# CRCNS: Understanding Single-Neuron Computation Using Nonlinear Model Optimization

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2022 · $335,714

## Abstract

PROJECT SUMMARY (See instructions}:
The long-term objective of this proposal is to integrate the design of experiments and computational
large-scale parameter estimation to advance the understanding of a central problem in neuroscience: How
and whether the subcellular localization of ion channels in dendritic compartments contributes to single
neuron computation. This problem can only be addressed using neurons whose biophysical properties are
well characterized and whose role in the processing of sensory information and the generation of behavior
is well understood. The focus will be on three channel types that play a key role in synaptic integration:
hyperpolarization-activated, cyclic nucleotide-gated, mixed sodium/potassium channels, transient dendritic
potassium channels, and calcium channels involved in the generation of burst firing. The specific aims will
focus (i) on determining how these channels, in particular calcium channels, contribute to the dendritic
excitability of hippocampal pyramidal cells; and (ii) on determining how the same channels contribute to
visual object segmentation in collision detecting neurons. Additionally, the project will (iii) develop a
broader, integrated large-scale modeling optimization framework to study the impact of channel localization
on dendritic computation. The application of this framework in the two systems studied will allow (iv) to
compare channel distributions obtained by model optimization to experimentally derived ones, thus
shedding light on their role in neuronal information processing. A final specific aim will be (v) to disseminate
the newly developed optimization methods to a broader audience allowing the wide application of state-ofthe-
art mathematical knowledge in neuroscience research. The project will apply advanced mathematical
methods centered on second-order optimization algorithms based on multiple-shooting or a collocation
discretization of the dynamical system associated with the modeled neurons. The project will also use
electrophysiological, and immunostaining anatomical techniques to determine subcellular channel
localization experimentally. Overall, the project will contribute to advance the fundamental knowledge on
how subcellular channel localization contributes to the processing of information within individual neurons.

## Key facts

- **NIH application ID:** 10612187
- **Project number:** 1R01NS130917-01
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** FABRIZIO GABBIANI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $335,714
- **Award type:** 1
- **Project period:** 2022-08-01 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10612187

## Citation

> US National Institutes of Health, RePORTER application 10612187, CRCNS: Understanding Single-Neuron Computation Using Nonlinear Model Optimization (1R01NS130917-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10612187. Licensed CC0.

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