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Updated: May 26, 2026
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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
Published on: June 24, 2015
Nonlinear dynamic modeling of neuron action potential threshold during synaptically driven broadband intracellular
Ude Lu1, Shane M Roach, Dong Song
1Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA 90089, USA. ulu@usc.edu
IEEE Transactions on Bio-Medical Engineering
|December 14, 2011
Summary
Neuronal action potential (AP) thresholds vary with activity, impacting neural coding. This study models these dynamic thresholds, improving spike prediction accuracy by 33% in a computational neuron model.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Computational Biology
Background:
- Activity-dependent variation in neuronal thresholds is crucial for transforming synaptic input into output spike trains.
- Understanding these dynamic thresholds is key to modeling neural information processing.
Purpose of the Study:
- To model the nonlinear dynamics of neuronal action potential (AP) threshold variation during synaptically driven activity.
- To develop and validate a method for measuring AP thresholds from intracellular recordings.
- To improve the accuracy of single neuron models in predicting spike trains.
Main Methods:
- Recorded membrane potentials from CA1 pyramidal cells under broadband stimulation.
- Developed a method to measure AP thresholds by analyzing third-order derivatives of membrane potentials.
- Constructed a nonlinear dynamical third-order Volterra model to capture threshold dynamics.
Main Results:
- The developed method accurately measured AP thresholds, revealing nonlinear dynamics.
- The Volterra model successfully predicted threshold variations based on preceding AP activity.
- Integrating the dynamic threshold model into a single neuron model improved spike prediction by 33%.
Conclusions:
- Neuronal AP threshold dynamics are nonlinear and activity-dependent.
- A Volterra model can accurately capture and predict these dynamic threshold changes.
- Incorporating dynamic thresholds significantly enhances the predictive power of computational neuron models.
Related Concept Videos
01:19The Role of Ion Channels in Neuronal Computation
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
01:41Action Potentials
Overview
01:14Action Potential
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
01:14Action Potential
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
01:28Integration of Synaptic Events
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
01:23Propagation of Action Potentials
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...