# From ion channels to graph theory in sensorimotor learning > **NIH NIH UF1** · UNIVERSITY OF CHICAGO · 2020 · $3,854,200 ## Abstract Project Summary Mechanistically linking network connectivity and the dynamics of neural networks to variation in the behavior of individuals is an overarching goal of neuroscience. Here we address this goal using techniques from network science to calculate functional networks that summarize pair-wise and higher order interactions between all recorded neurons. Network activity will be assessed using sophisticated two-photon (2P) imaging of activity- dependent Ca2+ signaling optimized to maximize the rate of recording and the numbers of neurons recorded. Multineuronal interactions within the networks will be identified, giving rise to encoding models to predict the network activity. Techniques from statistical physics will be used to optimally couple data from intracellular recordings to biologically realistic Hodgkin-Huxley (HH) models representing the contributions of ion currents and other free model parameters of the individual neurons. Networks of HH neurons using model synapses will replace pair-wise correlations to delinate the interrelationships between the ion currents of individual neurons and network interactions and dynamics. Taking advantage of the birdsong learning model, in the proposed experiments these approaches will be applied to the cortical song system "HVC" nucleus, allowing us to link these scales of investigation directly to behavior. Recent results demonstrate that changes in the intrinsic properties (IP) (ion current magnitudes) of HVC neurons is related to each individual's song, implicating changes within neurons as well as at synapses and networks that are related to learning. Aim 1: fast 2P imaging will be made in brain slices containing HVC that express spontaneous network activity. Model building will be supported by extensive efforts at 3-cell and 4-cell whole cell patch recordings, to better characterize HVC connectivity. The hypothesis that network structure depends on learning will be tested by examining how models vary between individual birds who sang similar or different songs. Models will be extended to in vivo observations by fast 2P imaging in sleeping birds while eliciting fictive singing using song playback, and in singing birds using 1P imaging. Results from the other Aims will further constrain the network and HH model building of Aim 1. Aim 2: the predictive power of the models will be further tested by using cellular resolution 2P optogenetic inhibition of selected neurons in in vivo and in vitro preparations. Aim 3: the role of neuronal IP in shaping network dynamics will be tested by using genetic and viral techniques to transiently modify specific ion channels in specific classes of HVC neurons. Changes in birds' singing behavior will be compared against a predictive HH model relating song structure and ion channel efficacy. Fast 2P imaging in slice and multisite extracellular recordings in singing birds will help to define how IP contribute to network models. Aim 4: single cell gene expression te... ## Key facts - **NIH application ID:** 9950584 - **Project number:** 1UF1NS115821-01 - **Recipient organization:** UNIVERSITY OF CHICAGO - **Principal Investigator:** Genevieve Konopka - **Activity code:** UF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $3,854,200 - **Award type:** 1 - **Project period:** 2020-05-01 → 2024-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9950584 ## Citation > US National Institutes of Health, RePORTER application 9950584, From ion channels to graph theory in sensorimotor learning (1UF1NS115821-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/9950584. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*