# Scalable Bayesian Stochastic Process Models for Neural Data Analysis

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2020 · $376,035

## Abstract

Summary
The overarching goal of this proposal is to integrate the development of a novel class of statistical methods
with unique electrophysiological experiments in rats to address fundamental and unresolved questions about
hippocampal function and, in subsequent studies, to provide unprecedented insight into the neural
mechanisms underlying memory impairments. The development of novel statistical tools for the analysis of
neural data is key to advance our understanding of fundamental memory mechanisms and of memory
disorders. However, many existing statistical methods are not capable of handling such data-intensive
problems in terms of theoretical foundation, computational complexity, and scalability. To address this issue,
we will design a robust framework for analyzing neural data using flexible multivariate Gaussian process (GP)
models (Aim 1). This novel framework will allow the integration of multiple data modalities, in particular multi-
neuronal spike trains and multi-node local field potentials (LFP), while identifying their joint low-dimensional
representations and underlying structures. To make our approach practical for big data analysis, we will
develop computationally efficient algorithms for fast, yet accurate statistical inference (Aim 2). Our proposed
approach is based on a novel combination of fast variational approximation methods and computationally
efficient Markov Chain Monte Carlo algorithms. We will apply our analytical methods to unique
electrophysiological datasets collected as part of a research program aimed at elucidating the fundamental
neural mechanisms underlying the memory for sequences of events, a defining feature of episodic memory
(Aim 3). In these datasets, we use high-density electrophysiological techniques to record neural activity in
hippocampal region CA1 (spikes and LFP) as rats perform an odor sequence memory task. Importantly, this
nonspatial approach allows us to determine whether spatial coding properties (thought to be fundamental to
hippocampal memory function) extend to the nonspatial domain, including sequence reactivation (reactivation
of previously traversed or upcoming sequences of locations) and phase precession (spikes occurring at
progressively earlier phases of the theta cycle during traversal of the cell's place field). Combining these novel
analytical tools with the sophisticated behavioral, electrophysiological, and DREADDs inactivation approaches
proposed here will provide us with an unparalleled opportunity to address fundamental questions about
hippocampal function.

## Key facts

- **NIH application ID:** 9834979
- **Project number:** 5R01MH115697-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Babak Shahbaba
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $376,035
- **Award type:** 5
- **Project period:** 2018-03-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9834979

## Citation

> US National Institutes of Health, RePORTER application 9834979, Scalable Bayesian Stochastic Process Models for Neural Data Analysis (5R01MH115697-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9834979. Licensed CC0.

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