MIND AS A LAYERED NETWORK OF COMPUTATIONAL
PROCESSES ALL THE WAY DOWN TO QUANTUM
Abstract. Talking about models of cognition, the very mention of “computationalism” often incites reactions
against Turing machine model of the brain and perceived determinism of the computational model. Neither of
those two objections affects models based on natural computation or computing nature where model of
computation is broader than deterministic symbol manipulation. Computing nature consists of physical
structures that form levels of organization, on which computation processes differ, from quantum level up. It has
been argued by (Ehresmann, 2012) and (Ghosh et al., 2014) that on the lower levels of information processing in
the brain finite automata or Turing machines might be adequate models, while on the level of the whole-brain
information processing computational models with beyond-Turing computation is necessary.
Critique of Computationalism Answered by New Understanding of Computation
Historically, computationalism has been accused of many sins. In what follows I would like to answer three Mark
Sprevak’s concerns about computationalism, (Sprevak, 2012) p. 108:
(R1) Clarity: “Ultimately, the foundations of our sciences should be clear.” Computationalism is suspected to lack
clarity.
(R2) Response to triviality arguments: “(O)ur conventional understanding of the notion of computational
implementation is threatened by triviality arguments.” Computationalism is accused of triviality.
(R3) Naturalistic foundations: “The ultimate aim of cognitive science is to offer, not just any explanation of
mental phenomena, but a naturalistic explanation of the mind.” Computationalism is questioned for being formal
and unnatural.
Sprevak concludes that meeting all three above expectations of computational implementation is hard, and that
“Chalmers’ account provides the best attempt to do so, but even his proposal falls short.” In order to be fully
understood, Chalmers account, I will argue, should be seen from the perspective of intrinsic, natural computation
instead of conventional designed computer. Based on computational formalism of combinatorial state automata,
he argues:
“Computational descriptions of physical systems need not be vacuous. We have seen that there is a wellmotivated formalism, that of combinatorial state automata, and an associated account of implementation, such
that the automata in question are implemented approximately when we would expect them to be: when the
causal organization of a physical system mirrors the formal organization of an automaton. In this way, we
establish a bridge between the formal automata of computation theory and the physical systems of everyday life.
We also open the way to a computational foundation for the theory of mind.” David Chalmers (Chalmers, 1996)
In the above it is important to highlight the distinction between intrinsic /natural/ spontaneous computation that
describes natural processes at different levels of organization and is always present in physical system and
designed/conventional computation which is used in our technological devices and which uses intrinsic
computation as its basis.
The idea of computing nature (Dodig Crnkovic & Giovagnoli) builds on the notion of physical computation,
described in (Piccinini, Stanford encyclopedia) (a few words on physical computation here). [Here introduce infocomputationalism Floridi Burgin Marcin] In the framework of info-computationalism, which is a variety of
pancomputationalism, physical nature spontaneously performs different kinds of computations that present
information dynamics at different levels of organization. This intrinsic computation of a physical system can be
used for designed computation, which would not appear spontaneously in nature, but with constant energy
supply and designed architecture performs computations such as found in conventional designed computational
machinery.
Natural/Intrinsic Computation and Physical Implementation of Computational
System
Why is natural computationalism not vacuous in spite of the underlying assumption of the whole of the universe
being computational? It is not vacuous for the same reason for which physics is not vacuous which makes the
claim that the entire physical universe consists of matter-energy and builds on the same elementary building
blocks – elementary particles. Here we will not enter the discussion of ordinary matter-energy vs. dark matterenergy. Those are all considered to be the same kind of phenomena – natural phenomena that are assumed to
be universal in nature. The principle of universal validity of physical laws does not make them vacuous. Thinking
of computation as implementation of physical laws on the fundamental level makes it more obvious that
computation can be seen as the bass of all dynamics in nature.
Introduction to Focus Issue: Intrinsic and Designed Computation:
Information Processing in Dynamical Systems—
Beyond the Digital Hegemony
James P. Crutchfield,1,a! William L. Ditto,2,b! and Sudeshna Sinha3,c!
Causation is transfer of information (Collier, 1999) and computation, as information processing, is causation at
work, as argued in (Dodig-Crnkovic, …). What are the implications of this view for computing nature in general
and computational models of mind in particlular?
Top-Down Causation and the Rise of Information in the Emergence of Life Sara Imari Walker
Walker SI. Top-Down Causation and the Rise of Information in the
Emergence of Life. Information. 2014; 5(3):424-439.
CARL F. CRAVER, and WILLIAM BECHTEL, Top-down causation without top-down causes, Biology and Philosophy
(2006)
In his Open Problems in the Philosophy of Information Floridi (2004) lists the five most interesting areas of
research for the nascent field of Philosophy of Information (and Computation), containing eighteen fundamental
questions as follows:
17. The “It from Bit” hypothesis: Is the universe essentially made of informational stuff, with natural
processes, including causation, as special cases of information dynamics?
KAMPIS: CAUSAL DEPTH
Network of causal connectedness
Andre Ehresmann talks about synonymity
Memory Evolutive Systems
However, Marcin Miłkowski suggests “the physical implementation of a computational system – and its
interaction with the environment – lies outside the scope of computational explanation”.
”For a pancomputationalist, this means that there must be a distinction between lower-level, or basic,
computations and the higher level ones. Should pancomputationalism be unable to mark this distinction, it will be
explanatorily vacuous.” (Miłkowski, 2007)
From the above I infer that the model of computation, which Miłkowski assumes is a top-down, designed
computation. Even though he rightly argues that neural networks and even dynamical systems can be
understood as computational, Miłkowski does not think of intrinsic computation as grounded in physical process
driven by causal mechanism, characteristics of computing nature.
The problem of grounding. Where the following question comes from?
If we would apply the above logic, we would demand from physicists to explain where matter comes from.
Where do the elementary particles come from? They are simply empirical facts for which we have enough
evidence. We might not know all of their properties and relationships, we might not know all of them, but we can
be sure at least that they exist. The bottom layer for the computational universe is the bottom layer of its
material substrate, which with constant progress of physics is becoming more and more fine-grained.
Mind as a process and Computational Models of Mind
Of all computational approaches, the most controversial are the computational models of mind. There exists
historically a huge variety of models, some of them taking mind to be a kind of substance opposed to matter. It is
more natural for computational approaches to consider mind as a process, a complex process of computation on
many different levels of organization.
“To sum up: mind is a set of processes distinguished from others through their control by an immanent end. (…)
At one extreme it dwindles into mere life, which is incipient mind. At the other extreme it vanishes in the clouds; it
does not yet appear what we shall be. Mind as it exists in ourselves is on an intermediate level.” (Blanshard, 1941)
Within info-computational framework, cognition is understood as synonymous with process of life, a view that
even Brand Blanshard adopted. Following Maturana and Varela’s argument from 1980 (Maturana & Varela,
1980), we can understand the entire living word as possessing cognition of various degrees of complexity. In that
sense bacteria possess rudimentary cognition expressed in quorum sensing and other collective phenomena
based on information communication and information processing. Brain of a complex organism consists of
neurons that are networked communication computational units. Signalling and information processing modes
of a brain are much more complex and consist of more computational layers than bacterial colony. Even though
Maturana and Varela did not think of cognition as computation, the broader view of computation as found in
info-computationalism is capable of representing processes of life as studied in bioinformatics and
biocomputation.
Relation between mind and cognition [Marcin’s book]
http://www.scholarpedia.org/article/Mind-body_problem:_New_approaches
Starting with mind as life itself, a single cell, and studying increasingly more complex organisms such as rotifers
(which have around a thousand cells, of which a quarter constitute their nervous system with brain) or the tiny
Megaphragma mymaripenne wasps (that are smaller than a single-celled amoebas and yet have nervous system
and brains) – with more and more layers of cognitive information-processing architectures we can follow the
evolution of mind as a capacity of a living organism to act on their own behalf:
“(W)herever mind is present, there the pursuit of ends is present”. (…) ”Mental activity is the sort of activity
everywhere whose reach exceeds its grasp.” (…) “Now mind, at all of its levels and in all of its manifestations, is a
process of this kind” [i.e. a drive toward a special end]. (Blanshard, 1941)
And the process powering this goal-directed behavior on a variety of levels of organization in living organisms is
information self-organization. Andre Ehresmann (Ehresmann, 2012) proposes the model of brain where lower
levels are made of Turing machines while the higher levels of cognitive activity are non-Turing, based on the fact
that the same symbol has several possible interpretations. In contrast, Subrata Ghosh et al. (Ghosh et al., 2014)
remarkable brain model demonstrates how mind can be modelled from the level of quantum field theory up to
the macroscopic whole-brain, in twelve levels of computational architecture, based on computing beyond Turing
model.
REFERENCES
Blanshard, B. (1941). The Nature of Mind. The Journal of Philosophy, 38(8), 207–216.
Chalmers, D. J. (1996). Does a Rock Implement Every Finite-State Automaton? Synthese, 108, 309–33.
Collier, J. (1999). Causation is the transfer of information. In H. Sankey (Ed.), Causation, natural laws and
explanation (pp. 279–331). Dordrecht: Kluwer.
Ehresmann, A. C. (2012). MENS, an Info-Computational Model for (Neuro-)cognitive Systems Capable of
Creativity. Entropy, 14, 1703–1716.
Ghosh, S., Aswani, K., Singh, S., Sahu, S., Fujita, D., & Bandyopadhyay, A. (2014). Design and Construction of a
Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a
Supramolecular Organic, Inorganic System. Information, 5(1), 28–100. doi:10.3390/info5010028
Maturana, H., & Varela, F. (1980). Autopoiesis and cognition: the realization of the living. Dordrecht Holland: D.
Reidel Pub. Co.
Miłkowski, M. (2007). Is computationalism trivial? In G. Dodig-Crnkovic & S. Stuart (Eds.), Computation,
Information, Cognition – The Nexus and the Liminal (pp. 236–246). Newcastle UK: Cambridge Scholars
Press.
Sprevak, M. (2012). Three challenges to Chalmers on computational implementation. Journal of Cognitive
Science, 13(2), 107–143.
From the mail to Terry Deacon
The central claim that I wish to reflect upon is how a system "registers"* information
(and a connected question why it "registers" some information and not the other).
From Deacon’s text (private communication):
"The far-from-equilibrium case is of major importance also because it provides the
foundation for an analysis of the nature of an interpretive process (see next section).
A simple exemplar of a far-from-equilibrium information medium is a metal
detector."
There is for example connection to:
Kaneko, K. and Tsuda, I. "Complex systems: chaos and beyond. A constructive approach with applications in life sciences",
Springer, Berlin/Heidelberg, 2001.
http://books.google.se/books?id=7lcINfgupggC&pg=PA265&dq=Complex+Systems:+C
haos+and+Beyond+by+K.+Kaneko;+I.+Tsuda&hl=en&sa=X&ei=5RVHU8f-E6SbygGmuYGwCA
&ved=0CC0Q6AEwAA#v=onepage&q=Complex%20Systems%3A%20Chaos%20and%20Beyond%20
by%20K.%20Kaneko%3B%20I.%20Tsuda&f=false
They discuss different topics, but this seems to me be of interest in connection to
Deacon’s paper:
Dynamics of living organism creates a sensitive state that reacts on the changes in
the environment.
“Those data are in accordance with the dynamic viewpoint of the brain as proposed
by Tsuda, which can be summarized as follows: a neuron and a neuron assembly are
not structured to reveal a single function, but structured such that they can
implement multiple functions according to the internal states of the brain and the
external environment.
Furthermore, these activities should reveal temporally complex behavior, which
perhaps is related to the chaotic itinerancy. Thus the study of the relational
dynamics among the elements involved in the information processing of the brain
will be an important issue.” P.18
This in turn may be expressed in terms of von Foersters notions of eigenvalues
(stable structures) and eigenbehaviors (stable behaviors established in the
interaction with the environment):
“ Any system, cognitive or biological, which is able to relate internally, selforganized, stable structures (eigenvalues) to constant aspects of its own interaction
with an environment can be said to observe eigenbehavior. Such systems are
defined as organizationally closed because their stable internal states can only be
defined in terms of the overall dynamic structure that supports them.” (Rocha 1998:
342)
Rocha L. M. (1998) Selected self-organization and the semiotics of evolutionary systems. In: Salthe S., Van de Vijver G. & Delpos
M. (eds.) Evolutionary systems: Biological and epistemological perspectives on selection and self-organization. Kluwer,
Dordrecht: p. 342
Similarly, the following thought may be found in Gilbert Simondon's discussion of
form-information relationship:
"The notion of form must be replaced by that of information, which implies the
existence of a system in metastable equilibrium that can individuate; information,
the difference in shape, is never a single term, but the meaning that arises from a
disparation (disappearance)."
Simondon, Gilbert (2007) L'individuation psychique et collective. Paris: Editions Aubier (p. 28, translation by Andrew Iliadis)
In the above picture of a metastable state capable of reacting to the relevant
changes in the environment, two things are particularly interesting (apart from the
meta-stability itself): time-dependence (dynamics) and fractality.
Both are addressed in a radically new approach to the modeling of a whole brain in
the following article by (Ghosh et al., 2014):
Ghosh, Subrata; Aswani, Krishna; Singh, Surabhi; Sahu, Satyajit; Fujita, Daisuke; Bandyopadhyay, Anirban. 2014. "Design and
Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a
Supramolecular Organic, Inorganic System." Information 5, no. 1: 28-100.
http://www.mdpi.com/2078-2489/5/1/28
What seems interesting in this context is that the central point in the above model is
the collective time behavior and (fractal) frequencies at number of different levels of
organization. (Ghosh et al., 2014) suggest a possibility of connection between levels
from QFT (Quantum Field Theory) up to macroscopic levels based on time
dependence of physical oscillators involved. It might not be an adequate model of a
brain, but as Bohr model of atom it can contain some interesting insights. (refer to
Basti here)
“Here, we introduce a new class of computer which does not use any circuit or logic
gate. In fact, no program needs to be written: it learns by itself and writes its own
program to solve a problem. Gödel's incompleteness argument is explored here to
devise an engine where an astronomically...
Abstract: Here, we introduce a new class of computer which does not
use any circuit or logic gate. In fact, no program needs to be written: it
learns by itself and writes its own program to solve a problem. Gödel’s
incompleteness argument is explored here to devise an engine where
an astronomically large number of “if-then” arguments are allowed to
grow by self-assembly, based on the basic set of arguments written in
the system, thus, we explore the beyond Turing path of computing but
following a fundamentally different route adopted in the last half-a-
century old non-Turing adventures. Our hardware is a multilayered seed
structure. If we open the largest seed, which is the final hardware, we
find several computing seed structures inside, if we take any of them
and open, there are several computing seeds inside. We design and
synthesize the smallest seed, the entire multilayered architecture grows
by itself. The electromagnetic resonance band of each seed looks
similar, but the seeds of any layer shares a common region in its
resonance band with inner and upper layer, hence a chain of resonance
bands is formed (frequency fractal) connecting the smallest to the
largest seed (hence the name invincible rhythm or Ajeya Chhandam in
Sanskrit). The computer solves intractable pattern search (Clique)
problem without searching, since the right pattern written in it
spontaneously replies back to the questioner. To learn, the hardware
filters any kind of sensory input image into several layers of images,
each containing basic geometric polygons (fractal decomposition), and
builds a network among all layers, multi-sensory images are connected
in all possible ways to generate “if” and “then” argument. Several such
arguments and decisions (phase transition from “if” to “then”) selfassemble and form the two giant columns of arguments and rules of
phase transition. Any input question is converted into a pattern as noted
above, and these two astronomically large columns project a solution.
The driving principle of computing is synchronization and desynchronization of network paths, the system drives towards highest
density of coupled arguments for maximum matching. Memory is
located at all layers of the hardware. Learning, computing occurs
everywhere simultaneously. Since resonance chain connects all
computing seeds, wireless processing is feasible without a screening
effect. The computing power is increased by maximizing the density of
resonance states and bandwidth of the resonance chain together. We
discovered this remarkable computing while studying the human brain,
so we present a new model of the human brain in terms of an
experimentally determined resonance chain with bandwidth 10−15 Hz
(complete brain with all sensors) to 10+15 Hz (DNA) along with its
implementation using a pure organic synthesis of entire computer (brain
jelly) in our lab, software prototype as proof of concept and finally a new
fourth circuit element (Hinductor) based beyond Complementary metaloxide semiconductor (CMOS) hardware is also presented.
Keywords: Turing machine; Gödel’s incompleteness theorem; nonalgorithmic computing; self-assembly; wireless communication;
antenna; receiver; electromagnetic resonance; synchronization; brainlike computer; creative machine; intelligent machine; conscious
machine
*This expression I borrow from Brian Cantwell Smith's "On the Origin of
Objects"
Top-down causation without top-down causes
Carl F. Craver & William Bechtel
Biology and Philosophy 22 (4):547-563 (2007)
Abstract
We argue that intelligible appeals to interlevel causes (top-down and bottom-up)
can be understood, without remainder, as appeals to mechanistically mediated
effects. Mechanistically mediated effects are hybrids of causal and constitutive
relations, where the causal relations are exclusively intralevel. The idea of causation
would have to stretch to the breaking point to accommodate interlevel causes. The
notion of a mechanistically mediated effect is preferable because it can do all of the
required work without appealing to mysterious interlevel causes. When interlevel
causes can be translated into mechanistically mediated effects, the posited
relationship is intelligible and should raise no special philosophical objections. When
they cannot, they are suspect.
The software/wetware distinction
Comment on “Toward a computational framework for
cognitive biology: Unifying approaches from cognitive
neuroscience and comparative cognition” by W. Tecumseh
Fitch
DanielDennetta,b
aTufts University, United States1
bSanta Fe Institute, United States2
Received23 May 2014; accepted26 May 2014
Fitch WT. Toward a computational framework for cognitive biology: unifying
approaches from cognitive neuroscience and comparative cogni-tion. Phys Life
Rev 2014. http://dx.doi.org/10.1016/j.plrev.2014.04.005[this issue].
« 3 » An MES gives a constructive
model for a self-organized multi-scale cognitive
system that is able to interact with
its environment through information processing,
such as a living organism or an
artificial cognitive system. Its dynamics
is modulated by the interactions of a network
of specialized internal agents called
co-regulators (CRs). Each CR operates at its
own rhythm to collect and process external
and/or internal information related to its
function, and possibly to select appropriate
procedures. The co-regulators operate with
the help of a central, flexible memory containing
the knowledge of the system, which
they contribute to develop and adapt to a
changing environment.
« 4 » In an MES, a central role is played
by the following properties of information
processing in living systems: (i) The system
not only processes isolated information
items, but also takes their interactions into
account by processing information patterns,
that is patterns of interconnected information
items. (ii) The MES satisfies a multiplicity
principle (MP), asserting that several
such information patterns may play the
same functional role once actualized, with
the possibility of a switch between them
during processing operations. This principle
formalizes the degeneracy property that is
ubiquitous in biological systems, as emphasized
by Edelman (1989; Edelman & Gally
2001). It permits Gregory Bateson’s sentence
(Åò21) to be completed into “a difference that
makes a difference, but also may not make a
difference.” The MP is at the root of the flexibility
and adaptability of an MES; it will also
be responsible for the non-computability of
its global dynamics.
« 5 » Once actualized in the MES, an information
pattern P will take its own identity
as a new component cP of a higher complexity
order, which “binds” the pattern, for instance
as a record of P in the memory. The binding
process is modeled by the categorical colimit
operation (Kan 1958): cP becomes the colimit
of P and also of each of the other functionallyequivalent information patterns; thus
it acts as a multi-facetted component. Such
multi-facetted components are constructed
through successive complexification processes
(Ehresmann & Vanbremeersch 2007). The
complexification also constructs the links
interconnecting two multi-facetted components
cP and cQ. There are simple links, which
bind together a cluster of links between the