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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

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Published on: June 24, 2015

A new method to infer higher-order spike correlations from membrane potentials.

Imke C G Reimer¹, Benjamin Staude, Clemens Boucsein

¹Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg, Germany.

Journal of Computational Neuroscience

|March 12, 2013

Summary

This study introduces a novel method to analyze neuronal communication by examining subthreshold membrane potential fluctuations. This approach efficiently infers higher-order spike correlations in large neural populations without needing spike sorting.

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Neuroscience

Background:

Understanding neuronal information processing requires analyzing higher-order spike correlations.
Current methods are limited to analyzing recordings from multiple single neurons.
Analyzing large populations is crucial for comprehensive insights into neural circuits.

Purpose of the Study:

To develop a new method for inferring higher-order spike correlations from subthreshold membrane potential fluctuations.
To enable the study of very large neural populations (thousands of neurons).
To bypass the need for spike sorting in correlation analysis.

Main Methods:

Representing subthreshold membrane potential fluctuations as filtered presynaptic activity using a leaky integrator neuron model.
Adapting the Cumulant Based Inference of Higher-Order Correlations (CuBIC) method to infer maximal correlation order.
Utilizing numerical simulations to validate the method's sensitivity and data requirements.

Main Results:

The new method reliably infers weak higher-order correlations from short membrane potential recordings.
It demonstrates robustness against violations of simplifying assumptions.
The approach is suitable for analyzing large neural populations, up to several thousands of neurons.

Conclusions:

This method offers a powerful tool for investigating functional higher-order statistics in neural networks.
It significantly advances the analysis of neuronal information processing by enabling large-scale population studies.
The technique is applicable to in vivo intracellular recordings, broadening its experimental utility.