# Neuronal population dynamics within and across cortical areas

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2020 · $334,260

## Abstract

Project Summary: The cortex must both track and process dynamically changing environments as well
as store and combine diverse inputs to generate complex behavior. Further, the neuronal circuits that
accomplish this must be malleable to changing contexts, such as during attention related tasks. Charged
with these tasks it is perhaps unsurprising that the response dynamics of populations of cortical neurons
is then dauntingly complex. Currently, we lack a deep understanding of the circuit mechanics that
underlie the rich dynamics exhibited in the nervous system. This omission is particularly serious given
the ever increasing breadth of data showing that neuronal dynamics, and its variability, is context-
dependent and shared across large regions of the brain. Our proposal seeks to address several
fundamental issues facing current network models. Namely, spiking network models with balanced
excitation and inhibition are not currently capable of generating realistic transient activity, steady state
activity, and neural variability within a single model. To address these shortcomings, we will develop an
automated method for optimizing the parameters of network models. We will then validate the
optimization method and resulting network models by comparing the population activity generated by
the network models with that recorded in macaque visual area V4 and prefrontal cortex during
discrimination and working memory tasks. To perform this comparison, it is a fruitless exercise to
attempt to correspond each recorded neuron to a neuron in the network model. Instead, a key
innovation of our proposal is that we will compare the low-dimensional representations of the
population activity in the network model and the real data. The network models and optimization
method that we build will be will be widely shared with the research community. If successful, the work
proposed here will lead to a vastly deeper understanding of how neural circuits give rise to
transient activity, steady-state activity, and neural variability, and equip the research community with
the tools to make further discoveries in this direction.

## Key facts

- **NIH application ID:** 10233310
- **Project number:** 7R01EB026953-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Brent D. Doiron
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $334,260
- **Award type:** 7
- **Project period:** 2020-08-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10233310

## Citation

> US National Institutes of Health, RePORTER application 10233310, Neuronal population dynamics within and across cortical areas (7R01EB026953-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10233310. Licensed CC0.

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