# Discovering dynamic computations from large-scale neural activity recordings

> **NIH NIH R01** · COLD SPRING HARBOR LABORATORY · 2020 · $441,600

## Abstract

Project Summary/Abstract
How neural activity is coordinated within local microcircuits and across brain regions to drive behavior is
a central open question in neuroscience. Recent advances in massively-parallel neural recording tech-
nologies are producing dynamic activity maps during complex behaviors, with single-neuron granularity
and single-spike resolution. To reveal fundamental dynamic features in these large-scale datasets, new
principled and scalable computational methods are urgently needed. To address this need, we will de-
velop a broadly applicable, non-parametric inference framework for discovering dynamic computations
from large-scale neural activity recordings. Our framework seeks a dynamical model of the data, but
unlike existing techniques, does not require a priori model assumptions. Existing techniques commonly
fit data with simple ad hoc models, which often miss or distort defining dynamic features. Instead, our
non-parametric approach explores the entire space of all possible dynamics in search for the model
consistent with the data, and thereby eliminates a priori guess work, ambiguous model comparisons
and model-induced biases. We aim to develop optimization algorithms to effectively search through the
space of all dynamical models, implement these algorithms on GPUs to achieve maximal computational
speed, and derive information-theoretic bounds to quantify reliability of our computational methods. To
demonstrate how our novel methods aid scientific discovery, we will employ them to examine decision-
related activity in parietal and premotor cortices. While different theoretical models of decision-making
have been proposed, it still remains unknown how decision computations are implemented on the level
of individual neurons and neural populations. Our analyses will offer the first computational models of
decision-making rooted directly in neural data, reconcile stability of population dynamics with hetero-
geneity of single-neuron responses, reveal differences in decision-computations across cortical layers,
and identify differences in decision-related dynamics of excitatory vs. inhibitory neurons.

## Key facts

- **NIH application ID:** 10002240
- **Project number:** 5R01EB026949-03
- **Recipient organization:** COLD SPRING HARBOR LABORATORY
- **Principal Investigator:** Tatiana Engel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $441,600
- **Award type:** 5
- **Project period:** 2018-09-20 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002240

## Citation

> US National Institutes of Health, RePORTER application 10002240, Discovering dynamic computations from large-scale neural activity recordings (5R01EB026949-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10002240. Licensed CC0.

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