Limb Coordination in Crustacean Swimming: The Underlying Neural Mechanisms and Fluid Dynamics

Crayfish, krill, prawns, lobsters and other similar crustaceans swim forward by moving pairs of limbs called swimmerets rhythmically through cycles of power-strokes and return-strokes. Independent of paddling frequency, movements of adjacent limbs maintain an approximate quarter-period (0.25) phase-difference with the more posterior limbs leading the cycle. This natural tail-to-head metachronal wave of swimmeret rowing raises two questions: (i) Is this tail-to-head metachrony a biomechanically optimal limb stroke pattern for crayfish swimming? (ii) What is the underlying neural mechanism for producing this tail-to-head metachrony? Using an immersed-boundary-type computational fluid dynamics model, we show that the natural tail-to-head metachronal wave of swimmeret rowing with 0.25 inter-limb phase-differences produces maximal flux and near maximal swimming efficiency compared to other hypothetical stroke patterns over a wide range of biologically relevant Reynolds numbers.;Using a coupled phase model based on recent experimental data on the structure of the crayfish swimmeret neural circuit, we show that the tail-to-head metachrony with 0.25 inter-limb phase-differences emerges robustly from (1) the half-centered structure of the crayfish swimmeret CPG, (2) the asymmetric network topology of the connections between the CPGs and (3) the generic phase response properties of the crayfish swimmeret CPG.;To further understand the biophysical and dynamical mechanisms of the crayfish swimmeret CPG that lead to the required phase response properties for generating the tail-to-head metachrony, we examine the phase response properties of half-center oscillators (HCOs) modeled by a pair of Morris-Lecar-type neurons connected by strong fast inhibitory synapses. We find that the two basic HCO mechanisms, "release" and "escape", give rise to strikingly different phase response curves (PRCs). By analyzing the phase space structure, we identify the dynamical mechanisms shaping HCOs' PRCs.;It is generally believed that neural circuits have evolved to optimize behavior that ultimately increases the animals' reproductive fitness. Despite this general belief, few studies have clearly identified the neural mechanisms producing optimal behaviors. This dissertation provides a concrete example in which the architecture of a neural circuit together with circuit dynamics lead to optimal behavior in a robust manner.