Seminar announcement (Revised 5 Mar 2020)

TITLE: New developments in the Meta-Configured Genome theory.
4pm Thurs 5th March 2020
Computer Science Common Room

   Aaron Sloman and Peter Tino (Computer Science)
   [Absent collaborator: Jackie Chappell (Biosciences)]


This joint presentation follows on from the presentation by AS on 2nd Sept 2019,
on the idea of a Meta-Configured Genome (MCG), developed previously with JC, in
his practice talk for the Models of Consciousness conference September 9 - 12, 2019
in the Oxford Mathematical Institute:
    Recordings of the conference talks are available here:

Key MCG ideas, developed in collaboration with Jackie Chappell, are elaborated in
some notes and a short video here:

After AS explains some of these ideas PT will make some remarks about how
discoveries concerning gene expressions may be relevant to the implementation of
our proposed mechanisms.

The central idea of MCG is that an organism's interactions with its environment
during earlier phases of gene expression can cause information to be collected
that provides 'parameters' for use during later stages of gene expression that
are only partly specified genetically. Examples where such parameterisation is
required include genetically specified control mechanisms for producing various
kinds of motion (e.g. walking, swimming, flying) where the details of control
have to be changed as an individual becomes larger and heavier, and
relationships between body parts change through growth. (Striking examples
include the different motion patterns available to horses at different stages of

In many cases the parameters specifying control of action have to be acquired
from the environment, e.g. sizes, weights and shapes of objects used as tools,
parts of nests, or missiles! In the case of cared for infants, such as young
orangutans, the mother will gradually extend the challenges of tasks that the
infant has to master, e.g. by moving to different parts of foliage, remaining
less close to the infant, and changing her speed of motion. In animals with high
spatial intelligence this learning produces ablities to reason about novel
situations -- using brain mechanisms that are not yet known, but may be related
to the mechanisms used by humans in making mathematical (e.g. geometrical and
topological) discoveries about possible and impossible structures and

Since statistics-based, probability-inferring, neural nets cannot represent
impossibility or necessity, and since a great deal of genetically triggered
or controlled development must depend on chemical processes involving the
genome, we conjecture that some of this learning about structures, including
discovery of impossibilities and necessary connections is implemented chemically,
not neurally, e.g. using unknown sub-neural chemical mechanisms, that may be
extensions of the chemically controlled developmental processes involved in
construction of nervous systems and other parts of a foetus or hatchling.

We know that chemistry-controlled gene expression is used to assemble extremely
complex biological structures of many kinds during individual development
(perhaps most spectacularly in building up a whole mature organism from a single
fertilised egg, including in some cases transforming one whole organism
(caterpillar, or larva) into another (so-called metamorphosis) there is a vast
amount of sub-neural chemical activity, we conjecture that unexplained aspects
of animal intelligence, including abilities to reason about The resulting
information structures formed from patterns plus parameters can then provide
parameters for later stages of gene expression. Such gene expression is
inherently compositional, with multiple layers of compositionality.

This means that at least some of the later evolved, later expressed, genetic
specifications are parametrised, and get some of their parameters used
during individual development from previously stored information acquired during
earlier stages of gene expression, by individuals acting in the environment when
triggered by a variety of cues, often without expectation of reward, since the
rewards cannot be known in advance. (This can be described as Architecture-Based
Motivation (ABM), as contrasted both with Reward-Based Motivation (RBM), as explained in
and "Free-Energy Minimization" theories, e.g.

Stored information (e.g. about which parameters work in which situations)
acquired as a result of such triggering, can be used later, when more recently
evolved aspects of the genome are activated. E.g. word forms and word meanings
acquired at one stage can be used as parameters for grammatical forms acquired
later. The competences acquired can then be used as components for more complex
competences that develop later.

New "genetic layers", based on more recently evolved portions of the genome,
generating new motives and behaviours, can be activated at various stages of
development, e.g. into puberty and beyond. We'll try to show that this can
produce far more powerful forms of learning than a multi-layer neural net
created from an early stage, e.g. soon after birth.

In particular, as AS and JC have previously suggested, the MCG theory seems to
be able to explain the ability of a shared multi-layer human genome to produce
multiple stages of language development in thousands of languages, that differ
at all levels from the basic sounds used (or signs, in the case of sign-languages),
up to modes of composition of phrases, sentences and beyond (e.g. However...,
Therefore..., Although...).
    [This work is partly inspired by the linguistic theories of Chomsky and
    other linguists, without accepting that these features are unique to evolution
    and development of linguistic competences. It is also likely to apply to
    internal forms of representation that are much older than human languages
    some of which are shared with intellignt non-human species.]

The MCG mechanisms may also be able to explain the development of spatial
understanding over many generations, each extending the learning opportunities
of successor generations, leading eventually to deep ancient mathematical and
engineering competences, such as produced discoveries of Archimedes, Euclid and
others -- still in use worldwide. JC's work on crows, parrots and orangutans
suggests that there are also relevant (but simpler) non-human examples.

Since genetic mechanisms are chemistry-based, the MCG theory implies that
important aspects of learning and development in humans and other animals make
use of changing sub-neural chemical structures and processes rather than the
currently fashionable statistics-based neural net mechanisms (which are
incapable of discovering or even representing topological and geometric
theorems). Identifying those chemical mechanisms is mainly a task for the

Peter Tino recently pointed out that changes in our understanding of the
complexities of gene expression -- since the early theories following the
discovery of the structure of DNA -- seem to be particularly relevant to the
role of parametrised structures postulated in the MCG hypothesis. He will
describe aspects of gene transcription discovered by biologists that vastly
enrich the ways in which proteins are produced based on genome codes, going far
beyond the original "Central dogma of molecular biology":
    genome-triplets of base pairs --> aminoacid --> protein.

Here is an excellent collection of mini videos totalling just over 7 minutes,
illustrating some of the processes involving gene expression:

Many variants have been discovered, including immune responses, as summarised
    After an encounter with a new pathogen, the adaptive immune system often
    "remembers" the pathogen, allowing for a faster response if the pathogen
    ever attacks again.

This is *chemical*, not *neural*, remembering. We suggest that, in a similar
way, a shared *meta-configured* genome implemented chemically can produce
dramatically different effects in members of the same species in different
physical terrain requiring different forms of behaviour, or in different
cultures, in the same species, though using far more complex mechanisms than
antibodies. This may explain why neurons contain such varied and complicated
chemical mechanisms, not required for the simple arithmetical oprations
postulated by neural net learning mechanisms.

As remarked above, one of the most striking examples of what the MCG theory
seeks to explain is the ability of the shared multi-layer human genome to
produce multiple stages of development of competences in thousands of languages,
that differ at all levels from the basic sounds used (or signs, in the case of
sign-languages), up to modes of composition of words, phrases, sentences and
beyond. We suggest that that power is used in many other contexts.

The theory has deep implications for philosophy of mathematics and philosophical
questions about consciousness, insofar as mathematical discoveries provide new
contents of consciousness -- e.g. awareness of impossibilities and necessary
connections, which could be related to spatial intelligence in other species, as
well as a huge variety of human examples.

This is part of the core of Kant's philosophy of mathematics, and does not seem
to be explained by anything in current neuroscience: e.g. statistics-based
neural networks cannot discover or even represent impossibility/necessity. This
aspect of ancient mathematics is usually ignored in modern discussions of
logic-based foundations of mathematics, e.g. using axiomatic systems -- of types
unknown to ancient mathematicians -- or human toddlers, squirrels, orangutans
and others with sophisticated spatial reasoning and manipulation capabilities.
(There is no evidence to suggest that they are all unwittingly using logical
forms of representation and have logic-based theorem provers in their brains.)

Although there is a great deal of evidence for chemistry-based information
processing in organisms, including chemical control of development of body parts
and control mechanisms, including nervous systems and brains, I don't think
anyone knows how those mechanisms could produce spatial intelligence including
abilities to build and use tools, make nests, peel and eat bananas, crack open
and eat nuts, manipulate unfamiliar objects and watch over their offspring as
they develop spatial intelligence in increasingly challenging contexts.

This research is still in its early phases. We hope this seminar will inspire
other researchers with relevant knowledge and expertise to contribute relevant
background information, criticisms, and suggestions for improvement. In
particular, its implications for mathematical consciousness may require major
changes in philosophical, psychological, and neural theories of consciousness.

Alan Turing's thoughts
Can digital computers ever replicate all the capabilities of sub-neural
chemistry-based computations using a mixture of discrete and continuous
processing? Perhaps Turing thought not, in 1952, when he published "The chemical
basis of morphogenesis". He had previously (in 1936) distinguished mathematical
intuition and mathematical ingenuity, suggesting that computers are capable only
of the latter. (There are debates about whether he changed his mind later.)

Time permitting, examples of various kinds of spatial reasoning, including
familiar geometrical discoveries reported long ago in Euclid's Elements will
be presented that provide challenges both for logic-based geometry theorem
provers and statistics-based Deep learning systems.

[A disorganised collection of examples can be found here, including links to
more examples:]

The evolutionary products we describe are examples of the *metaphysical* creativity
of biological evolution, which continually produces new *types* of entity,
illustrating the concept of metaphysical causation developed by Alastair Wilson,
Birmingham philosopher.

We invite anyone interested in learning more about this research, or in
contributing to it in any way to contact any or all of:
    Aaron Sloman
    Peter Tino
    Jackie Chappell

We are particularly keen to learn about information known to neuroscientists
that could be relevant to explaining the phenomena we describe, including
ancient forms of mathematical intelligence concerning spatial impossibilities
or necessites in humans (some mentioned above) and related forms or spatial
intelligence in other species. 

Please report errors or omissions to
Aaron Sloman (a.sloman AT
5 Mar 2020