A Novel Method for Quantifying Periodicity and Time Delay in dynamic Neural Networks using unstable sub-action potential threshold depolarisations.

BACKGROUND/OBJECTIVES Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multi-site recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straight forward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveform is unstable (e.g. fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. METHODS Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. CONCLUSIONS The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability¬.