This thesis may be of interest for you:

Brain-Machine Interface for Reaching: Accounting for Target Size, Multiple Motor Plans, and Bimanual Coordination

Ifft, Peter J.. Duke University, ProQuest, UMI Dissertations Publishing, 2014. 3613740.

Beatrice thanks the Ifft dissertation looks nice. By Dec. 2014 

I hope to find observational practices (shape analysis, lateralized movement, and emerging touch-induced movement patterns) where I might quantify individual's motor stereotypies as locally-optimal for each individual. Thus denoting degrees of severity (entrenchment) in complex motor stereotypies. Improved measure of individualized behavior further measures variability witnessed throughout the literature on motor stereotypies. I suspect I will develop a morphological characterization that bridges Arber's interleaving organization and Chalfie's touch-induced model. This work will rely upon in vivo and in vitro (microfluidics) platforms to conduct such observations across scales. Moving from the morphological to the cellular circuit is part of arriving at direct interfaces with biological substrates, perhaps.

One of the challenges of timing in neural "circuitry," as you've put it, is that the timing is a bit hairy to get a hold on, statistically. Among the issues following from this challenge is the divergence of time scale (e.g., "critical slowing down"), often exhibited as a fractal temporal structure.

Recent work (http://www.ncbi.nlm.nih.gov/pubmed/24387548) has suggested that the fractal divergence of timing of the neural dynamics may be due to architectural flexibility, specifically the case of degeneracy. Of course, we can find and/or instantiate degeneracy in "circuitry," but to my mind, degeneracy invites some reflection about whether or not, or within what bounds, the model concept of "circuitry" is helpfully consonant with neural dynamics.

Best wishes,

Damian

Damian, Agreed! Thanks for your feedback! I look forward to seeing your work in the future.

The interest in the divergence of time scale... I would term as the challenge to arrive at a cross-scale analysis. Agreed regarding the fractal analysis as a non-literal form of analysis using self-similarity within a heterogenous dataset. But under what conditions do we conduct our measurement? How do we use in vitro models while maintaining behavioral inquiry?

How do we read signal? Do we look at performance or the supply-side, that is, the "allocation of resources" within an organisms motor activity and behavior? On the one hand there is the setup of the measure as listed above. Damian below there is the issue of paying equal attention to the mode of inquiry.

 Marder (2011)  and Marder and Taylor (2011)(http://www.nature.com/neuro/journal/v14/n2/full/nn.2735.html)

 address how to experimentally pursue what is essentially a system-wide problem. While there is the question what instrumentation will help us to clarify cross-scale analysis. This involves moving from nanoscience platforms (microfluidics) to morphological behavior (e.g. undulatory movement in c. elegans or bending leeches).  Have you seen Kelso' paper on multistability and metastability (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3282307/)? He has something to say about degeneracy. 

Since my posting I have found the standard computational neuroscience literature since Wolpert might benefit from biophysical models as in Stephens et al. (2008)

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1000028

or 

multi-organ/innervated skin models studied through in vivo assays (body-on-the -chip) at UCF Hybrid Systems Lab.

http://nanoscience.ucf.edu/hickman/

http://nanoscience.ucf.edu/hickman/publications.php