# Connectivity principles underlying network dynamics and learning

> **NIH NIH K99** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $125,442

## Abstract

PROJECT ABSTRACT
 If an organism performs an action that leads to a desired outcome, it is able to perform that action again in
the future in order to obtain that same outcome. While work on the mechanisms of reinforcement learning has
extensively studied how the brain learns certain actions are more valuable than others, there is little knowledge
about how the brain actually re-enters neural states on-demand to produce the behavior that leads to
the desired outcome. This is a central question in neuroscience which underlies learning, memory, and
movement and has implications for therapies to restore these abilities including brain-machine interfaces. It is
believed that connectivity between neurons gives rise to dynamics—rules for how the brain transitions between
neural states—and that modification of connectivity enables learning to re-enter neural states. However, two
main experimental challenges have impeded direct investigation: 1) measuring and manipulating connectivity
between neurons in vivo, and 2) identifying the neurons and activity patterns generating a behavior.
 In this proposal, I will overcome these challenges using 1) 2-photon microscopy to measure and
manipulate functional connectivity in vivo by photostimulating individual targeted neurons and measuring the
network’s response, and 2) a brain-machine interface (BMI) paradigm to define how neural activity is
transformed into behavior and reinforcement. Through experiments that apply these techniques based on
novel models of network dynamics, my proposal seeks principles for how functional connectivity
underlies network dynamics and enables learning in motor cortex, a critical region for generating
movement. In the first Aim (K99), I will determine whether a model of network dynamics predicts functional
connectivity and how patterned photostimulation propagates through connectivity to modify the network state.
In Aim 2 (K99/R00), I will design a BMI to study whether functional connectivity constrains learning. The BMI
will test whether it is easier to learn network states that can be entered through photostimulation propagation. I
will also determine whether changes in functional connectivity support learning by testing whether
photostimulation more easily propagates to enter learned network states. Finally, in Aim 3 (R00), I will reveal
principles for how network activity can change network connectivity and dynamics. I will test different protocols
for stimulating spatiotemporal patterns and reveal principles of stimulation protocols that change the network.
 During the K99, this work will be conducted in the collaborative Zuckerman Institute for Brain and Behavior
at Columbia University with the mentorship of Dr. Rui Costa - expert in the neurobiology of action and Dr. Liam
Paninski – expert in computational modeling, and with the collaboration of Dr. Darcy Peterka – expert in optics
and 2-photon microscopy with photostimulation. I believe their technical and professional mento...

## Key facts

- **NIH application ID:** 10507579
- **Project number:** 1K99NS128250-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Vivek Athalye
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $125,442
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10507579

## Citation

> US National Institutes of Health, RePORTER application 10507579, Connectivity principles underlying network dynamics and learning (1K99NS128250-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10507579. Licensed CC0.

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