# Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model

> **NIH NIH F31** · UNIVERSITY OF WASHINGTON · 2020 · $41,894

## Abstract

Project Summary / Abstract
Cortical stimulation has become a prevalent therapy for various ailments including stroke and traumatic brain
injury. Although targeted plasticity using closed-loop stimulation paradigms has been extensively characterized
in vitro, mechanisms in vivo remain to be further explored. Previous results have also been difficult to compare
due to differences in stimulation methods, brain regions, implants, and animal models. Thus, we will incorporate
a integrate-and-fire (IF) spiking neural network model in conjunction with experimental validation to methodically
compare and uncover novel conditioning paradigms as well as better understand how stimulation affects neural
circuitry. The simplicity and computational efficiency of the IF neural network model allows us to quickly simulate
hundreds of interconnected neurons. The model will be initialized with a spike-timing dependent plasticity (STDP)
rule that reflects previous findings reported from the laboratory, as experimental results strongly suggest classic
STDP mediates stimulus-induced plasticity in vivo. The proposed experiments will seek to optimize spike-
triggered stimulation by discerning whether various stimulus parameters, including number of pulses and
stimulation frequency, affect the induced plasticity. They will also explore novel conditioning protocols such as
gamma-triggered stimulation and brain-state dependent stimulation to determine if population-based paradigms
could be more effective methods of inducing plasticity. I will conduct these studies within the primary motor cortex
of intact macaques for greatest clinical relevance. Experiments will be performed in conjunction with the IF neural
network model simulations to coevolve the stimulation parameters and model architecture. The results of this
study will provide a framework for comparing stimulation methods and inform clinical applications of cortical
stimulation.
Through these projects the PI will be trained in a wide array of disciplines including experimental techniques with
behaving non-human primates, analytical techniques involving circuit level analyses, and computational
modeling skills. Beyond science, he will also learn how to communicate effectively as a research scientist and
continue participating in the neuroscience community through collaborations with different laboratories and
involvement with various research centers. The research training will take place in the Fetz laboratory at the
University of Washington with the facilities, equipment, and resources made available through the Department
of Bioengineering, Department of Physiology & Biophysics, and the Washington National Primate Research
Center.

## Key facts

- **NIH application ID:** 10065860
- **Project number:** 1F31NS118781-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Richy Yun
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $41,894
- **Award type:** 1
- **Project period:** 2020-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10065860

## Citation

> US National Institutes of Health, RePORTER application 10065860, Optimization of stimulation-induced cortical plasticity using an integrate-and-fire spiking neural network model (1F31NS118781-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10065860. Licensed CC0.

---

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