# Cracking Genetically Defined Neocortical Circuits across Learning and Behavior

> **NIH NIH DP2** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $62,698

## Abstract

PROJECT SUMMARY
How does our genome instruct the circuit-level neural computations that give rise to cognition? This is a
fundamental question that aims to explain our cognitive capacity as humans. It requires a deep understanding
for how gene expression in individual neurons relates to activity patterns across the population during behavior.
Comprehensively integrating molecular, anatomical, functional, and behavioral measurements is the main
technical challenge in achieving such an understanding. Here, I propose to determine how circuit-level
implementations of gene expression define specific neural computations and learning rules in the neocortex. In
the course of my work I will seek to address the following questions:
 1) How pervasive are genetically defined circuit motifs in the neocortex and what do they compute?
 2) How are activity-regulated genes induced and expressed across neocortical circuits during learning?
Addressing this requires innovative approaches to characterize circuit components and their functional
interactions. To this end, I will combine large-scale single-cell functional imaging and transcriptional profiling
into an integrated methodological platform called CRACK (Comprehensive Readout of neuronal Activity and Cell
type marKers). I will use this platform to “crack” genetically defined neural circuits underlying sensorimotor
integration, higher-level sensory processing, and decision making in the mouse whisker sensorimotor system. I
will first apply CRACK in a forward screen to identify novel circuit motifs by characterizing unique functional
relationships between known cell types and validating their connections with subsequent anatomical measures.
I will then apply this platform to survey the relationship for how learning drives plasticity in cortical circuits by
generating intersectional circuit maps of activity patterns and immediate early gene expression in defined cell
types. Through these projects, I aim to accelerate the discovery of core circuits underlying learning and behavior
in the mammalian neocortex.

## Key facts

- **NIH application ID:** 10561327
- **Project number:** 3DP2NS111134-01S1
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** Jerry L Chen
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $62,698
- **Award type:** 3
- **Project period:** 2018-09-30 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10561327

## Citation

> US National Institutes of Health, RePORTER application 10561327, Cracking Genetically Defined Neocortical Circuits across Learning and Behavior (3DP2NS111134-01S1). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10561327. Licensed CC0.

---

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