Separating burst from background spikes in multichannel neuronal recordings using return map analysis.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Signal Processing

Background:

• Neuronal recordings often contain high background spike activity, hindering accurate burst detection.
• Existing burst detection methods struggle with nonstationary and noisy data, limiting their effectiveness.
• Interspike interval (ISI) distributions can be bimodal in stationary recordings, aiding spike separation.

Purpose of the Study:

• To introduce a novel preprocessing method for separating neuronal bursts from background spikes.
• To enhance the reliability of burst detection algorithms in both simulated and real-world neuronal recordings.
• To enable robust analysis of neuronal network activity under conditions of nonstationarity and high background noise.

Main Methods:

• Utilized single and multichannel neuronal activity data.
• Employed the interspike interval (ISI) return map for spike separation.
• Compared a fixed heuristic (2-step) thresholding method with an iterative data-driven threshold estimation.
• Validated the method using a stochastic model and primary cortical neuron recordings on multielectrode arrays.

Main Results:

• The proposed preprocessing method significantly improved the reliability of established burst detection algorithms.
• Effective separation of burst and background spikes was achieved, even with noisy and nonstationary data.
• The iterative threshold selection method demonstrated superior performance in distinguishing spike types.

Conclusions:

• The developed preprocessing technique offers a robust solution for separating neuronal bursts from background activity.
• This method enhances the reliability of burst detection, facilitating the study of neuronal network dynamics.
• It provides a valuable tool for investigating the impact of diseases or pharmacological interventions on neuronal activity patterns.