Locomotion-specific Neuron Models

What neuron model should be used for Locomotion tasks? Is there a big difference? Does the increased parameter space overwhelm the gains of autonomous periodicity? That's the topic of An Analysis of Neural Models for Walking Control and, at least for quadraped locomotion, the answer seems to be that more complex neurons are a gain overall.

Neural models that they consider:

• Threshold.
• Sigmoidal.
• Leaky-integrator/Continuous-time, or first-order ODE. (Beer) (note that self-connections are needed for oscillations). Funahashi and Nakamura. Did Beer use plastic weights? Do R&H? Is it advantageous?
• Taga (10). Second-order ODE, basically an extension of leaky-integrator with an additional, coupled 1st-order ODE.
• Ekeberg (12). Three state version, 3rd-order ODE. Developed for analysis of the lamprey spinal cord networks that controlling swimming.

On a similar note, Center-Crossing Recurrent Neural Networks for the Evolution of Rhythmic Behavior shows that Center-crossing CTRNNs work better for locomotion than randomly aligned CTRNNs.

I'd like to see some comparisons between CC-CTRNNs and Taga's and Ekeberg's models as well.

The details of Reeve's and Hallam's setup are in Reeve's PhD Disseration at Edinburgh, Generating Walking Behaviors in Legged Robots.

Finally, what I keep expecting to find is evolution of a linear combination of sine functions. I mean, if you just want a stable walker, wouldn't this work nicely?

Also worth looking at CiTRuS: The Continuous Time Recurrent System, from Robert Vicker at Sussex.