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Updated: May 20, 2025

Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
Published on: March 31, 2016
Mechanisms and implications of high depolarization baseline offsets in conductance-based neuronal models
Anal Kumar1, Anzal K Shahul1, Upinder Singh Bhalla1
1National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.
None:
Somatic step-current injection is commonly used to characterize the electrophysiological properties of neurons. Many neuronal types show a large depolarization baseline offset (DBLO), which is defined as the positive difference between the minimum membrane potential during action potential trains and resting. We used stochastic parameter search in experimentally constrained conductance-based models to show that four key factors together account for high DBLO: liquid junction potential correction, high backpropagating passive charges during the repolarization phase of an action potential, fast potassium delayed rectifier kinetics, and appropriate transient sodium current kinetics. Several plausible mechanisms for DBLO, such as Ohmic depolarization due to current input or low-pass filtering by the membrane, fail to explain the effect, and many published conductance-based models do not correctly manifest high DBLO. Finally, physiological levels of DBLO constrain ion channel levels and kinetics, and are linked to cellular processes such as bistable firing, spikelets, and calcium influx.NEW & NOTEWORTHY Our study uncovers mechanisms behind a poorly understood phenomenon-the high membrane potential baseline during depolarization-induced action potential trains. Using data-driven conductance-based pyramidal neuron models, we identify somatic-dendritic electrotonic coupling, potassium channel deactivation kinetics, and sodium channel kinetics as key contributors. We show that ignoring high depolarization baseline leads to incorrect predictions about ion channel levels and neuronal computations. The insights gained from this work will enable more accurate and precise neuronal modeling.
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