# Modeling the structure-function relation in a reconstructed cortical tissue

> **NIH NIH R01** · ALLEN INSTITUTE · 2020 · $1,349,805

## Abstract

Abstract
 How is connectivity between neurons related to patterns of activity exhibited by these neurons in vivo? This
question of structure-function relations in brain circuits is of fundamental importance. Answering it in a
quantitative manner would have far-reaching consequences both for our theories of how brain works and for
applications ranging from better disease treatments to new tools for artificial intelligence. However, our current
understanding of structure-function relations is relatively poor, in large part because the fine structure of
neuronal connectivity has remained largely unknown. In turn, this severely limits connecting modeling to
theoretical efforts. This problem is particularly challenging in the case of studying the highly heterogeneous
cortical circuits, which are involved in important functions like perception, cognition, and learning.
 Fortunately, recent experimental work by our collaborators at the Allen Institute for Brain Science is now
resulting in transformational new datasets that characterize connectivity in the mouse cortical area V1 at the
level of Cell Types using multi-patch synaptic physiology and at the level of individual neurons using electron
microscopy (EM). For the first time in history of neuroscience, we will have connectome of individual neurons
coupled with dense recordings of activity in ~1 mm3 of V1, plus systematic characterization of synaptic
properties.
 We will leverage these unique datasets to build and share with the community new models of V1 and use
them to study the relationships between cortical connectivity and in vivo activity and computations. We will
analyze how multiple features of neuronal code depend on individual cell properties and on higher-order
connectivity motifs, which are present in the EM connectome, but not in the statistics-based connectivity
inferred from sparse measurements at the Cell Types level or from existing literature. We also will evaluate the
consistency of the new models of V1 with predictions made by current theories of structure-function relations.
 These models and simulations will be freely shared with the community as a resource that scientists will
use to guide future experiment designs, improve biological realism in models, and assist in generating and
testing theories. By providing a rich and biologically realistic framework for new theoretical, modeling, and
experimental studies, this resource will fuel new discoveries regarding relations between the structure and
function of cortical circuits.

## Key facts

- **NIH application ID:** 10005712
- **Project number:** 1R01EB029813-01
- **Recipient organization:** ALLEN INSTITUTE
- **Principal Investigator:** ANTON ARKHIPOV
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,349,805
- **Award type:** 1
- **Project period:** 2020-09-14 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005712

## Citation

> US National Institutes of Health, RePORTER application 10005712, Modeling the structure-function relation in a reconstructed cortical tissue (1R01EB029813-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10005712. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*