Universal laws and architectures: theory and lessons from brains, nets, hearts, bugs, grids, flows, and zombies

John Doyle ( doyle@…)

See abstract for initial tutorial below. I've also made videos that are a simple intro and cover the same material:


There is a link to this from my website too...

There is a folder called "Telluride Slides" that has the slides from the July 7 talk. I'll put other related stuff there as it gets made. The other folders contain background videos which I would love for you to watch.

The emphasis in the videos is on tutorials and accessibility and questions and minimal math (so far). We've also made huge progress on neuro-relevant math foundations recently and this is not fully reflected in the videos, but this is a top priority so will have something soon, and this is the subject of my CNS talks on July 7-8. The focus throughout is on brains, but other examples come up to concretely illustrate the "universal" aspects of the laws and architectures.

These short videos say how it is (not really very well) organized: 00LookHereFirstVideosSlides.mp4 0OverviewIntroShort.mp4

and then the content starts with more introductory videos in this folder: 0IntroLawsArch

and then there are a few other folders with additional details… hopefully the names will make things fairly clear… The greatest details are in medical physiology and cell biology, with extensive case studies and published papers.

Everything is numbered so that hopefully the ordering is obvious...and also all the slides that are used in the videos are also in the folders somewhere, as are all the papers that are mentioned… you have to download videos otherwise dropbox plays them in preview mode which stops early…

Please have a look at the videos and go as far as you want. If you are still interested, come to the discussions and ask questions. I'm also eager to get feedback on content and presentation.

Tutorial Abstract

This tutorial aims to accessibly introduce a new theory of network architecture relevant to neuroscience, biology, medicine and technology (particularly SDN/NFV and cyberphysical systems), initially minimizing math details. Key ideas are motivated by familiar examples from neuroscience, including live demos using audience brains, and further illustrated with examples from technology and biology. The status of the necessary math will be sketched in as much detail as time permits during my July 7 CNS lecture. Background material and additional details are in online videos (accessible from website above) and a recent blog post: rigorandrelevance.wordpress.com/author/doyleatcaltech.

My research is aimed at developing a more “unified” theory for complex networks motivated by and drawing lessons from neuroscience[4], cell biology[3], medical physiology[9], technology (internet, smartgrid, sustainable infrastructure)[1][8], and multiscale physics [2],[5],[6]. This theory involves several elements: hard limits, tradeoffs, and constraints on achievable robust, efficient performance ( “laws”), the organizing principles that succeed or fail in achieving them (“architectures” and protocols), the resulting high variability data and “robust yet fragile” behavior observed in real systems and case studies (behavior, data, statistics), the processes by which systems adapt and evolve (variation, selection, design), and their unavoidable fragilities (hijacking, parasites, predation, zombies). The final and central element is scalable algorithms to allow study and design of complex networks involving all the other elements.

We will leverage a series of case studies with live demos from neuroscience, particularly vision and sensorimotor control, plus some hopefully familiar and simple insights from medicine, cell biology and modern computer and networking technology. Zombies emerge throughout as a ubiquitous, strangely popular, and annoying system fragility, particularly in the form of zombie science. In addition to the above mentioned blog and videos, papers [1] and [4] (and references therein) are the most accessible and broad introduction while the other papers give more domain specific details. For math details the best place to start is Nikolai Matni’s website (cds.caltech.edu/~nmatni/).

Selected recent references:

[1] Alderson DL, Doyle JC (2010) Contrasting views of complexity and their implications for network-centric infrastructures. IEEE Trans Systems Man Cybernetics—Part A: Syst Humans 40:839-852.

[2] Sandberg H, Delvenne JC, Doyle JC. On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurements, IEEE Trans Auto Control, Feb 2011

[3] Chandra F, Buzi G, Doyle JC (2011) Glycolytic oscillations and limits on robust efficiency. Science, Vol 333, pp 187-192.

[4] Doyle JC, Csete ME (2011) Architecture, Constraints, and Behavior, P Natl Acad Sci USA, vol. 108, Sup 3 15624-15630

[5] Gayme DF, McKeon? BJ, Bamieh B, Papachristodoulou P, Doyle JC (2011) Amplification and Nonlinear Mechanisms in Plane Couette Flow, Physics of Fluids, V23, Issue 6, 065108

[6] Page, M. T., D. Alderson, and J. Doyle (2011), The magnitude distribution of earthquakes near Southern California faults, J. Geophys. Res., 116, B12309, doi:10.1029/2010JB007933.

[7] Namas R, Zamora R, An, G, Doyle, J et al, (2012) Sepsis: Something old, something new, and a systems view, Journal Of Critical Care Volume: 27 Issue: 3

[8] Chen, L; Ho, T; Chiang, M, Low S; Doyle J,(2012) Congestion Control for Multicast Flows With Network Coding, IEEE Trans On Information Theory Volume: 58 Issue: 9 Pages: 5908-5921

[9] Li, Cruz, Chien, Sojoudi, Recht, Stone, Csete, Bahmiller, Doyle (2014) Robust efficiency and actuator saturation explain healthy heart rate control and variability, P Natl Acad Sci USA 2014 111 (33) E3476-E3485