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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
Published on: June 21, 2022
Linear versus nonlinear signal transmission in neuron models with adaptation currents or dynamic thresholds
Jan Benda1, Leonard Maler, André Longtin
1Division of Neurobiology, Department Biology II, Ludwig-Maximilians-Universität München, Planegg-Martinsried, Germany. benda@bio.lmu.de
Journal of Neurophysiology
|November 4, 2010
Summary
Spike-frequency adaptation in neurons can be modeled either as an adaptation current or a dynamic threshold. This study reveals adaptation currents cause linear shifts in firing rate curves, while dynamic thresholds have divisive effects, guiding model selection for neuronal dynamics.
Area of Science:
- Computational Neuroscience
- Computational Neuroscience and Biophysics
Background:
- Spike-frequency adaptation is crucial for neuronal signal processing, influencing dynamics from milliseconds to seconds.
- In integrate-and-fire models, adaptation is implemented as either an adaptation current or a dynamic firing threshold.
- The distinction between these modeling approaches for physiological adaptation mechanisms remains unclear.
Purpose of the Study:
- To differentiate the effects of adaptation currents versus dynamic thresholds on neuronal firing properties.
- To provide guidelines for selecting appropriate models for observed spike-frequency adaptation mechanisms.
- To investigate the impact of different adaptation models on neuronal transfer functions.
Main Methods:
- Analysis of the onset f-I (frequency-input current) curves under different adaptation states.
- Comparison of the effects of adaptation currents (subtractive shift) and dynamic thresholds (divisive effect) on f-I curves.
- Simulations of conductance-based spiking models with various adaptation currents (AHP, M-type, KNa) and comparison with integrate-and-fire models.
Main Results:
- Dynamic thresholds exhibit a divisive effect on onset f-I curves, altering their slope with adaptation.
- Adaptation currents cause a subtractive shift in f-I curves, preserving the slope and acting linearly.
- Simulations of conductance-based models align with adaptation current properties, not dynamic threshold properties.
- Slow inactivation of sodium currents is not captured by either model.
Conclusions:
- Adaptation currents are suitable for modeling when lateral shifts in onset f-I curves are observed.
- Dynamic thresholds are appropriate when the slope of onset f-I curves changes with the adaptation state.
- Observed divisive alterations in f-I curves may indicate novel biophysical mechanisms affecting neuronal spike thresholds.
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