CRCNS: Understanding Single-Neuron Computation Using Nonlinear Model Optimization

NIH RePORTER · NIH · R01 · $335,714 · view on reporter.nih.gov ↗

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
BAYLOR COLLEGE OF MEDICINE
Principal Investigator
FABRIZIO GABBIANI
Activity code
R01
Funding institute
NIH
Fiscal year
2022
Award amount
$335,714
Award type
1
Project period
2022-08-01 → 2027-07-31