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Spike patterning in oxytocin neurons: Capturing physiological behaviour with Hodgkin-Huxley and integrate-and-fire models
- Source :
- PLoS ONE, Vol 12, Iss 7, p e0180368 (2017), PLoS ONE, Leng, T, Leng, G & MacGregor, D J 2017, ' Spike patterning in oxytocin neurons : Capturing physiological behaviour with Hodgkin-Huxley and integrate-and-fire models ', PLoS ONE, vol. 12, no. 7, pp. e0180368 . https://doi.org/10.1371/journal.pone.0180368
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- Integrate-and-fire (IF) models can provide close matches to the discharge activity of neurons, but do they oversimplify the biophysical properties of the neurons? A single compartment Hodgkin-Huxley (HH) model of the oxytocin neuron has previously been developed, incorporating biophysical measurements of channel properties obtained in vitro. A simpler modified integrate-and-fire model has also been developed, which can match well the characteristic spike patterning of oxytocin neurons as observed in vivo. Here, we extended the HH model to incorporate synaptic input, to enable us to compare spike activity in the model with experimental data obtained in vivo. We refined the HH model parameters to closely match the data, and then matched the same experimental data with a modified IF model, using an evolutionary algorithm to optimise parameter matching. Finally we compared the properties of the modified HH model with those of the IF model to seek an explanation for differences between spike patterning in vitro and in vivo. We show that, with slight modifications, the original HH model, like the IF model, is able to closely match both the interspike interval (ISI) distributions of oxytocin neurons and the observed variability of spike firing rates in vivo and in vitro. This close match of both models to data depends on the presence of a slow activity-dependent hyperpolarisation (AHP); this is represented in both models and the parameters used in the HH model representation match well with optimal parameters of the IF model found by an evolutionary algorithm. The ability of both models to fit data closely also depends on a shorter hyperpolarising after potential (HAP); this is explicitly represented in the IF model, but in the HH model, it emerges from a combination of several components. The critical elements of this combination are identified.
- Subjects :
- 0301 basic medicine
Physiology
Single compartment
Peptide Hormones
Evolutionary algorithm
Action Potentials
lcsh:Medicine
Oxytocin
Bioinformatics
Synaptic Transmission
Biochemistry
0302 clinical medicine
Animal Cells
Medicine and Health Sciences
lcsh:Science
gamma-Aminobutyric Acid
Internal Ribosome Entry Site
Neurons
Neurotransmitter Agents
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Model representation
Neurochemistry
Electrophysiology
medicine.anatomical_structure
Physical Sciences
Excitatory postsynaptic potential
Spike (software development)
Cellular Types
Neurochemicals
Biological system
Supraoptic Nucleus
Algorithms
Vasopressin
Research Article
medicine.drug
Models, Neurological
Glutamic Acid
Neurophysiology
Biology
Research and Analysis Methods
Membrane Potential
Microbiology
03 medical and health sciences
Virology
Journal Article
medicine
Animals
Computer Simulation
Genetic Algorithms
lcsh:R
Biology and Life Sciences
Excitatory Postsynaptic Potentials
Cell Biology
Hormones
Viral Replication
Rats
Hodgkin–Huxley model
030104 developmental biology
Cellular Neuroscience
Synapses
lcsh:Q
Neuron
Software
Mathematics
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....74f14b6c70bdb53f2c9559c70216e5e7