All presentations,talks, and discussions are in Room 115, Global Learning Center, Building A12 on Kawauchi Campus (North) of TohokuUniversity (entrance to A12 is about 50 meters SW (up the hill) from Kawauchi SubwayStation – easy-to-spot a white vending machine with the name KIRIN next to the
entrance). All sessions will be accessible via ZOOM sessions. Please feel free
to share the invitation link with your friends who are interested in the
subject.
For those who will participate via ZOOM:
Zoom Meeting Link
https://zoom.us/j/93925762089?pwd=NExRSmhTR3NhQmI3L0dlOHVORlZFUT09
Meeting ID: 939 2576 2089
Passcode: 220105
PLEASE FEEL FREE TO SHARE THE PROGRAM, LINK, ETC WITH YOUR FRIENDS AND COLLEAGUES
18:00-20:30 Short popular lectures for Tohoku University students on the subject of the Workshop. Confirmed
lectures:
Ø 10:00 - 10:15 Opening
Ø 10:15 - 10:45 Yasuhiro Suzuki (GraduateSchool of Informatics, Nagoya University Japan) ysuzuki@nagoya-u.jp
“Inter-InduceComputation, as a model of Computing with Tactile Sense”
Ø 11:00 -12:00 Shin-ichiro M. Nomura (Tohoku University, Japan) shinichiro.nomura.b5@tohoku.ac.jp INVITEDTALK “Molecular cybernetics: viewpoints from the material side”
Ø 12:15 - 13:30 Lunch Break
Ø 13:30 – 14:30 Kenichi Morita (HiroshimaUniversity, Japan) km@hiroshima-u.ac.jp
INVITED TALK "Time-reversalsymmetries in reversible partitioned cellular automata" Ø 14:45 – 15:45 Yukio-Pegio Gunji (Waseda University, Japan) pegioyukio@gmail.comand Daisuke Uragami (Nihon University) INVITED TALK “Critical computation in the disequilibration between the active and passive computing”
Ø 16:00 – 16:30 Marcin J. Schroeder (IEHETohoku University Sendai Japan) mjs@gl.aiu.ac.jp “Merging Mathematical Models ofInformation for the Digital and Analog Computing”
Ø 16:45 – 17:45 Aaron Sloman (University of Birmingham, UK) a.sloman@bham.ac.uk
INVITED TALK “Recently hatched ideas about hatching andintelligence”
Ø 18:00 – 18:30 Discussion & Reflectionson Topics of the Day
Ø 8:45 – 9:00 Opening of the Day
Ø 9:00 - 10:00 Michael Levin (AllenDiscovery Center at Tufts University, USA) Michael.Levin@tufts.edu" INVITED TALK“Bioelectrical networks enable navigation of anatomical morphospace in
embryogenesis, regeneration, and cancer"
Ø 10:15– 10:45 Masami Hagiya (University of Tokyo, Japan) hagiya@g.ecc.u-tokyo.ac.jp
“GrowingPoint Automata and DNA Computing”
Ø 11:00 – 11:30 Attila Egri-Nagy (Akita International University, Akita, Japan) egri-nagy@aiu.ac.jp “AI, games, and theproblem of scientific realism”
Ø 11:45 –12:15 Martin Robert (Laboratory of Microbial Systems Biology, GraduateSchool of Pharmaceutical Sciences, Kyoto University, Japan) robert.martin.4m@kyoto-u.ac.jp “Metabolism as one instantiation of natural computing”
Ø 12:30 – 13:30 LUNCH BREAK
Ø 13:30 – 14:00 Akika Utsumi1,Toru Moriyama2 (Graduate School of BiomedicalEngineering, Shinshu University, Japan) 21BS202A@shinshu-u.ac.jp1 toru@shinshu-u.ac.jp2 “A study on the ability to regulate inverterfor turn reaction in pill bugs”
Ø 14:15 – 14:45 DavinBrowner (Robotics Laboratory, Royal College of Art, London,UK) davin.browner-conaty@network.rca.ac.uk “Electropolymericelectronics compiler approach to fabrication of ferroelectric memristors with applications in auditory neurotechnologies”
Ø 15:00 – 16:00 Gordana Dodig-Crnkovic(Chalmers University, Sweden) Gordana.dodig-crnkovic@chalmers.se INVITED TALK “Exploring the Connection Between Life, Cognition, and Intelligence
through an Info-computational Approach” Ø 16:15 – 17:15 Andrew Adamatzky (Unconventional Computing Laboratory UWE Bristol, UK) andrew.adamatzky@uwe.ac.uk INVITED TALK "Towards fungal brain: sensing and computing with fungi"
Ø 17:30 – 18:30 Discussion on the future directions in the inquiry of natural computing
ANNOUNCEMENT OF THE WORKSHOP
14th InternationalWorkshop on Natural Computing will be held at Tohoku University in Sendai,
Japan on the weekend Friday through Sunday, January 20-22, 2023. Although the Workshop is planned in principlein the in-person format, we are planning a limited number of presentations to
be delivered by ZOOM for those whose long-distance travel would make
participation impossible.
The series of InternationalWorkshops on Natural Computing initiated in 2006 grew up from the original
interest in molecular computing. However, within the years following this
original initiative, the topic of natural computing became one of the main
directions of study in several disciplines. Natural processes or even entire
life started to be considered a form of information processing with
characteristics of computing. On the other hand, information processing in
natural systems became a source of inspiration for innovation in computer
science, artificial intelligence, and engineering. Moreover, computer
simulation became a common tool for the study of nature.
Following this general trend ofmutual interactions of disciplines, the 14th International Workshopon Natural Computing continues the already-established tradition of the IWNC
series to devote its sessions to the recent and future developments in
research, practice, philosophical reflection, and creative activity within the
crossroads of nature, computing, information science, cognitive science, the
study of life and culture.
The workshops bring together a verywide range of perspectives on natural computing (in its diverse ways of
understanding) from philosophical to scientific ones, to visions of artists.
There is an opportunity to present original and creative contributions without
any restriction by disciplinary divisions or the level of advancement of
research. Contributions from the beginning of the academic or intellectual
career are as welcome as those from its peak.
ORGANIZING/PROGRAMCOMMITTEE OF THE 14th IWNC:
(Chair)Marcin J. Schroeder (Tohoku University) <schroeder.marcin.e4@tohoku.ac.jp>
MasamiHagiya (University of Tokyo) <hagiya@g.ecc.u-tokyo.ac.jp>
KatsunobuImai (Hiroshima University) <imai@hiroshima-u.ac.jp>
YasuhiroSuzuki (Nagoya University) <ysuzuki@nagoya-u.jp>
ConfirmedInvited Speakers: Ø Andrew Adamatzky (Unconventional Computing Laboratory UWE Bristol, UK): "Towards fungal brain: sensing and computing with fungi"
Ø Shin-ichiro M. Nomura (Tohoku University,Japan): “Molecular cybernetics: viewpoints from the materialside”
Ø Aaron Sloman (University of Birmingham, UK): “Recently hatched ideas about hatching and intelligence”
1. Towards fungal brains
Andrew Adamatzky
Unconventional Computing Lab, UWE Bristol, UK
andrew.adamatzky@uwe.ac.uk
Fungi exhibit oscillations of extracellular electrical potentialrecorded via differential electrodes inserted into a substrate colonised by
mycelium or directly into sporocarps. Fungal spiking activity is very similar
to that recorded from central nervous system of humans and animals. Fungi
significantly reduce their electrical activity when placed under general anesthesia,
similarly to what humans do. Thus, we can conclude that fungal mycelium acts as
a brain. Fungal brain is a very slow and very large brain spanning hectares. We
analysed fungal electrical activity using linguistic and information complexity
techniques. We found that distribution of fungal word length matches that of
human languages. We also discovered that language of fungi is more complex
morphologically than human language.
***
2. Exploring the Connection Between Life,Cognition, and Intelligence through an Info-Computational Approach
Gordana Dodig-Crnkovic
Chalmers University ofTechnology, Gothenborg, Sweden
&School of Innovation, Design and Engineering, Mälardalen University, Sweden
Gordana.dodig-crnkovic@chalmers.se
Cognition, traditionally thoughtof as a uniquely human ability, has been found to be present in all living
organisms, from single cells to humans. This research investigates cognition
from an info-computational perspective, viewing structures in nature as
information and natural processes as computation, from the perspective of a
cognizing agent.
New discoveries in relatedfields, including self-organization, autopoiesis, and evolution on various
levels of organization, have enhanced our understanding of the foundations of
cognition. It has become evident that cognition drives evolution, and evolution
drives cognition in living organisms, a process that is best understood through
the extended evolutionary synthesis.
Morphological and morphogeneticcomputation play a vital role in the self-assembly and self-organization of
physical, chemical, and biological agents, leading to the emergence of
cognition. Morphogenesis, the process by which an organism develops from active
matter, is seen as a form of morphological computation. Cells are
information-processing, communicating, learning, and adaptive agents, with
morphogenesis supporting Bayesian inference. The free energy principle and
agency are central to cognition from single cells up.
This work is exploring theinterplay between life, cognition, and intelligence through an
info-computational approach. A deeper understanding of the mechanisms of
cognition is important for the advancement of fields such as artificial
intelligence, robotics, and medicine.
***
While cellular automata synchronously updated can be classified into chaos (Class III), order (Class 1, II) and critical behavior (Class IV), and it was reported that critical behavior can be used for universal computation, Class IV automata are very rare and very specific. If elementary cellular automata are implemented by asynchronously updating equipped with disequilibration between the active and passive updating, most cellular automata which show Class I, II and III behavior if they are synchronously updated show Class IV behavior. We here show this type of cellular automata have criticality in terms of power law distribution, highly efficient and universal computation power, and reveal higher performance in machine learning of the reservoir computing than reservoir computing equipped with class 3 automata.
***
4. Bioelectrical networks enable navigation ofanatomical morphospace in embryogenesis, regeneration, and cancer
MichaelLevin
Allen Discovery Center at Tufts University, USA
Michael.Levin@tufts.edu
Living beings are remarkable self-assemblingassembling computationalcomputational systems, which make the journey from the chemistry and physics of
a quiescent oocyte to the complex metacognitive capacities of humans, and many
variants of cognition in between. In this talk, I will describe our efforts to
understand life as a multi-scale competency architecture, using computational
methods as both tools and metaphors. Analysis of the embryogenesis and
regeneration of complex living forms as a collective intelligence navigating various
problem spaces has enabled interesting advances in basic evolutionary biology
and also applications in biomedicine. I will discuss and show experimental
examples of concepts such as biological reprogrammability, creative
problem-solving machines, and the basal cognition of tissues that are not
brains, and the implications of these ideas across medicine, robotics, and AI.
***
5. Time-reversal symmetries inreversible partitioned cellular automata
Kenichi Morita
HiroshimaUniversity
km@hiroshima-u.ac.jp
Areversible cellular automaton (RCA) is called `time-reversal symmetric'
(T-symmetric, for short) if its backward evolution is governed by the same
local transition function for the forward evolution. In this study, we
investigate this property using the framework of elementary triangular
partitioned CA (ETPCA), and elementary square partitioned CA (ESPCA). It is
shown that all reversible ETPCAs and about 60 percent of reversible ESPCAs are
T-symmetric under simple transformations on configurations. The transformations
used here are (1) reversing moving directions of all particles, (2) taking a
mirror image of a configuration, (3) 0-1 complementation
ofeach state, and their compositions. They are somewhat similar to the
CPT-symmetry in physics.
Theproved results on T-symmetries are used to find and analyze backward evolution
processes in reversible PCAs. Inparticular, for a given functional module implemented in a reversible PCA,
wecan obtain its inverse functional module very easily using its T-symmetry. By
this, designing larger functional objects or reversible machines is simplified.
***
6. Molecular cybernetics: viewpoints from thematerial side
Shin-ichiroM. Nomura
Tohoku University, Sendai, Japan
shinichiro.nomura.b5@tohoku.ac.jp
Molecularcybernetics is a fundamental research framework that enables molecular systems
to behave more intelligently and autonomously [1]. In recent years, there have
been ongoing challenges for preparation the desired higher-order structures and
intermolecular interactions by designing the sequence of biological macromolecules
such as DNA, RNA, or proteins, and to realize molecular systems for logical
operations and decision making [2-5]. We have realized prototype molecular robots
that use lipid membrane vesicles as compartments that separate the internal molecules
from the environment [6], and in which sensors, processors, and actuators are
systemized [7-10]. As the next step, molecular cybernetics seeks to realize
information input from environment, storage, learning, output, and deployment
through information transfer between the individual compartments [1]. Specific
challenges include the transfer of sequence information across membranes, the
realization of memory and learning circuits, the precise design and arrangement
of artificial multicellular structures, and the transfer of molecular information
between vesicles. In this presentation, we will introduce the achievements
realized to date, especially information transfer inside and outside vesicles
[11,12] and automated production of artificial multicellular structures [13]
and discuss the prospects for the future.
References
[1]S. Murata, T. Toyota, S.-I. M. Nomura, T. Nakakuki, A. Kuzuya, Adv. Funct.
Mater.
2022,32, 2201866.
[2]L. M. Adleman, Science 1994, 266, 1021–1024.
[3]K. Sakamoto, H. Gouzu, K. Komiya, D. Kiga, S. Yokoyama, T. Yokomori, M.
Hagiya,Science 2000, 288, 1223–1226.
[4]S. Okumura, G. Gines, N. Lobato-Dauzier, A. Baccouche, R. Deteix, T. Fujii, Y.
Rondelez,A. J. Genot, Nature 2022, 610, 496–501.
[5]I. Kawamata, S.-I. M. Nomura, S. Murata, New Gener. Comput. 2022, DOI
10.1007/s00354-022-00156-4.
[6]M. Hagiya, New Generation Computing 1999, 17, 131–151.
[7]Y. Sato, Y. Hiratsuka, I. Kawamata, S. Murata, S.-I. M. Nomura, Sci Robot 2017,
2,DOI 10.1126/scirobotics.aal3735.
[8]Y. Sato, K. Komiya, I. Kawamata, S. Murata, S.-I. M. Nomura, Chem. Commun.
2019,55, 9084–9087.
[9]S. Murata, A. Konagaya, S. Kobayashi, H. Saito, New Generation 2013.
[10]M. Hagiya, A. Konagaya, S. Kobayashi, H. Saito, S. Murata, Acc. Chem. Res.
2014,47, 1681–1690.
[11]S. Iwabuchi, I. Kawamata, S. Murata, S.-I. M. Nomura, Chem. Commun. 2021,
57,2990–2993.
[12]S. Iwabuchi, S.-I. M. Nomura, Y. Sato, Chembiochem 2022, e202200568.
[13]S.-I. M. Nomura, R. Shimizu, R. J. Archer, G. Hayase, T. Toyota, R. Mayne,
A.Adamatzky, ChemSystemsChem 2022, DOI 10.1002/syst.202200006.
***
7. Recently hatched ideas about hatching andintelligence:
Using verylow energy physics and chemistry at "normal" temperatures in egg-laying vertebrates, with surprising implications for several research fields,possibly including fundamental physics.
AaronSloman
University ofBirmingham, UK
a.sloman@bham.ac.uk
ExtendedAbstract for invited talk at International Workshop on Natural Computing 20-22
Jan 2023 https://www.natural-computing.com/iwnc-ws
Very Short Abstract
I'll try to show how hatching processes ineggs of vertebrates have important links with a collection of apparently
unrelated problems in a variety of research and engineering domains, including
philosophy of mathematics, philosophy of mind, metaphysics, artificial
intelligence, theoretical physics, biological evolution, developmental biology,
chemistry, biochemistry, neuroscience, developmental psychology, and especially
the Symbiogenesis theory proposedby Lynn Margulis.
Extended Abstract and Notes
I'll try to show how hatching processes in eggs of vertebrates have important
links with a collection of apparently unrelated problems in a variety of
research and engineering domains, including philosophy of mathematics,
philosophy of mind, philosophy of science, metaphysics, artificial
intelligence, theoretical physics, biological evolution, developmental biology,
chemistry, biochemistry, neuroscience, developmental psychology, and especially
the Symbiogenesis theory proposed by LynnMargulis.
Metamorphosis in insects, summarised usefullyin https://nhm.org/marvelous-metamorphosis,is also very interesting and closely related, but for now it suits my purposes
to focus on vertebrate egg-laying species: they present important
challenges and (largely unnoticed) clues!
The key ideas are also relevant to mammalianreproduction, although that is far more complex than reproduction in eggs
outside the mother, because of rich biochemical interactions between mother and
foetus, before birth, and continuing varieties of influence after birth, in the
case of mammals.
Birds that have to keep their helpless hatchedoffspring in the nest to be fed until they have the strength to fly are also
exceptions. This presentation will ignore, or say very little about, such
cases.
I suspect that some of the hatching mechanisms andprocesses discussed below challenge current theories about fundamental physics.
I am grateful to theoretical physicist, AnthonyLeggett https://royalsociety.org/people/anthony-leggett-11804/ fornon-committal but encouraging comments relating to this.
(He has not read and is not responsible for any errors in, this document.)
Key ideas:
Processes of gene expression producing new biochemical structures, mechanisms
and processes, in organisms of many kinds, have been investigated by many
researchers in many different research and engineering fields, including
biochemical medical engineering. But, as far as I can tell, none of them have
attempted to formulate the specific collection of questions and conjectures
discussed below -- questions about mechanisms that are able to initiate and
help to control increasingly complex construction processes in eggs of
vertebrates during hatching.
This process includes biochemicaldisassembly and assembly processes that transform relatively unstructured
chemical biomasses in a new-laid egg into an intricately structured new
vertebrate animal, e.g. a baby chicken, duck, swan, alligator, crocodile,
turtle, python, etc. etc. -- that also possesses unlearnt knowledge about how
to act in the environment.
How is that transformation achieved?
Key sub-questions:
How can highlyparallel, multi-layered, constantly branching, chemical processes in an egg produce not only extremely complex, intricately related, physiologicalstructures and mechanisms of many kinds, all required for post-hatching
functioning of the new animal, but also post-hatching species-specific behavioural competences,i.e. abilities to act appropriately (for organisms of that type), in their
environment, e.g. moving around, avoiding obstacles, feeding themselves and in
some cases, but not all, following parents?
The point isillustrated by the post-hatching behaviours of avocet chicks in this 35 second
videoclip from a BBC Springwatch programme in June 2021:
https://www.cs.bham.ac.uk/research/projects/cogaff/movies/avocets/avocet-hatchlings.mp4.
The full programme is available on Youtube
It shows new hatchlings engaged inrich interactions with a complex environment, including walking to a river and
"fishing" for food, all done long before they have had time to train
neural networks for the purpose.
Another type of example is shown bysea turtles that fend for themselves after hatching, with no parents to look
after them, in this tutorial video https://www.youtube.com/watch?v=C9VydNhr35Y
Of course, there are thousands (orbillions?) of different examples of hatching processes that produce both new
bodies and new competences in those bodies, to be found all over this planet.
In all the above cases, hatchlingsneed to use fairly complex competences before they have had time, or
opportunity, to acquire the competences by training neural networks (which
don't exist during early phases of hatching and could not be trained within an
egg to control behaviour in an inaccessible environment outside the eggshell!).
The only possible source ofcompetences of newly hatched animals is the genetically (biochemically)
controlled in-egg hatching process.
(Does anyone know whether any otherother parts of the universe include similar forms of biological reproduction
and evolution? Could evolution of vertebrate animals and/or mathematicians able
to make discoveries about topological or geometric necessity or impossibility
be unique to this planet?)
I have the impression that very fewresearchers understand that the vast amount of recent research on
statistics-based neural networks cannot explain ancient human (and some
non-human, e.g. squirrel) abilities to detect and make use of
spatial impossibility or necessity.
And few understand Immanuel Kant'spoint in his (Critique of PureReason 1781) implyingthat statistics-based mechanisms cannot explain mathematical discoveries
concerning impossibility and necessity (e.g. in geometry and topology) --
including discoveries that were made centuries before well known ancient
mathematicians such as Pythagoras, Euclid and Archimedes were born, and even
longer before logic-based reasoning mechanisms were invented and used by
humans.
Necessity and impossibility are nothigh and low points on a scale of probabilities. They have totally different
origins from probabilities.
As Kant noticed, ancient precursorsof such mathematical competences are essential for many non-mathematical
spatial competences. Examples include avoiding obstacles, making tools, moving
complex objects through narrow openings (e.g. a table too wide to fit through a
doorway, but capable of being rotated in 3D space), building nests, and caring
for offspring. (He had other examples.)
Crucially, he also realised thatneither impossibility nor necessity can be derived from statistical evidence
supporting probabilities.
So, impossibility and necessity --key features of mathematical discoveries in past millennia, and also relevant
to intelligent selection of actions to achieve goals -- cannot be detected
using statistics-based neural network mechanisms, a point that seems not to
have been understood by the vast majority of contemporary researchers working
on natural and artificial neural networks that collect statistical evidence and
derive probabilities!
Presumably, those researchers havenot read and understood Kant's claims made in 1781.
Many philosophers, and perhaps otherthinkers, now accept the mistaken, belief (which I first encountered as a
student, around 1958) that Kant's views on mathematical discovery were refuted
by Eddington's observations of a solar eclipse in 1919, confirming Einstein's
claim in his theory of General Relativity that physical spaces are not
necessarily Euclidean.
Discussions of that claim usuallyignore the fact that non-Euclidean structures have been commonplace, since long
before Einstein was born, e.g. the surface of a ball, or a kettle, and the
surfaces of most human body-parts.
The existence of such non-Euclideanstructures does not refute ancient mathematical discoveries about properties of
Euclidean structures!
The ideas presented below providesteps toward a defence of Kant's views on mathematical discovery that nobody
could have thought of two and a half centuries ago.
Is there related work doneelsewhere?
I would be interested to hear about any researchers attempting to explain the
combined, detailed, problems of parallel construction, within an egg, of a
very broad and varied, intricately interrelated, collection of physiological
structures, while also producing significant species-specific competences
that are required soon after hatching, for interacting appropriately with
structures in the environment -- including abilities to
detect impossibilities, or to perform actions with useful necessary
consequences.
There are many researchers workingon self-organising, chemistry-based mechanisms, e.g. in slime moulds, but I
have not encountered any that meet the above requirements.
The work of Mike Levin at Tufts University (another invited speaker atthis conference) is particularly impressive, but the work I have seen also
addresses only a subset of the problems. As far as I know he has not discussed
mechanisms that are able to detect impossibility or necessity (which are not
degrees of probability) or tried to explain the parallel assembly of a
huge variety of different but intricately interrelated physiological
structures.
I apologise to any researchers whosework does address all the issues mentioned here. I'll be pleased to add
references to such work in the online version of this document.
(Email a.sloman-At-bham.ac.uk.)
Competences of new hatchlings
My attempts to link these old investigations to hatching processes in eggs of
vertebrates (extending previous research with students and colleagues in Philosophy/AI/Cognitive
Science since 1959) began in 2020, triggered by reflecting (for the first time)
on the well known (but widely ignored?) fact that there are many species of
vertebrate hatchlings that emerge from eggs, not only possessing bodies that contain
a huge variety of extremely intricately interconnected internal physiological
substructures and mechanisms, but also possessing important species-specific
spatial competences combining complex perception and action mechanisms, that
are used before the new hatchling has had time or opportunity to train neural
networks after hatching.
These mechanisms are used inperforming tasks such as breaking out of the eggshell, following a parent, or
going to food and eating it, all done without prior learning or training, as
illustrated by the 35 second avocet video extract above,showing new hatchlings engaged in rich interactions with a complex environment
including walking to a river and "fishing" for food.
There seems to be evidence ofsimilar precociality in hatchlings of a long extinct dinosaur species,
referenced below.
Do mechanisms underpinningcompetences of new hatchlings challenge current physics?
The abilities of such hatchlings must somehow be produced by (hitherto
unnoticed??) biochemical hatching processes in eggs. How?
My (partial, tentative, and hard todigest(!)) answer outlined below attempts to link products of processes on many
different time scales within and across species, produced by branching and
converging evolutionary histories.
The answer seems (to me) tochallenge current fundamental physical theories (as has happened repeatedly in
the history of physics), though my original aim was
to use fundamental physical theories (with expert help from
theoretical physicists), not to challenge current physics.
If it turns out that physics needs anew revolution, in order to explain hatching processes in eggs, it won't be the
first revolution in physics triggered by new empirical discoveries. For
example, the discovery of magnetism, centuries ago, and the discovery (many
centuries later) of connections between magnetism and electricity, led (via
Oersted, Ampere, Faraday, Maxwell, and others) to major changes in fundamental
physical theories, as well as many engineering applications of electromagnetic
mechanisms.
Could understanding hatchingprocesses also lead to currently unexpected (unbelievable?) new developments in
science and engineering?
Relevant well-known facts
There are many different egg-laying vertebrate species, including chickens,
avocets, alligators, turtles, pythons, etc., whose young emerge from eggs with
very different physical forms, all possessing intricately interrelated general
and species-specific physiological structures, and a collection of
species-specific behavioural competences available after hatching, without
having to be learnt by acting in the environment.
Less obviously, hatching processesdepend on species-specific chemical assembly competences used inside eggs
during hatching -- competences with their own developmental and evolutionary
histories.
Hatching processes in the eggs ofsuch species both:
-- transform the original relatively small variety of chemicals in new-laideggs into a much larger variety of chemicals used in multiple, intricately
interrelated, body-parts of the new hatchling, with many different functions.
and also
-- construct mechanisms that provide a varietyof post-hatching spatial competences, used without requiring any training.
E.g. new hatchlings of many speciescan perceive objects in the environment, select goals, plan and execute
suitable actions, including following a parent, moving toward, or avoiding,
objects in the environment; and feeding themselves.
- during hatching there's arepeatedly branching variety of new physiological substructures, requiring a
repeatedly branching variety of new construction-control mechanisms for
constructing new construction-control mechanisms. Earlier ancestors of such
species would have had fewer developmental phases and would have needed fewer
construction-control processes during hatching.
Note about invertebrate species:
Related phenomena occur in insects that start life as grubs that grow by
feeding themselves then produce cocoons in which processes of metamorphosis
occur that transform the grubs into adults that have both completely new
physiological structures (e.g. including wings) and new behavioural
competences, e.g. flying, feeding in a new way and mating.
The insect examples may laterprovide evidence that can suggest or test answers to my questions about
processes in vertebrate eggs, but they will not be discussed further in this
introduction.
Main conjecture
There must be a multi-layered answerto this question:
How can so much extremely complexinternal rearrangement of physical matter happen inside the eggshell of a
developing vertebrate animal, starting with a single fertilised cell surrounded
by a relatively small number of chemical substances separated by membranes in
the egg?
Discussion and questions
The above question is concerned withproduction of physical structures and mechanisms inside eggs. There are also
questions about how behavioural competences in newly hatched animals are
produced.
How can chemical processes in eggsalso produce competent new animals with a combination of enormously complex and
varied species-specific internal physiological structures and processes as well
as unlearnt behavioural competences?
(Like the competences of the avocets, referenced above.)
The main idea proposed here is thatthe abilities of biochemical processes in eggs to produce post-hatching
behaviour, in addition to assembling increasingly complex physical structures
within the egg, is a recent extension of an evolutionarily older class of
biochemical mechanisms that contribute to development of additional
physiological complexity in the hatching process while they are controlling
physical assembly processes by rearranging chemical substances within the egg.
The older mechanisms were alsopresumably once relatively recent extensions of even older classes of
mechanisms and competences inherited via backward branching routes (coming from
male and female parents, and their mail and female parents, etc.)
Biological evolution is not andcannot be a continuous process in any species that uses existing members to
produce new members of the species.
Moreover, insofar as the processes of developmentin an egg involve removal and formation of chemical bonds they also cannot be
continuous. This is explicit in Schrödinger's discussion of reproductive
processes in What is life? (1944).
What I am now suggesting is that abilities ofchemical processes in eggs to produce competences for used by an
animal after hatching, are later developments of much older abilities
of chemical processes occurring during a particular phase of assembly
inside the egg corresponding to a certain period in its evolution, to
produce the assembly control mechanisms required for the next stage of
development inside the egg, i.e. a developmental stage that is a product of
more recent evolutionary processes.
So each phase of assembly produces or modifies newphysiological structures and also creates control mechanisms required for
assembly of the next phase of development, corresponding to a more recent
evolutionary development.
The above line of thought leads to the conjecturethat the mechanisms producing competent post-hatching behaviours are relatively
recent evolutionary products, following earlier evolutionary products whose
behaviours (i.e. separating and re-using components of molecules provided in
the egg by the mother) occur during, not after, hatching, whereas the latest
evolutionary products produce behaviours after hatching.
There are also difficult questions about howevolutionary processes were able to get the required information into the
chemical structures in a newly formed egg.
[After I asked myself that question, an internet searchled me to this surprising answer: https://www.ucdavis.edu/food/news/study-challenges-evolutionary-theory-dna-mutations-are-random (byEmily C. Dooley).
My main claim is that answers to these questions,i.e. explanations of the evolutionary and developmental phenomena relevant to
assembly of new structures in an egg, depend on multi-stage processes of
development in eggs, using increasingly complex biochemical developmental
control mechanisms specific to eggs of that species.
The in-egg mechanisms "bootstrap"construction of both
-- increasinglycomplex physiological structures in eggs, corresponding to different stages in
evolution of the species,
and also construction of
-- increasinglycomplex forms of new virtual (non-space-occupying) machinery, that
control additional intricate, multi-strand, highly parallel, chemical assembly
processes inside the egg.
As a result those in-egg processesare also able to produce mechanisms that control species-specific post-hatching
behavioural competences, illustrated by the post-hatching behaviours of young
avocets above.
How is all that possible?
An incomplete answer is sketched inmy talks and online notes on this topic, involving (among other features)
hypothesized use of a previously unnoticed [*], parallel,branching, growing collection of increasingly species-specific in-egg
"Maxwell demons", implemented using conjectured increasingly complex
multi-layered forms of virtual machinery operating within the egg, but without
occupying physical space in the egg, since no spare space is available!
[*]. Ifanyone knows of related work on hatching mechanisms, I'll be grateful for
information. [Contact: a.sloman AT bham.ac.uk]
Could all that be achievable duringhatching by using increasingly large collections of simultaneously
active eletromagnetic signals, some of which trigger production of both
new physiological structures and also new control machinery required for later
stages of assembly?
What mechanisms could enable increasinglymany concurrently active construction/assembly mechanisms to operate in
parallel in the same small space (i.e. inside the hatching egg), without
seriously interfering with one another, during normal development -- though
sometimes things go wrong, producing deformities, conjoined twins etc., as
reported in
https://www.thepoultrysite.com/articles/chicken-embryo-malpositions-and-deformities
In short: I conjecture thatsuccessive collections of virtual machinery in the egg control constructions in
different developmental stages during hatching, by controlling chemical
processes that produce new, increasingly complex,
species-specific physiological structures, and also controlling processes
that produce the next level, species-specific, more recently
evolved, successor control machinery, i.e. more recently evolved (virtual)
assembly demons!
(Compare Levin's work.)
These ideas are crudely depicted ina complex diagram whose explanation will be an important part of the talk:
https://www.cs.bham.ac.uk/~axs/fig/evo-devo/evo-devo-final.jpg
(possibly modified before the talk).
Also linked in the full version of this document below.
<="" a="">Possibleimplications of these ideas
The conjectured mechanisms, including a great deal of simultaneous more or less
coordinated "action at a distance" within the egg (performing much
more complex sorting and construction tasks than Maxwell's demon), may point to
gaps in current fundamental physical theories, in addition to the need for
important revisions of theories in philosophy of mind, philosophy of
mathematics, philosophy of science, psychology, neuroscience, theoretical
biology, and AI.
<=""a="">A comparison with the following may be useful:
Increasingly sophisticated forms of virtual machinery have been developed by
human designers during the last half century, especially since the advent of
the internet and applications such as online banking, online distributed
databases of information, online reservation systems, and applications such as
email, discussion support mechanisms (e.g. Zoom), online games, etc.
<=""a="">Instead of the kinds of digital computing technology used by
human engineers to implement virtual machines, ancient processes of biological
evolution anticipated such achievements for use in far more complex and
intricate biological control processes, on a far smaller physical scale, using
biochemical mechanisms.
<=""a="">This is related to some of the claims made by Lynn Margulis,
referenced below,regarding the role of symbiogenesis in the history of this planet.
Compare Shubin et.al.(1997)
Limitations of digital technology
Digital technology would be incapable of replicating these processes, in which
-- very complex molecularreorganisation mechanisms are used, decomposing a relatively small variety of
types of molecule in new-laid eggs, and using the products of decomposition in
construction of many new materials with a variety of different sorts of
physical properties and physiological functions,
-- the processes are controlled(e.g. in hatching eggs) in accordance with a very detailed set of species-specific
requirements for a fully functional biological organism of a particular sort,
e.g. alligator, chicken, swan, turtle, python, etc.,
-- the processes occur in very muchsmaller spaces than assembly processes in human designed factories,
-- the process products varyenormously, within individual organisms, including bones with different sizes,
shapes and locations in the body, muscles, nerve fibres, various brain
structures, digestive mechanisms, blood vessels, blood, glands, chemicals produced
by glands (different sorts of hormones), lungs, skin, scales, feathers, and
other outer coverings, and various bodily openings, e.g. for feeding,
breathing, excreting etc. after hatching, and different sex organs for males
and females,
-- whereas components ofhuman-designed machines are typically manufactured separately then brought
together (e.g. on an assembly line) by humans or specialised assembly robots,
many components of a new creature formed in an egg are created in parallel in
roughly their required spatial relations, so that once started they can grow
together with rates of growth of various components varying according to
required later spatial relationships.
-- construction of products ofearlier phases of evolution will normally begin during earlier phases of
hatching, in parallel with required construction control mechanisms.
and
-- apart from maintenance oftemperature and protection from external disturbances (in cases where parents
are involved in hatching), there is very little intervention or control by
parents or other external objects.
Some implications
The above summary implies that eggs (of vertebrate species, and others not
discussed here) do not only produce the very complex physical products that
emerge after hatching, i.e. animals with a large variety of parts with
different structures and functions:
They also produce the increasinglycomplex and increasingly differentiated control mechanisms required for
producing all that complexity and diversity during the hatching process.
That includes producing increasinglypowerful, increasingly varied, bootstrapping processes, including bootstrapping
processes for producing more complex and functionally differentiated,
bootstrapping processes required during later stages of hatching.
I am not claiming that ontogenesisreplicates phylogenesis: individual organisms developing in an egg do not go
through the developmental stages of their ancestors. They don't pass through
the adult forms of all their ancestors.
However the mechanisms ofontogenesis (mechanisms controlling assembly of parts of a new organism
using available chemical resources) replicate, at least partially, the
evolution of mechanisms controlling assembly of parts! This claim is presented
graphically in a complex (over-complex?) diagram available in the full version of this document.
During the hatching process, both molecularreorganisation processes and mechanisms controlling molecular reorganisation,
grow increasingly complex and increasingly diverse, producing increasingly
varied physical materials needed in different parts of the developing organism,
with increasing amounts of physical parallelism, as hatching proceeds and the
foetus becomes more highly differentiated.
***
1. Electropolymeric electronics compiler approach tofabrication of ferroelectric memristors with applications in auditory
neurotechnologies
Davin Browner
PhD candidate, Robotics Laboratory, Royal Collegeof Art, London, UK
davin.browner-conaty@network.rca.ac.uk
Auditoryneurotechnology development focuses on the modelling, augmentation and
enhancement of human hearing and in the development of bio-inspired machine
audition methods. Spike-enabled auditory neurotechnologies that make use of
spiking neural networks (SNNs) in combination with acoustic processing
circuitry could be utilised in a wide range of devices for human-interventional
medicine and in novel sensory technologies for neurorobotic and neuroprosthetic
applications [1].
Auditoryneurotechnologies where learning methods are based on the physics of memristive
SNNs (MemSNNs) may offer the promise of lower power Artificial Neural Networks
(ANNs) via resistive memory and switching phenomena [2]. However, existing
methods for memristor fabrication are difficult to implement in durable
polymer-based substrates and do not have suitable biocompatibility or acoustic impedance
matching profiles for integration into auditory neurotechnologies. More
generally, many of the resistive switching methods in the literature rely on
phenomena with significant cycle-to-cycle and device-to-device stochastic
switching characteristics (e.g., phase change, ion migration, conductive filament
formation, etc) that complicate hardware implementation and device modeling.
Ferroelectric polymer memristorswith good biocompatibility and acoustic impedance matching profiles would be
beneficial for development of MemSNNs for auditory neurotechnologies [3]. In
general, ferroelectric memristors are excellent candidates for auditory
learning applications due to their cycle-to-cycle endurance based on continuous
ferroelectric polarisation, i.e., the “ferroelectric plasticity” that can be obtained
by regulating the amplitude or duration of the applied voltage pulses. The two-terminal
type ferroelectric artificial synapses, which include memristor based devices,
consist of two electrodes that are separated by a ferroelectric film. When a
presynaptic signal is applied to one electrode, an update of the conductance is
generated accordingly, and the synaptic weight can be readout from the other
electrode. In this way, spike signals are transferred from the pre- synaptic
terminal to the postsynaptic one. Ferroelectric spike-enabled memristors also
benefit from potential multi-functionality such as concurrent piezoelectric,
pyroelectric, and/or multiferroic phenomena.
In terms offabrication, inorganic ferroelectric memristors require procedures that are
highly sensitive to initial conditions such epitaxial growth and complex
crystal growth optimisations such as use of specific substrates for lattice
matching. In contrast, ferroelectric polymers can easily be dissolved in an
organic solution and directly printed onto flexible substrates. This means that
their easy processing, low cost, and versatility offer feasible solutions for
large scale synaptic design and network formation while also having excellent
properties for use in bio-informational interfaces such as those required in
auditory learning devices due to their biocompatibility and low acoustic
impedance.
Despite thesegeneral advantages, the optimal methods for fabrication of ferroelectric
memristors for neuromorphic hardware based on polymers is not established.
Electropolymeric deposition could provide several advantages in terms of
fabrication based on polymers due to bench-top scale fab, rapid speed of deposition,
low cost, and use of flexible and soft substrates. Compared to methods such as
spin coating and screen printing the technique could allow for control over
thickness and resulting conductivity profiles. Electrical control of these
properties would aid in fine tuning of programmable resistance values and
intervals between high-resistance and low resistance states, emulation of
synaptic functionality evidenced by the cycle-to-cycle hysteresis loops of the
resulting polymeric electrode-ferroelectric-electrode (EFE) structure.
Apolymeric memristor “compiler” approach to fabrication of memristors is
developed and implemented based on the respective EFE structure via
electropolymeric deposition of the ferroelectric resistive switching layer.
Here, “compiler” is referred to as a method to assemble analogue ferroelectric
memristive elements via potentiostatic control of deposition of alternating EFE
layers. The implemented system uses a silver coated working electrode,
silver/silver chloride reference electrode, and steel wire counter electrode in
the potentiostatic experiment. The procedure is simple. First, a ferroelectric
resistive switching layer material based on an organometallic glycinate complex
is deposited onto the conductive substrate via electropolymeric deposition.
Then, a PEDOT: PSS top electrode layer is deposited on top of the ferroelectric
layer matching the geometry of the conductive substrate and allowing for
electrical connection.
Theferroelectric response of the 3D deposited structure has implications for
design methods in auditory neurotechnologies with the potential for more
economical fabrication of devices such as artificial cochleas, hair cells, and
other neuroprosthetics. In addition, the ferroelectric and piezoelectric
response of the metal glycinate complex suggests its existence in biopolymers
such as elastin and collagen [5]. Opening up the potential for devices that
match the biochemistry and acoustic impedance of the skin of the individual.
Finally, novel methods for acoustic sensing in neurorobotic applications could
be based on these properties.
Keywords: ferroelectricmemristors, memristive auditory neurotechnologies, fabrication methods, machine
audition.
REFERENCES
[1] Goodman,D. and Brette, R., 2010. Learning to localise sounds with spiking neural
networks.
Advancesin Neural Information Processing Systems, 23.
[2] Wu,X., Dang, B., Wang, H., Wu, X. and Yang, Y., 2022. Spike-Enabled Audio Learning
in Multilevel
Synaptic MemristorArray-Based Spiking Neural Network. Advanced Intelligent Systems, 4(3),p.2100151
[3] Tian,B., Liu, L., Yan, M., Wang, J., Zhao, Q., Zhong, N., Xiang, P., Sun, L., Peng,
H., Shen, H. and Lin,
T., 2019. A robustartificial synapse based on organic ferroelectric polymer. AdvancedElectronic
Materials, 5(1),p.1800600.
[4] Niu,X., Tian, B., Zhu, Q., Dkhil, B. and Duan, C., 2022. Ferroelectric polymers for
neuromorphic
computing. AppliedPhysics Reviews, 9(2), p.021309.
[5] Liu,Y., Cai, H.L., Zelisko, M., Wang, Y., Sun, J., Yan, F., Ma, F., Wang, P., Chen,
Q.N., Zheng, H. and
Meng, X., 2014.Ferroelectric switching of elastin. Proceedings of the National Academy of
Sciences, 111(27),pp.E2780-E2786.
***
2. AI,games, and the problem of scientific realism
AttilaEgri-Nagy
Akita International University, Akita, Japan
egri-nagy@aiu.ac.jp
How can we learn from superhuman black box AIs? We assume thatthe best way is to use them as an unlimited source of experiments. In other
words, a human user should take the role of a scientist to acquire knowledge:
form hypotheses, and test them with AIs. In the traditional application domain
of board games, a human player should devise a plan, explaining why a chosen
move is good, and then use the machine evaluation to see any possible faults.
Treating the human-AI relationship as a scientific situationbrings in all the philosophical issues of science itself. Here we will discuss
the problem of scientific realism, the question of whether the universe studied
by science exists or not. In our analogy, this question has some curious twists.
The explored universe, the complete game tree, does exist. However, in its
totality, it is still an unobservable entity. The obstacle is computational
complexity. In this talk, we will investigate this epistemological issue.
***
3. GrowingPoint Automata and DNA Computing
MasamiHagiya
University of Tokyo, Japan
hagiya@g.ecc.u-tokyo.ac.jp
We propose acomputational model in which a network around the nodes called ports is formed
with fibers elongated by the extension and branching of their growing points,
and various computations in the network (such as path formation, circuit
generation, matching, etc.) are realized by the signals transmitted along the
fibers. Growing points and ports havestates that are changed depending on the states of neighboring growing points
and ports. Since the model featuresgrowing points that are elongated according to their states, it is named
growing point automata (GPA) after the growing point language (GPL) proposed in
the study of amorphous computing. Inthis talk, we first give the simple model of growing point automata with an
example of path formation between ports. We then refine this simple model by taking transmission of signals alongfibers into account. We show that thereexists a weak bisimulation between the simple and refined models of path
formation. Finally, we discuss thepossibility of implementing the refined model with DNA molecules. We mainly examine self-assembly of DNA tiles,including so called active tiles that can change their states upon assembly.
***
4. Metabolism as one instantiation ofnatural computing
MartinRobert
Laboratoryof Microbial Systems Biology, Graduate School of Pharmaceutical Sciences,
KyotoUniversity, Japan
robert.martin.4m@kyoto-u.ac.jp
Metabolism can be definedas the network of cellular biochemical reactions at the core of mass and energy
exchange in living cells and organisms. One of its important role is the
maintenance of cellular coherence over time and growth. This is achieved by
continuous uptake of nutrients (inputs), their processing through the cellular
metabolic network and resulting in outputs of biomass, energy and waste that
allow to accomplish the objective of cellular maintenance and growth. It thus
represents a central element of cellular natural computing encoded in the form
of biochemical reactions. In this presentation I would like to introduce some
of the important concepts of metabolism that connect to natural computing.
There are interesting examples of optimization in metabolism that appear
conserved in different types of cells from bacteria, stem cells, cancer cells
or brain cells, that consume energy at a high rate. Bacterial biofilms may
provide a simple model to investigate this type of metabolic economy and the
management of shared resources in cellular communities and the resulting
conflicts. These apparent universal and sometimes paradoxical metabolic
responses provide some insight on a cellular economy related to natural
computing that needs to be further appreciated and investigated.
***
5. Merging Mathematical Models ofInformation for the Digital and Analog Computing
MarcinJ. Schroeder
IEHE Tohoku University Sendai Japan
mjs@gl.aiu.ac.jp
The distinction between analog anddigital information is understood as similar to the distinction between the
state of a physical system and observables. The paper presents a formalism based on closure spaces overarching both types of information and merging conceptual frames of diverse theories such as probability, quantum theory of physics, logic, and modal logics.
The most popular distinction between digital and analog computing introduced by von Neumann is
based on the difference between the symbolic, digital, and discrete
representation of numbers and their apparently continuous representation as
magnitudes characterizing the states of physical objects. In reality, although
this distinction is highly intuitive and seems to reflect objective differences
between the forms of information, it is purely conventional. The functioning of
all computing devices involves the manipulation of the physical states of their
operating systems and at the same time, the digital representation of numbers
is achieved by a conventional discretization of continuous magnitudes.
In this paper, I am usingthe distinction between analog and digital information and computing that I
introduced in my earlier work in which the difference between analog and
digital information is similar to the difference between the concepts of
physics characterizing physical systems by physical states (analog) and
observables (digital). This distinction in physics acquired fundamental
importance with the rise of quantum mechanics but was already present earlier.
I wrote “similar” because essentially identical distinctions can be identified
elsewhere. For instance, the foundations of probability theory can be built
starting from the concept of a family of events understood as measurable
subsets of an outcome space and proceeding to random variables, or
alternatively starting from an appropriate algebraic structure of random
variables and proceeding to special class of random variables that can be
interpreted as characteristic functions for subsets of an outcome set
corresponding to events.
The depth of thedistinctions in both quantum theory and in probability theory is rarely
recognized, at least not in an open way. In quantum theory, the issues are
hidden for instance by the use of ad hoc terminology of “hidden variables”
(that sounds better than the oxymoron “unobservable observables”). In
probability theory, the standard trick is to focus on just two special cases of
discrete and continuous random variables while excluding anything else that
causes trouble. Probably the closest to an honest denunciation of the forgotten
problems was the series of the 1998 Turin Lectures by Gian-Carlo Rota “Twelve
Problems in Probability No One Likes to Bring Up.”
Rota’s lectures addressednot only issues within probability theory but also in the study of information.
In particular, Rota addressed the issue of the formulation of the orthodox
information theory derived from probability theory while it should precede
probability. This concern was not new as it was already voiced by Kolmogorov a
long time ago when he proposed his solution in the form of a description of
algorithmic complexity. Kolmogorov’s solution did not bring a sufficiently
general solution and Rota directed the future inquiry toward a new logic of
information in terms of the lattice of partitions.
Quantum computing becamethe hottest topic of this century but it seems that here too the carriage was
placed in front of the horse. The usual approach is to study quantum computers
considered as a special case of a quantum system and quantum information is
just an engineering concept necessary for the use of such computing devices.
With the increasing role of information as the most fundamental physical
concept as promoted by Rolf Landauer (“Information is Physical”) and John
Archibald Wheeler (“It From Bit”) quantum theory should be derivable from
quantum information theory.
A closer look at theformalisms involved in quantum and probability theories brings into focus
another similar type of formalism developed in semantic inquiries of modal
logics based on the idea of possible worlds (initiated by Rudolf Carnap but
already considered by Leibniz), in particular in the frames of Kripke Semantic.
The need for a unifying formalism becomes more and more clear.
This paper is an attemptto use my earlier published version of a theory of information to develop an
overarching formalism for information in which all formalisms mentioned above
are special instances. Its conceptual framework is based on the theory of
closure spaces.
An information systemspecifying the type of information is a closure space. In this study, there is
no need to restrict this concept by additional conditions unless we proceed to
its application in one of its specific applications. Thus, information can be
of the geometric, topological, logical, or physical type associated with
additional defining conditions. However, here we want to consider a general
formalism. The logic of such a general information system is a complete lattice
of closed subsets of the information system. At this point, it is important to
indicate that the term “logic” has here a much more general meaning than usual
which only in the case of linguistic or probabilistic information systems can
be identified with the familiar Boolean lattice defined by the connectives
between propositions of some language. The less conventional use of this term
consistent with the approach of this paper can be found in quantum logics
defined on the closed subspaces of a Hilbert space (or alternatively on the
projectors on these subspaces) or in Rota’s logic of information identified
with lattices of partitions.
The instances of theinformation within an information system are filters (sometimes called dual
ideals) defined on the lattice of closed subsets (the logic of the information
system). Ultrafilters, principal filters, and prime filters characterize
special types of information. Since filters representing instances of the
information are defined on logics that are not necessarily Boolean lattices,
and the theory of filters is typically studied in this particular context (e.g.
Stone Theorem) it is important to be aware of the ramifications of the theory
of filters when we transcend the Boolean context. For instance, ultrafilters
are not necessarily prime filters anymore, which is a fact that frequently
confuses physicists in discussing the question of hidden variables.
The formalism based onfilters reflects structural characteristics of information and filters
represent a state of some universe of inquiry (quite frequently simply called
“possible worlds”). Information defined or characterized as filters can be
identified as analog type. However, we have an alternative tool for inquiry of
information referring to observed numerical characteristics associated with
digital type. Here we have s-fields of subsets and measures defined onthem. The measures can be arbitrary, associated with magnitudes characterizing
the objects of inquiry, can be restricted to probability type, or further
restricted to binary logical valuations. In each case, we can construct a
corresponding lattice that can be interpreted as a logic of information.
Furthermore, we can distinguish filters representing instances of information.
Finally, we can establishthe relationships between the analog and digital descriptions of information.
These relationships are complex and they heavily depend on the specifics of
information systems. In general, the closest and simplest relationships in the
Boolean type of information systems become more complex and ramified when their
logics are unconventional.
Thispaper is about information and its analog and digital forms. However, it
contains as a byproduct some explanations of exotic features (from the point of
view of Boolean logic) of quantum information. For instance, some controversies
in the discussion of the so-called hidden variables can be resolved by
clarifying confusion caused by the import of our common-sense concepts to the
study of quantum theory. The issue of the unavoidable incompleteness of the
quantum-theoretical description of reality can be resolved not by a
demonstration of the absence of hidden variables but rather by using the simple
lattice-theoretical basic fact that a distributive lattice cannot have a
non-distributive sublattice or in other words, we cannot injectively extend a
non-distributive lattice to distributive one.
***
6. Inter-InduceComputation, as a model of Computing with Tactile Sense
YasuhiroSuzuki
Graduate School of Informatics, Nagoya University Japan
ysuzuki@nagoya-u.jp
Tactile communication isa typical interaction in nature. Not only among animals such as humans but also
in molecules, and cells, no matter how it is between living systems or between
substances, it plays a crucial role in information processing.
Herewe define "information processing" as computing with an algorithm and
its algorithm as a sequence of instructions. So, algorithms are not only for
computers; even brushing teeth has its algorithm; putting on teeth paste on the
brush, then brushing teeth, and finally rinsing off bubbles with water. We can
change this sequence; for example, rinse off the teeth, then put on teeth paste
and brush; every instruction is equal to an instruction in the former sequence,
but the "result of computation" is different. We call changing a
sequence "programming.”
There are full ofalgorithms in nature; a sequence of glycans controls interactions between
influenza viruses and cells, a sequence of DNA / RNA processing proteins
compose living systems. And so on. It is essential that almost all the
information processing is performed by tactile communications; glycan, DNA, or
RNA does not have any "nerve systems" to recognize, such as visual or
auditorial sense. All these substances use tactile communication to recognize
the molecule's shape and process information.
The significantcharacteristic of tactile communication is the bi-directionality of
interaction; touching something is simultaneous with being touched by it. In
nature, many tactile communications are composed of multi-agents. Mostly, these
agents have different "aims" to communicate; for example, the aim of
the influenzas virus is the infection of a cell, and the aim of a cell
communicating with the virus is preventing its infection.
Theycompute with the algorithm of the sequence of glycans; this computing is
different from ordinal computing problems such as solving a maze or sorting
numbers. The goal of computing is to induce communicating agents to an
"ideal state," such as infection to a cell or repelling an infectious
virus. We call such computing with bidirectional interactions as Inter-Induce
Computing, IIC. We propose a framework of IIC and show some preliminary
results.
***
7. A study on theability to regulate inverter for turn reaction in pill bugs
AkikaUtsumi, Toru Moriyama
Graduate School of Biomedical Engineering, ShinshuUniversity, Japan
21BS202A@shinshu-u.ac.jp toru@shinshu-u.ac.jp
Pill bug is an arthropodof about 1 cm in length that lives under fallen leaves. When the experimenter
gives the animal two T-mazes in succession, it turns alternately left and right
with a probability as high as about 80%. It is thought that pill bugs are
equipped with inverters for turning. The success rate of this alternating turn
decreases as the distance between the intersection of the two T-mazes (usually
5-10 cm) is increased, reaching about 50% at 16 cm (Iwata & Watanabe,
1957). When a pill bug equipped with these properties for turning was given
approximately 2,000 consecutive T-maze trials, it was observed to regulate the
number of alternating turns in a continuous manner (Shokaku et al, 2020). This
animal is thought to have a neural circuit that regulates inverter for turning.
To explore new properties of this neural circuit, we conducted experiments at
distances of 16 and 30 cm between maze intersections to investigate how the
pill bug adjusts the inverter when memory of prior turns is lost. The results
of the analysis of the experimental data will be presented at the workshop.
