Classification of rhythmic patterns in the stomatogastric ganglion

A large class of neural pattern generators change their rhythmic output under the influence of neuromodulators. We present a method for identifying the variety of rhythmic patterns generated by small neural networks. The technique provides a tool for investigating the biological mechanisms responsible for pattern generation and pattern switching. Discrete methods based on transition graphs are applied to dynamic biological networks to generate sets of possible rhythmic behaviours. A measure is introduced onto the set of rhythms to quantify their differences and organize the set according to clusters of similar rhythms. Each cluster represents a different operational mode of the network. Examples are drawn from the stomatogastric ganglion, a well studied network that controls the muscles in the foregut of crustaceans. Classes of rhythms are found that correspond to experimentally observed patterns, and other classes of rhythms are found that have not yet been observed. Predictions are made for the rhythmic output of the stomatogastric ganglion under specific manipulations of parameters in the biological network.