# Investigating the microcircuit determinants of neural population activity through comparative analysis of latent dynamics across cortical areas in the mouse

> **NIH NIH RF1** · UNIVERSITY OF MINNESOTA · 2022 · $360,977

## Abstract

Project Summary
A key goal in neuroscience is determining how microcircuit structure predicts circuit function. An intriguing idea,
supported by some theoretical models, is that variation in microcircuit composition supports functional
specialization. This theory has received support from the observation of a correlation between gradients in circuit
properties (receptor expression densities; inhibitory cell types) and in measurements of average intrinsic
timescales of recorded activity across cortical areas. However, other theories show how observed hierarchies of
timescales can emerge in the absence of microcircuit variation, either in critically tuned random networks or in
feed-forward networks with each layer summing over correlated sets of inputs. Moreover, individual cells are
embedded in complex, interconnected networks, generating correlations on multiple timescales and broad
distributions of single-cell timescales even within a single region. Previous empirical investigations have relied
on quantifying timescales of activity using single-cell spike count correlations and by averaged readouts of
activity such as the electrocorticogram (ECoG), because until recently, massively parallel recordings of
populations of single neurons with cell-type information as well as the statistical methods to analyze collective
dynamics were not widely available.
We have developed an analytic framework based on dynamic latent variable models to quantify timescales and
states of cortical dynamics from spiking activity in local populations and from local measures of population activity
(the local field potential, LFP). We propose to apply these methods to a set of publicly accessible recordings of
cortical activity in the mouse to determine (1) the extent to which variation in specific features of the cortical
microcircuitry explains variation in cortical dynamics, (2) the role of specific cell types in determining collective
dynamics, and (3) the connection between activity models across recording modalities
This study is fundamentally about quantitatively mapping normal variation in function and determining the extent
to which that variation is predicted from the local circuit properties. Modeling this relationship accurately will have
a large impact on our ability to predict how neural dynamics arise from changes in microcircuit structure and
could be extended to understand disruption of activity dynamics arising from circuit changes linked to mental
health disorders. Independent of the relationship to microcircuit structure, success of this study will generate a
framework in which variability of cortical dynamics can be accurately and quantitatively mapped across
individuals or in the same individual over time, providing an invaluable tool for the studies of learning and
development.

## Key facts

- **NIH application ID:** 10505552
- **Project number:** 1RF1MH130413-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Audrey Sederberg
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $360,977
- **Award type:** 1
- **Project period:** 2022-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505552

## Citation

> US National Institutes of Health, RePORTER application 10505552, Investigating the microcircuit determinants of neural population activity through comparative analysis of latent dynamics across cortical areas in the mouse (1RF1MH130413-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10505552. Licensed CC0.

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