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Optimizing computer models of corticospinal neurons to replicate in vitro dynamics.
Samuel A Neymotin1, Benjamin A Suter2, Salvador Dura-Bernal3
1Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Medical Center, Brooklyn, New York; samn@neurosim.downstate.edu.
Journal of Neurophysiology
|October 21, 2016
Summary
We created computer models of corticospinal neurons (SPI) that accurately replicate their in vitro electrical activity. These models reveal how dendritic properties influence neuronal resonance, potentially tuning them to specific cortical oscillations for motor control.
Area of Science:
- Computational Neuroscience
- Motor Cortex Physiology
- Neuronal Modeling
Background:
- Corticospinal neurons (SPI) in motor cortex layer 5B exhibit unique electrophysiological properties in vitro, including hyperpolarization-induced sag and a linear frequency-current relationship.
- Understanding these properties is crucial for modeling motor cortex function and output.
- Existing models may not fully capture the complex dynamics of SPI neurons.
Purpose of the Study:
- To develop and validate computational models of corticospinal neurons (SPI) that accurately reproduce their in vitro electrophysiological characteristics.
- To investigate the role of dendritic properties, specifically the gradient of Ih, in shaping neuronal resonance and responsiveness.
- To explore the implications of these modeled dynamics for neural information processing in the motor cortex.
Main Methods:
- Utilized electrophysiological data from SPI neurons to construct detailed and simplified multi-compartment computer models.
- Employed PRAXIS and evolutionary multiobjective optimization (EMO) to determine ion channel conductances and refine model parameters.
- Analyzed model resonance properties and explored the impact of simulated dendritic Ih gradients.
Main Results:
- Developed archives of validated SPI neuron models (detailed and simplified) that closely match experimental data, including firing patterns and sag responses.
- Identified tradeoffs in model fitting, indicating no single 'best' model but rather usage-specific optimal models.
- Demonstrated that a proximal-to-distal gradient of Ih in dendrites leads to a gradient of resonance frequencies, with higher frequencies distally.
Conclusions:
- The developed SPI neuron models provide valuable tools for exploring single-neuron and network dynamics in the motor cortex.
- The gradient of dendritic resonance properties may enable SPI neurons to selectively respond to oscillations in different cortical layers.
- This layer-specific resonance could play a role in coordinating motor output by integrating information processed at various cortical depths.
