Firing rate control of a neuron using a linear proportional-integral controller

Area of Science:

• Computational Neuroscience
• Electrophysiology
• Systems Neuroscience

Background:

• Electrophysiology experiments, such as measuring phase response curves (PRCs), require stable neuronal firing rates.
• Neurons exhibit period variations, including rapid jitter and slow drift, which complicate long-term recordings and accurate measurements.
• Existing methods like averaging can mitigate jitter but not slow drift over experimental timescales.

Purpose of the Study:

• To design and implement a linear proportional-integral (PI) controller to compensate for neuronal firing rate drift.
• To maintain a desired interspike interval (ISI) by dynamically adjusting the applied current.
• To improve the accuracy and duration of electrophysiology experiments, particularly for PRC estimation.

Main Methods:

• A linear PI controller was designed using a first-order discrete model relating ISI to applied current.
• The controller was tested on pyramidal cells in the hippocampal formation, comparing open-loop and closed-loop conditions.
• An auto-tuning algorithm was implemented for real-time estimation of controller parameters.
• The system operates on the Real-Time eXperiment Interface (RTXI) software platform.

Main Results:

• The PI controller effectively reduced transient times to reach the desired ISI.
• Neuronal firing rate drift was successfully removed, enabling stable, long-duration experiments.
• The controller diminished ISI variance by eliminating slow drift, leading to improved data reliability.
• Enhanced accuracy in phase response curve estimation was demonstrated using the closed-loop system.

Conclusions:

• The developed PI controller provides an effective solution for stabilizing neuronal firing rates in electrophysiology.
• This method significantly enhances experimental efficiency and data quality by mitigating drift and reducing variance.
• The controller and auto-tuning algorithm offer a valuable tool for precise neuronal recordings and advanced analyses like PRC mapping.