevant computations (i.e.,
behavior) and emulate cortical dynamics recorded in monkeys performing those tasks. We will use this
iterative process to establish a principled framework relating neural dynamics to neural computations
underlying inference. Finally, we will put this framework to test using a novel task that demands an
unprecedented level of computational flexibility.
RELEVANCE (See instructions):
It has become increasingly apparent that the neurobiology of behavior in health and disease has to be
probed at the level of populations of neurons. However, we do not yet have a rigorous and quantitative
language for linking population neural activity to behavior. Our work combines primate electrophysiology
with neural network modeling and aims to develop such a language through the mathematics of dynamical
systems. The results hold promise for future translational research to diagnose behavioral symptoms of
brain dysfunction in terms of their computational modules and the dynamic patterns of activity that support
those modules.

## Key facts

- **NIH application ID:** 9999056
- **Project number:** 5R01MH122025-02
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mehrdad Jazayeri
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $184,715
- **Award type:** 5
- **Project period:** 2019-08-19 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999056

## Citation

> US National Institutes of Health, RePORTER application 9999056, CRCNS: US-French Research Proposal: Principles of Inference through Neural Dynamics (5R01MH122025-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9999056. Licensed CC0.

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