Evolution and analysis of model CPGs for walking: II. General principles and individual variability

Are there general principles for pattern generation? We examined this quest ion by analyzing the operation of large populations of evolved model centra l pattern generators (CPGs) for walking. Three populations of model CPGs we re evolved, containing three, four, or five neurons. We identified six gene ral principles. First, locomotion performance increased with the number of interneurons. Second, the top 10 three-, four-, and five-neuron CPGs could be decomposed into dynamical modules, an abstract description developed in a companion article. Third, these dynamical modules were multistable: they could be switched between multiple stable output configurations. Fourth, th e rhythmic pattern generated by a CPG could be understood as a closed chain of successive destabilizations of one dynamical module by another. A combi natorial analysis enumerated the possible dynamical modular structures. Fif th, one-dimensional modules were frequently observed and, in some cases, co uld be assigned specific functional roles. Finally, dynamic dynamical modul es, in which the modular structure itself changed over one cycle, were freq uently observed. The existence of these general principles despite signific ant variability in both patterns of connectivity and neural parameters was explained by degeneracy in the maps from neural parameters to neural dynami cs to behavior to fitness. An analysis of the biomechanical properties of t he model body was essential for relating neural activity to behavior. Our s tudies of evolved model circuits suggest that, in the absence of other cons traints, there is no compelling reason to expect neural circuits to be func tionally decomposable as the number of interneurons increase. Analyzing ide alized model pattern generators may be an effective methodology for gaining insights into the operation of biological pattern generators.