# The role of top-down dendritic processing in credit assignment and cortical dynamics

> **NIH NIH R01** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2024 · $692,842

## Abstract

Top-down regulation of cortical processing is critical for learning, attention, figure-ground separation,
multisensory integration, contextual-modulation and many other processes associated with cognition. However,
the biological mechanisms supporting top-down computation remain elusive. A popular and compelling
hypothesis is that top-down computation is implemented via the engagement of apical tuft dendrites in layer 1.
This hypothesis is indirectly supported by convergent anatomical and functional evidence, including observations
in patients with disorders associated with disrupted top-down processing, but more direct evidence linking
dendritic integration to top-down mechanisms is limited. To address this gap, we developed a novel imaging
approach that allows simultaneous recording of somas and dendrites in large populations of neurons during
learning, without signal contamination. We will combine this approach with two complementary behavioral tasks:
a highly controlled Brain-Computer Interface (BCI) paradigm and a comparatively naturalistic virtual navigation
task. Through the set of proposed experiments, we aim to test the overarching hypothesis that top-down
computation is implemented via the engagement of apical tuft dendrites in layer 1, and that these signals are
responsible for guiding learning in networks of neurons. We will do this by: (1) Establishing the relationship
between single-neuron dendritic integration and circuit dynamics and studying the principles governing the
changes in this relationship over the course of learning; (2) Interrogating the relationship between dendritic
activity and behavioral variables, how this relationship is modified over the course of learning, and how dendrites
instruct changes in their corresponding somas; and (3) Testing the hypothesis that dendrites receive vectorized
error signals consistent with an efficient solution to the credit assignment problem. Results from these
experiments will catalyze a new ways of thinking about cortical computation and learning principles in biological
systems, propelling the field into new directions with impactful scientific and translational potential.

## Key facts

- **NIH application ID:** 10777035
- **Project number:** 1R01MH135141-01
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Mark Thomas Harnett
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $692,842
- **Award type:** 1
- **Project period:** 2024-07-18 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10777035

## Citation

> US National Institutes of Health, RePORTER application 10777035, The role of top-down dendritic processing in credit assignment and cortical dynamics (1R01MH135141-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10777035. Licensed CC0.

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