Saturday Morning NeuroSim Discussion Thread: Physical Computing

 

From the “Macy Conference Redux” feature form our July 1 meeting

Over the past three years, the Saturday Morning NeuroSim group has met weekly on Saturdays (mornings in North America). The Saturday Morning format continues in the tradition of Saturday Morning Physics and covers a wide variety of topics.

One recent lecture/discussion thread is on Physical Computation. Our approach to the topic begins with the debate around the role of computation in Cognitive Science and the Neurosciences. And so we begin in Week 1 with a discussion of the connections between computation, information processing, and the brain, largely focusing on the work of Gualtiero Piccinini and Corey Maley. A starting point for this session is their Stanford Encyclopedia of Philosophy article on “Computation in Physical Systems”. Many current assumptions about computation in the brain stem from the Church-Turing thesis, which often leads to a poor fit between model and experiment. Piccinini and Maley propose that the Church-Turing-Deutsch thesis is preferable when talking about systems that perform non-digital computations. Amanda Nelson pointed out the it makes sese to think of evolved biological systems (brains) as instances of analogue computers. Another interesting point from the session is the distinction between the digital (Von Neumann) computers and alternatives such as “physical” or “analog” computation, which would be picked up on in the next session.

Physical Computation Session I from June 24 (roughly one hour in length).

The second session focused on physical computation, and led us to discuss the idea of pancomputationalism. While pancomputationalism is the fundamental assumption behind the phrase “the brain is a computer” [1], we we also introduced to pancomputationalism in ferrofluidic systems and mycelial networks. We discussed the works of Richard Feynman (Feynman Lectures on Computation) and Edward Fredkin (Digital Physics), which helped us form an epistemic framework for computation in nature [2]. We also discussed Andy Adamatsky’s work on unconventional computation, particularly his work on Reaction-Diffusion (R-D) Automata, that while discrete in nature has connections to excitable (e.g neural) systems via the Fitzhugh-Nagumo model.

Physical Computation Session II from July 1 (roughly one hour in length)

After taking a break from the topic, our July 15 meeting featured an alternative viewpoint on pancomputationalism. This was made manifest in a shorter discussion on physical computation, with views from Tomasso Toffoli and Stephen Wolfram. We covered Toffoli’s paper “Action, or the funcgability of computation”, which connects physical entropy, information, action, and the amount of computation performed by a system. This paper is of great interest to the group in light of our work and discussions on 4E (embodied, embedded, enactive, and extended) cognition [3]. Toffoli makes some provocative arguments herein, including the notion of computation as “units of action”. A concrete example of this is a 10-speed bicycle, which is not only not a conventional computer, but also has linkages to perception and action. Amanda Nelson found the notion of transformation from one unit into another particularly salient to the distinction between analogue and digital computation. The physical basis of all forms of computation can also be better defined by revisiting “A New Kind of Science” [4], in which Wolfram sketches out the essential components and analogies of a computational system with a physical substrate. We can then compare some of the more abstract aspects of a physical computer with neural systems. This is particularly relevant to engineered systems that include select components of biological networks.

Physical Computation Session III from July 15 (about 15 minutes in length)

The next session followed up on computation in natural systems as well as Wolfram’s notion of universality, particularly in terms of computational models. In particular, Wolfram argues that cellular automata models can characterize universality, which is related to pancomputationalism. Universality suggests that any one computational model can capture system behavior that can be applied across a wide variety of domains. In this sense, context is not important. Rule 30 produces an output that resembles pattern formation in biological phenotypes (the shell of snail species Conus textile), but can also be used as a pseudo-random number generator [5]. In “A Framework for Universality in Physics, Computer Science, and Beyond”, this perspective is extended to understand the connections between computation defined by the Turing machine and a class of model called Spin Models. This provides a framework for universality that is useful form defining computation across the various levels of neural systems, but also gives rise to understanding what is uncomputable. This sessions natural system examples featured computation among bacterial colonies embedded in a colloidal substrate along with computation in granular matter itself. The latter is an example of non-silicon based polycomputation [6].

Physical Computation Session IV from July 22 (about 12 minutes in length).

After talking a more extended break from the topic, we returned to this discussion four weeks later (August 19). Our sixth (VI) session occurred in our August 19 meeting, and covered three topics: physical computation and topology, morphological computation, and RNA computing/Molecular Biology as universal computer.

We have discussed category theory before in our discussions on Symbolic Systems and Causality. In this section, we revisited the role of category theory, but this time with reference to Physical Computation. John Carlos Baez and Mike Stay give a tour of category theory’s role in computation via topology. The idea is that category theory forms analogies with computation, which can be expressed on a topological surface/space.

Computable Topology, Wikipedia.

Baez, J. and Stay, M. (2009). Physics, Topology, Logic and Computation: A Rosetta Stone. arXiv, 0903.0340.

Mapping category theory operators to a topological description.

We aslo covered the role of Morphological Computation by reviewing three papers on this form of physical computation that intersects with digital computational representations. Morphological Computation is the role of the body in the notion of “cognition is computation”. One idea that is critiqued with in these papers is offloading from the brain to the body. Offloading is moving computational capacity from the central nervous system to the periphery. If you grab a ball with your hand, you recognize and send commands to grasp the ball, but you must grasp and otherwise manipulate the object to fully compute the object. Thus, this capacity is said to be offloaded to the hand or peripheral nervous system.

Interestingly, offloading and embodiment are integral parts of 4E (Embodied, Embedded, Enactive, and Externalized) Cognition, which itself critiques the brain as computation idea. But as an analytical tool, morphological computation is much more utilitarian than Cognitive Science theory, and is concerned with how the robotic bodies and other mechanical systems interact with an intelligent controller. In non-embodied robotics, body dynamics is treated as noise. But in morphological computation, body dynamics play an integral role in the intelligent system and contribute to a dynamical system.

Muller, V.C. and Hoffmann, M. (2017). What Is Morphological Computation? On How the Body Contributes to Cognition and Control. Artificial Life, 23, 1–24.

Fuchslin, R.M., Dzyakanchuk, A., Flumini, D., Hauser, H., Hunt, K.J., Luchsinger, R.H., Reller, B., Scheidegger, S., and Walker, R. (2013). Morphological Computation and Morphological Control: Steps Toward a Formal Theory and Applications. Artificial Life, 19, 9–34.

Milkowski, M. (2018). Morphological Computation: Nothing but Physical Computation. Entropy, 20, 942.

The three insights from our morphological computational discussion.

While these papers do not get too deeply into the role of pancomputation in Morphological Computation, it is implicitly stated and plays a central role in our last topic: RNA computing and Molecular Biology. For more information, see this talk on YouTube and the paper below. Basically, while the pancomputationalism perspective is missing from biology, the structure and potential function of DNA and RNA provide a route to phycial computation.

Akhlaghpour, H. (2022). An RNA-based theory of natural universal computation. Journal of Theoretical Biology, 537, 110984.

Bringing pancomputationalism into biology? What is its value?

Thanks to Morgan Hough for joining us from Hawaii (4:00 am!) on August 19.

References

[1] Richards, B.A. and Lillicrap, T.P. (2022). The Brain-Computer Metaphor Debate Is Useless: A Matter of Semantics. Frontiers in Computational Science, 4, 810358.

Should we just simply “shut up and calculate”, or debate some more?

[2] Fredkin, E. (2003). An Introduction to Digital Philosophy. International Journal of Theoretical Physics, 42(2), 189.

This work is the Rosetta Stone for many comparisons between modern AI systems and human-like intelligence, at least in terms of computation.

[3] Newen, A., DeBruin, L., and Gallagher, S. (2018). The Oxford Handbook of 4E Cognition. Oxford University Press.

[4] Wolfram, S. (2002). A New Kind of Science. Wolfram Media.

This is a link to the 20th Anniversary edition, with a full set of Cellular Automata rules, defined by number.

[5] Zenil, H. (2016). How can I generate random numbers using the Rule 30 Cellular Automaton? Quora post.

[6] Bongard, J. and Levin, M. (2023). There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines. Biomimetics, 8(1), 110.

The Way of The Polymath

This content is cross-posted to the Orthogonal Research and Education Lab Medium.

What constitutes a polymath, and why are they so rare? Another way to ask this question is why are there so few foxes relative to hedgehogs? The occasional hyper-specialist would have you believe the term "polymath" is an epithet. However, there are a number of skills that the polymath possesses that translate into an advantage for advancing both theory and fundamental knowledge. Aside from the mastery of multiple intellectual areas, the most important of these is the ability to synthesize information from a number of sources. The advent of digital scholarship may enable this ability in the foxes among us [1].

One depiction (late 19th, early 20th century) of a polymath.

Back in 2015, Nature Careers released a list of recommendations to combat the hyper-specialist tendencies of PhD programs [2], but many of these are simply window-dressing. One view is that improving the state of interdisciplinary thinking is to improve the infrastructure for collaboration and disseminating big ideas. However, a more fundamental (and harder-to- implement) change that can be made is to reconfigure the epistemic landscape of science [3]. One aspect of this indeed involves the training of scientific generalists, but generalist training does not equate polymathism.


The other factor involves the potential zero-sum nature of generalized knowledge [4]. There is a constant tradeoff between deep expertise in one area versus more shallow expertise in a number of areas simultaneously. Society tends to reward deep expertise, and synthesis is rather expensive knowledge-wise. In any case, there is a game-theoretic interpretation of this scenario, but that is a topic for another post.  

Here is a semi-annotated reading list on singular (but multidisciplinary) academic activity:

Hossenfelder, S.   The loneliness of my notepad. Backreaction blog, July 8 (2015).


Issacson, W.   Myth of the Lone Genius. Aspen Journal of Ideas, July 24 (2015). 

These articles critically examine the myth of the lone genius. The first article points out that tools enabling collaboration (e.g. internet, large-scale consortia) are finally starting to bear fruit. Proportion of single-author papers has gone down over last 20-30 years, but that does not mean lone efforts are in absolute decline. In fact, "isolation" is a myth, given the social networks and information-sharing culture in academia.


Bateman, T.S. and Hess, A.M.   Different personal propensities among scientists relate to deeper vs. broader knowledge contributions. PNAS, 112(12), 3653-3658. (2015).


Kirkegaard, E.   Personality correlates of breadth vs. depth of research scholarship. Project Polymath blog, March 6 (2015).

* relates style of scientific investigation to type of contributions (specialized studies vs. broad interdisciplinary synthesis) made by scientists. Survey methodology does not assume that contributions can be both deep and broad, despite setting this up as a dichotomy. Is "deeper" vs. "broader" a major dichotomy in scientific exploration.

* suggests that the difference between scientific generalists and specialists is epistemic, not economic as traditionally assumed (e.g. a certain strategy is more or less risky).

* views differences in types of scientists (e.g. polymathy) as a matter of personality, not epistemic bias.


Palla, G., Tibely, G., Mones, E., Pollner, P., and Vicsek, T.   Hierarchical networks of scientific journals. arXiv, 1506.05661 (2015).

Presents a hierarchical network analysis of scientific journals and their relevance to measuring influence and the diffusion of ideas in specific scientific fields.


Muldoon, R. and Weisberg, M.  Robustness and idealization in models of cognitive labor. Synthese, 183, 161-174 (2011).

* introduce us to an economic optimization model called the marginal contribution/reward (MCR).

* motivation of individuals or groups of scientists is accomplished either through self-interest or epistemic norms.

* MCR assumes that cognitive labor can be optimally distributed across collaborations to solve hard problems. The problem is stated as a constrained maximization of "success" and "return". Model does not provide good approximations of success, return, or epistemic norms, not does it distinguish amongst different scientific skill-sets (generalists vs. specialists vs. hyper-specialists).


Sarma, G.P.   Should we train scientific generalists? arXiv, 1410.4422 (2014).

* how to introduce students to a vocabulary of multiple disciplines, and how this would encourage research breadth.

alexarje   Disciplinarities: intra, cross, multi, inter, trans. Alexander Refsum Jensenius blog, March 12. (2012).

NOTES:
[1] Alicea, B.   "Academic Connectivity and the Future of Scientific Ideas". Synthetic Daisies blog, September 9 (2011).

[2] Nurse, P.   To build a scientist. Nature, 523, 371-373 (2015).

[3] Weisberg, M. and Muldoon, R.   Epistemic Landscapes and the Division of Cognitive Labor. Philosophy of Science, 76(2), 225-252 (2009).

[4] Downey, G.   Interdisciplinarity, sub-disciplinarity, and inter-topicality. Uncovering Information Labor blog, March 31 (2006).

fQXi essay on the Undecidable, Uncomputable, and Unpredictable

It's that time of year again: the fQXi essay contest for 2020 is going strong! Every 12-24 months, fQXi (Foundational Questions Institute) sponsors an essay content on a different topic. The fQXi community [1] then responds to the essay using a ratings and comment system. This year's topic was "Undecidability, Uncomputability, and Unpredictability", a topic not only applicable to physics, but to fields ranging from Computer Science to Sociology and even Biology. Check out the collection of submissions for some incredibly creative takes on the topic.

Myself, along with Orthogonal Research and Education Lab members Jesse Parent (@JesParent on Twitter) and Ankit Gupta (@ankiitgupta7 on Twitter) submitted an essay called "The illusion of structure or insufficiency of approach? the un(3) of unruly problems".

I have also posted several essays from years past as part of a ResearchGate project. These include "Establishing the Phenomenological Conditions of Intention-like Goal-oriented Behavior" from 2016 and "Towards the meta-fundamental: introducing intercontextual invariants" from 2018.

A few weeks after submitting this year's essay, I discovered the work of Nicolas Gisin, who has published a series of papers [2] on alternative forms of mathematics (such as intuitionism) for describing complex systems. While his examples are limited to physics, they are a complement to this year's essay.

NOTES:
[1] for some stimulating internet discussion, check out the Alternative Models of Reality section of the fQXi community.

[2] Gisin, N. (2020). Mathematical languages shape our understanding of time in physics. Nature Physics, 16, 114–116.

Silver Linings of COVID-19

PLoS headquarters when most of its staff is working remotely (click to enlarge).

A Brady Bunch pun on remote work from our friends at Numenta (click to enlarge).

This potentially tasteless title brings to mind the positive elements of canceling classes, academic conferences, and workplace meetings: the ability to do these activities virtually. Among my current projects, I am involved in a number of working groups that are entirely virtual. These group utilize Zoom and Google Meet to give talks and hold meetings, with Github, Google Docs, and a host of other tools to manage contributions and research products (papers, talks, social media posts). This might be called the "Zoom/Slack" paradigm. Below is a Twitter thread from a Sloan Foundation program officer that asks for thoughts on alternatives to this standard.

Potential support for people collaborating outside the Zoom/Slack paradigm (click to enlarge).

In 2014, I posted on a concept called a theory hackathon, which was held as a hybrid physical and virtual event. The idea is to define and work on problems that are best solved in teams where not all members can meet live. But online meetings are evolving beyond awkward encounters and technical glitches. Often, live physical meetings are meant to cement social ties. Indeed, below is an informal survey that asks this very question.

Why are live events (as opposed to virtual) so important (click to enlarge)? 

In general, live physical conferences seem to be useful for social connection. Revisiting the tweet from Josh Greenberg, perhaps what is needed aside from virtual meeting spaces and file exchange/ chat functionality is a frictionless social platform. This could be conventional social media, or more likely a virtual reality platform integrated with live video/version controlled file exchange/chat capabilities.

Virtual meetings and virtual work are not without their own rhythm and customs. Aside from the potential for social disconnection, it also poses a challenge for personal habituation and ultimately productivity. Below is a link to a Twitter thread that gives tips for meeting virtually for people who are unaccustomed to doing so.

Online meeting tips from Mozilla Open Leaders (click to enlarge).

Carpentries-style tips for synchronous online meetings (click to enlarge).

There are also tips for working from home more generally. As with virtual meetings, capacity to work remotely has been accelerated in the age of social media [1]. The link below gives tips about working from home as an adjustment from working in a large office or public place. Generally, virtual work does require a change in expectations, from dealing with technical glitches to dealing with gaps in social presence [2].

Tips on adjusting to working at home (click to enlarge).

Well-being while working from home (download) (click to enlarge).

Draft workbook on how to host an online conference (click to enlarge).

Online conference are more than simply scaling up virtual meetings. There is a method to conducting and organizing online conferences [3], and there are a number of options regarding the medium. Returning to the issue of greater social connectivity in virtual meeting, one solution is to hold the conference in a virtual world such as Second Life. In this type of meeting, you are able to meet other people as avatars, and even interact with the venue itself. Below are two examples of my experiences with Second Life academic events in the past, one being a continuing lecture series called Embryo Physics, and the other a conference called Simulation and Second Life.

Tour of the Embryo Physics Course @ Silver Bog, Second Life (click to enlarge).

My avatar at the Simulation and Second Life conference, 2007 (click to enlarge).

As a bonus, there is a new agent-based model of COVID-19 transmission created by Paul Smaldino and implemented in NetLogo. This model demonstrates the efficacy of social distancing (hence the resurgent interest in working virtually).

Discussion of COVID-19 transmission model as a Twitter thread (click to enlarge).

Be sure to also check out the Living Computation Foundation's "Pandemic in a Box"! Click to enlarge.

NOTES:
[1] Williams, A. (2017). How the Rise of Social Media Fostered a Culture of Remote Working. Social Media Week, April 14.

[2] Oh, C.S., Bailenson, J.N., and Welch, G.F. (2018). A Systematic Review of Social Presence: Definition, Antecedents, and Implications. Frontiers in Robotics and AI, doi:10.3389/frobt.2018. 00114.

[3] Reshef, O., Aharonovich, I., Armani, A., Gigan, S., Grange, R., Kats, M.A., Sapienza, R. (2020). How to organize an online conference. arXiv, 2003.03219.