A biophysical and statistical modeling paradigm for connecting neural physiology and function

Nathan G. Glasgow, Yu Chen, Alon Korngreen, Robert E. Kass, Nathan N. Urban

Research output: Contribution to journalArticlepeer-review

Abstract

To understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between spiking patterns and the stimuli they encode. We used public biophysical models of two morphologically and functionally distinct projection neuron cell types: mitral cells (MCs) of the main olfactory bulb, and layer V cortical pyramidal cells (PCs). We first simulated sequences of action potentials according to certain stimuli while scaling individual ion channel conductances. We then fitted point process generalized linear models (PP-GLMs), and we constructed a mapping between the parameters in the two types of models. This framework lets us detect effects on stimulus encoding of changing an ion channel conductance. The computational pipeline combines models across scales and can be applied as a screen of channels, in any cell type of interest, to identify ways that channel properties influence single neuron computation.

Original languageEnglish
Pages (from-to)263-282
Number of pages20
JournalJournal of Computational Neuroscience
Volume51
Issue number2
DOIs
StatePublished - May 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

Funding

The authors have no conflicts of interest relevant to the content of this article. This work was supported by the Binational Science Foundation/National Science Foundation Collaborative Research in Computational Neuroscience program BSF-NSF CRCNS (BSF no. 2013905, NSF no. 1622977 to AK, REK, and NNU), the US National Institutes of Health grant F32 DC016775 (NGG) National Institute of Mental Health (MH RO1 064537 to YC, RK), and National Institute on Drug Abuse (T90 DA022762 to YC). This research was supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. We specifically acknowledge the assistance of Kim Wong.

FundersFunder number
National Science Foundation1622977
National Institutes of HealthF32 DC016775
National Institute of Mental HealthMH RO1 064537
National Institute on Drug AbuseT90 DA022762
Bloom's Syndrome Foundation2013905
University of Pittsburgh
Nanjing Normal University
United States-Israel Binational Science Foundation

    Keywords

    • Compartmental Hodgkin-Huxley model
    • Point process GLM
    • Single neuron stimulus encoding

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