Chaos in the crayfish sixth ganglion
Xing Pei and Frank Moss
Center for Neurodynamics,
University of Missouri at St. Louis,
St. Louis, MO 63121 USA
Efforts to detect chaos and other low dimensional behavior in biological systems have been reported for more than a decade. However, most of these results have been questioned, primarily because most if not all biological data files are contaminated with large fractions of high dimensional random behavior or "noise". Tradidional methods for detecting chaos, such as measurements of the fractal dimension or the Lyapunov exponents, are highly susceptible to noise contamination and thus do not yield reliable results. Now a new method has emerged based on the detection of "rare" events which, if detected with statistical reliability, nevertheless indicate the presence of chaos or other low dimensional behavior. Files are searched for "signatures" of events based on their phase plane topologies. We show that the method easily distinguishes between noisy limit cycles and unstable periodic orbits (indicating chaos) even when these attractors are contaminated with as much as 100% noise [1]. Demonstrations are presented using experimental data from the crayfish caudal photoreceptor neuron in the sixth ganglion [2].