September 11, 2017

This Concludes This Version of Google Summer of Code

I am happy to announce that the DevoWorm group's first Google Summer of Code student has successfully completed his project! Congrats to Siddharth Yadav from IIT-New Delhi, who completed his project "Image Processing with ImageJ (segmentation of high-resolution images)".

Our intrepid student intern

His project completion video is located on the DevoWorm YouTube channel. This serves as a counterpart to his "Hello World" video at the beginning of the project. The official project repo is located here. Not only did Siddharth contribute to the data science efforts of DevoWorm but also contributed to the OpenWorm Foundation's public relations committee.

Screenshot from project completion video

As you will see from the video, a successful project proceeds by organizing work around a timeline, and then modifying that timeline as roeadblocks and practical considerations are taken into account. This approach resulted in a tool that can be used by a diverse research community immediately for data extraction, or build upon in the form of future projects. 

In terms of general advice for future students, communicate potential problems early and often. If you get hung up on a problem, put it aside for awhile and work on another part of the project. As a mentor, I encourage students to follow up on methods and areas of research that is most successful in their hands [1]. In this way, students can find and build upon their strengths, while also achieving some level of immediate success. 

[1] This seems like a good place to plug the Orthogonal Research Lab's Open Career Development project. In particular, check out our laboratory contribution philosophy.

August 25, 2017

Live streaming of Orthogonal Lab content

Research live-streaming: an experiment in content [1].

The Orthogonal Research Laboratory, in conjunction with the OpenWorm Foundation, is starting to experiment with live video content. We are using YouTube Live, and live streams (composed in Xsplit Broadcaster) will be archived on the Orthogonal Lab YouTube channel. The intial forays into content will focus on research advances and collaborative meetings, but ideas for content are welcome. 


[1] obscure reference of the post: a shot of Felix the Cat, whose likeness was used to calibrate early experimental television broadcasts.

August 3, 2017

War of the Inputs and Outputs

Earlier this Summer, I presented a talk on sub-optimal cybernetic systems at NetSci 2017. While the talk was a high-level mix of representational modeling and computational biology, there were a few loose ends for further discussion.

One of these loose ends involves how to model a biological system with boxes and arrows when biology is a multiscale, continuous process in both space and time [1]. While one solution is to add as much detail as possible, and perhaps even move to hybrid multiscale models, another solution involves the application of philosophy.

In the NetSci talk, I mentioned in passing a representational technique called metabiology. Our group has recently put out a preprint on the cybernetic embryo in which the level of analysis is termed metabiological. In a metabiological representation, the system components do not need to map isomorphically to the biological substrate [2]. Rather, the metabiological representation is a model of higher-order processes that result from the underlying biology.

From a predictive standpoint, this method is imprecise. It does, however, get around a major limitation of black box models -- namely what a specific black box is representative of. It makes more sense to black box an overarching feature or measurement construct than to constrain biological details to artificial boundaries.

A traditional cybernetic representation of a nonlinear loop. Notice that the boxes represent both specific (sensor) and general (state of affairs) phenomena.

The black box also changes through the history of a given scientific field or concept. In biology, for example, the black box is usually thought of as something to ultimately break apart and understand. This is opposed to understanding how the black box serves as a variable that interacts with a larger system. So it might seem odd to readers who assume a sort of conceptual impermanence by the term "black box".

A somewhat presumptuous biological example: in the time of Darwin, heredity was considered to be a black box. In the time of Hunt Morgan, a formal mechanism of heredity was beginning to be understood (chromosomes), but the structure was a black box. By the 1960s, we were beginning to understand the basic function and structure of genetic transmission (DNA and gene expression). Yet at each stage in history, the "black box" contained something entirely different. In a fast moving field like cell biology, this becomes a bit more of an issue.

A realted cultural problem in biology has involves coming to terms with generic categories. This goes back to Linnean classification, but more generally this applies to theoretical constructs. For example, Alan Turing's morphogen concept does not represent any one biological agent, but a host of candidate molecules that fit the functional description. Modern empirical biology involves specification rather than generalization, or precisely the opposite goal of theoretical abstraction [3].

The relationship between collective morphogen action and a spatial distribution of cells. COURTESY: Figure 4 in [4]. 

A related part of the black box conundrum is what the arcs and arrows (inputs and outputs) represent. Both inputs and outputs can be quite diverse. Inputs bring things like raw materials, reactants, free energy, sources of variation, components, while outputs include things like products, transformations, statistical effects, biological diversity, waste products, bond energy. While inputs and outputs can be broadly considered, the former (input signals) provide information to the black box, while the latter (output signals) provide samples of the processes unfolding within the black box. Inputs also constrain the state space representing the black box.

Within the black box itself, processes can create, destroy, or transform. They can synthesize new things out of component parts, which hints towards black box processes as sources of emergent properties [5]. Black boxes can also serve to destroy old relationships, particularly when a given black box has multiple inputs. Putting a little more detail to the notion of emergent black boxes involves watching how the black box transforms one thing into another [6]. This leads us to ask the question: do these generic transformational processes contribute to increases in global complexity?

Perhaps it does. The last insight about inputs and outputs comes from John Searle and his Chinese Room problem [7]. In his model of a simple message-passing AI, an input (in this case, a phrase in Chinese) is passed into a black box. The black box processes the input either by mere recognition or more in-depth understanding. These are referred to as weak and strong artificial intelligence, respectively [7]. And so it is with a cybernetic black box -- the process within the unit can be qualitatively variable, leading to greater complexity and potentially a richer representation of biological and social processes.

[1] certainly, systems involving social phenomena operate in a similar manner. We did not discuss social systems in the NetSci talk, but things discussed in this post apply to those systems as well.

[2] for those whom are familiar, this is quite similar to the mind-brain problem from the philosophy of mind literature, in which the mind is a model of thought and the brain is the mechanism for executing thought.

[3] this might be why robust biological "rules" are hard to come by.

[4] Lander, A. (2011). Pattern, Growth, and Control. Cell, 144(6), 955-969.

[5] while it is not what I intend with this statement, a good coda is the Sidney Harris classic "then a miracle happens..."


[7] Searle, J. (1980). Minds, Brains, and Programs. Cambridge University Press, Cambridge, UK.

July 26, 2017

Battle of the (Worm) Bots

There is an fledgling initiative at the OpenWorm Foundation to build a worm robot. This post highlights some of the first steps towards this goal. OpenWorm Robots will be useful for educational purposes and movement experiments/validation. No soft robotics (or miniaturization) yet, but progress is also continually being made on the brains behind the bot. More videos are forthcoming, so stay tuned. Thanks go to Dr. Tom Portegys and Shane Gingell for their efforts.

This is the latest version of the OpenWorm bot, as shown on the OpenWorm YouTube channel.

This is Shane's first iteration, called RoboWorm. Note the sinusoidal movement, and then compare to a biological C. elegans.

July 16, 2017

Wandering Towards an Essay of Laws

The winners of the FQXi "Wandering Towards a Goal" essay contest have been announced. I made an entry into the contest (my first FQXi contest entry) and did not win, but had a good time creating a number of interesting threads for future exploration. 

The essay itself, "Inverting the Phenomenology of Mathematical Lawfulness to Establish the Conditions of Intention-like Goal-oriented Behavior" [1], is the product of my work in an area I call Physical Intelligence in addition to intellectual discussions with colleagues (acknowledged in the essay). 

I did not have much time to polish and reflect upon the essay at the time it was submitted, but since then I have come up with a few additional points. So here are a few more focused observations extracted from the more exploratory essay form:

1) there is an underappreciated connection between biological physics, evolution, and psychophysics. There is an subtle but important research question here: why did some biological systems evolve in accordance with "law-like" behavior, while many others did not? 

2) the question of whether mathematical laws are discovered or invented (Mathematical Platonism) may be highly relevant to the application of mathematical models in the biological and social sciences [2]. While mathematicians have a commonly encountered answer (laws are discovered, notation was invented), an answer based on discovering laws from empirically-driven observations will likely provide a different answer.

3) how exactly do we define laws in the context of empirical science? While laws can be demonstrated in the biological sciences [3], biology itself is not thought of as particularly lawful. According to [4], "laws" fall somewhere in-between hypotheses and theories. In this sense, laws are both exercises in prediction and part of theory-building. Historically, biologists have tended to employ statistical models without reference to theory, while physicists and chemists often use statistical models to demonstrate theoretical principles [5]. In fields such as biology or the social sciences, the use of different or novel analytical or symbolic paradigms might facilitate the discovery of lawlike invariants.

4) the inclusion of cybernetic principles (Ashby's Law of Requisite Variety) may also bring together new insights on how laws occur in biological and social systems, and whether such laws are based on deep structural regularities in nature (as argued in the FQXi essay) or the mode of representating empirical observations (an idea to be explored in another post).

5) Aneural cognition is something that might guide information processing in a number of contexts. This has been explored further in another paper from the DevoWorm group [6] on the potential role of aneural cognition in embryos. It has also been explored in the form of the free-energy principle leading to information processing in plants [7]. Is cognition a unified theory of adaptive information processing? Now that's something to explore.

[1] A printable version can be downloaded from Figshare (doi:10.6084/m9.figshare.4725235).

[2] I experienced a nice discussion of this issue during an recent NSF-sponsored workshop. The bottom line is that while the variation typical of biology often makes the discovery of universal principles intractable, perhaps law discovery in biology simply requires a several hundred year investment in research (h/t Dr. Rob Phillips). For more, please see:

Phillips, R. (2015). Theory in Biology: Figure 1 or Figure 7? Trends in Cell Biology 25(12), 1-7.

[3] Trevors, J.T. and Saier, M.H. (2010). Three Laws of Biology. Water Air and Soil Pollution, 205(S1), S87-S89.

[4] el-Showk, S. (2014). Does Biology Have Laws? Accumulating Glitches blog, Nature Scitable.

[5] Ruse, M.E. (1970). Are there laws in biology? Australasian Journal of Philosophy, 48(2), 234-246. doi:10.1080/00048407012341201.

[6] Stone, R., Portegys, T.E., Mihkailovsky, G., and Alicea, B. (2017). Origins of the Embryo: self-organization through cybernetic regulation​. Figshare, doi:10.6084/m9.figshare.5089558.

[7] Calvo, P. and Friston, K. (2017). Predicting green: really radical (plant) predictive processing. Journal of the Royal Society Interface, 14, 20170096.