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Limb Coordination in Crustacean Swimming: The Neural Mechanisms and Mechanical ImplicationsMathematical Biology
A fundamental challenge in neuroscience is to understand how
biologically-salient motor behaviors emerge from properties of the
underlying neural circuits. Crayfish, krill, prawns, lobsters and
other long-tailed crustaceans swim by rhythmically moving limbs called
swimmerets. Over the entire biological range of animal size and
paddling frequency, movements of adjacent swimmerets maintain an
approximate quarter-period phase-difference with the more posterior
limbs leading the cycle. We use a computational fluid dynamics model
to show that this frequency-invariant stroke pattern is the most
effective and mechanically efficient paddling rhythm across the full
range of biologically relevant Reynolds numbers in crustacean
swimming. We then show that the organization of the neural circuit
underlying swimmeret coordination provides a robust mechanism for
generating this stroke pattern. Specifically, the wave-like limb
coordination emerges robustly from a combination of the half-center
structure of the local central pattern generating circuits (CPGs) that
drive the movements of each limb, the asymmetric network topology of
the connections between local CPGs, and the phase response properties
of the local CPGs, which we measure experimentally. Thus, the
crustacean swimmeret system serves as a concrete example in which the
architecture of a neural circuit leads to optimal behavior in a robust
manner. Furthermore, we consider all possible connection topologies
between local CPGs and show that the natural connectivity pattern
generates the biomechanically optimal stroke pattern most robustly.
Given the high metabolic cost of crustacean swimming, our results
suggest that natural selection has pushed the swimmeret neural circuit
toward a connection topology that produces optimal behavior.
This work is joint with Calvin Zhang (NYU), Bob Guy (UC Davis),
Brian Mulloney (UC Davis), and Lucy Spardy (Skidmore).
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