Measuring spike pattern reliability with the Lempel-Ziv-distance.

Area of Science:

• Computational Neuroscience
• Systems Neuroscience

Background:

• Spike train distance measures are crucial for assessing neuronal firing reliability and classifying neural activity.
• Existing methods often require arbitrary parameters and can be computationally intensive.

Purpose of the Study:

• Introduce a novel, parameter-free, and computationally efficient spike train distance measure based on Lempel-Ziv complexity.
• Evaluate the utility of this LZ-distance for determining neuronal firing reliability and classifying spike trains.

Main Methods:

• Developed a Lempel-Ziv-distance (LZ-distance) metric for spike train analysis.
• Determined in vivo firing reliability by comparing spike train distances to a Poisson reference.
• Applied LZ-distance and a coincident firing distance to macaque visual pathway data (LGN, V1, MT).
• Utilized sequential superparamagnetic clustering in conjunction with the LZ-distance.

Main Results:

• The LZ-distance effectively measures neuronal firing reliability and classifies spike trains without arbitrary parameters.
• It reveals patterns and timing reliability in neural firing along the visual pathway.
• Clustering with LZ-distance groups spike trains with similar, not just synchronized, firing patterns.
• LZ-distance provides unique insights where other measures fail.

Conclusions:

• The LZ-distance offers a novel, efficient, and robust approach to analyzing spike train data.
• It enhances the understanding of neuronal firing reliability and neural coding.
• This method facilitates more nuanced neuron classification, particularly in complex datasets.