# Tracking Changes in High-Dimensional Circuit Behaviors over Long-Term Neural Recordings

> **NIH NIH F32** · STANFORD UNIVERSITY · 2020 · $69,554

## Abstract

Project Summary / Abstract
Recent breakthroughs in neural recording technologies suggest the possibility of understanding the
collective dynamics of large-scale brain circuits. However, investigations into these circuits are typically
limited to short recording sessions, and overlook the possibility that dynamics can change over longer
timescales due to differences in arousal, cognitive state, learning, and low-level biochemical turnover.
Identifying which aspects of network behavior are sensitive to these factors, and which are persistent,
would produce deeper and more contextualized understandings of many different neural systems.
This goal poses severe data analytic challenges. While hundreds of neurons can be recorded over long
time periods, we lack established statistical methods that track changes to the high-dimensional structure
of network interactions over time. I will work with Dr. Scott Linderman, an expert in neural time series
analysis, to overcome this challenge.
I will collaborate with two premier experimental labs (Dr. Lisa Giocomo and Dr. Krishna Shenoy) to study
circuit-level plasticity across different species (rodents and nonhuman primates) and behavioral tasks
(navigation and motor learning). How these circuits reconfigure themselves over multiple hours, days, and
weeks is poorly understood. This work will yield early scientific results in this regard, and develop
general-purpose statistical tools for the neuroscience community.

## Key facts

- **NIH application ID:** 9989472
- **Project number:** 1F32MH122998-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Alexander Henry Williams
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $69,554
- **Award type:** 1
- **Project period:** 2020-03-11 → 2023-03-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9989472

## Citation

> US National Institutes of Health, RePORTER application 9989472, Tracking Changes in High-Dimensional Circuit Behaviors over Long-Term Neural Recordings (1F32MH122998-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/9989472. Licensed CC0.

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