A biophysical conductance-based model of neural spike timing and interspike interval correlations

Neurons encode information in the form of spike times. It is argued that the timing between spikes (i.e., spike-timing) encodes the stimulus input. Spike threshold adaptation is speculated to be the major driving force behind a spike-timing code. Efforts to understand the neuron channels that drive an adaptive threshold have relied on purely mathematical models. Such models have had great success at predicting spike times, but do not have a strong basis in biophysics or what is known about neural channels to predict spike timing. State-of-the-art biophysical models, however, suffer at the cost of model interpretability and spike-timing accuracy. We investigate spike threshold adaptation with a biophysical, Hodgkin-Huxley type model in a reduced fashion, with consideration to voltage-gated ion channels present only the axonal hillock, and fit spike timing that outperforms the best phenomenological model. We then evaluate the presence and effect of spike-threshold adaptation by estimating the current-voltage interaction and determine that the Kv7/KCNQ ion channel is likely the major ion channel responsible for an adaptive threshold in rodent pyramidal cells in-vitro. These findings are an alternative approach to previous research investigating outward potassium channels, and partially agree with previous research. We next develop a stochastic extension of the model with consideration of other stochastic models to produce stochastic spike timing and behavior observed in experimental data. We find that a stochastic Kv7/KCNQ ion channel with correlated spike-to-spike noise is able to reproduce neuron variability in experimental data and is consistent with the theoretical parameters derived from a stochastic dynamic threshold model.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Neuroscience

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Asilador, Alexander

Contributors dc:contributor

• Ratnam, Rama
• Jones, Douglas L
• Llano, Daniel A
• Auerbach, Benjamin D

• Biophysical Model
• Neural Coding
• Sensory Neurons
• Temporal Coding

Statement dc:rights

• Copyright 2023 Alexander Asilador

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/121459

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121459