# Models and Methods for Calcium Imaging Data with Application to the Allen Brain Observatory

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2020 · $356,806

## Abstract

PROJECT SUMMARY. New advances in calcium imaging make it possible to survey the brains of behaving animals at
single-neuron resolution, thereby promising to transform the field of neuroscience. However, existing statistical models
and methods are inadequate for this complex and noisy data. This proposal involves developing statistical models and
methods for the analysis of calcium imaging data.
 Aim 1 involves deconvolving a neuron's fluorescence trace in order to infer its underlying spike times. A number of
authors have considered a simple auto-regressive model for the effect of a neuron's spike on calcium dynamics, which
leads naturally to a non-convex optimization problem previously thought to be computationally intractable. A scalable
online algorithm will be developed for solving this non-convex optimization problem, leading to drastic improvements
over competing approaches. This approach will be extended to perform spike deconvolution while allowing for the
effect of a neuron's spike on calcium dynamics to take a completely non-parametric form.
 Existing approaches for quantifying the association between a neuron's activity and covariates of interest assume
that it is governed by a single model, which applies across all trials. However, this assumption appears not to hold
for calcium imaging data, which is characterized by a huge amount of heterogeneity in a single neuron's activity (and
association with covariates) across trials. Aim 2 involves developing a mixture model for the association between a
neuron's activity and covariates of interest, which can adequately capture real-world heterogeneity across trials.
 Researchers typically fit a separate model for each neuron in order to quantify the association between that neu-
ron's activity and the covariates of interest. Aim 3 involves “borrowing strength” across a population of ρ neurons, by
assuming that each neuron in the population follows one of L response models, where L << ρ. The neurons associ-
ated with a given response model can be viewed as a “functional cell type”; thus, this approach will lead not only to
the identification of functional cell types, but also to more accurate estimation of the model that governs each neuron's
firing rate, and a more refined understanding of neural dynamics.
 Finally, Aim 4 involves the development of high-quality open source software implementing the models and methods
developed in this proposal, as well as plans for the careful evaluation of these tools by two end-users: a theorist and
an experimentalist.
 The models and methods developed in this proposal are motivated by, and will be applied to, data from the Allen
Brain Observatory, a large-scale publicly-available repository of calcium imaging data from the visual cortex of mice that
were exposed to five types of visual stimuli. The investigators will create high-quality publicly-available software that
implements the models and methods developed in this proposal. All tools (models, metho...

## Key facts

- **NIH application ID:** 10000915
- **Project number:** 5R01EB026908-03
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Michael Buice
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $356,806
- **Award type:** 5
- **Project period:** 2018-09-20 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000915

## Citation

> US National Institutes of Health, RePORTER application 10000915, Models and Methods for Calcium Imaging Data with Application to the Allen Brain Observatory (5R01EB026908-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10000915. Licensed CC0.

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