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


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
Issue number2
StatePublished - May 2023


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

All Science Journal Classification (ASJC) codes

  • Sensory Systems
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience


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