Comments for Nik Kasabov: "Evolving Connectionist Systems" book
Springer-Verlag London Limited, 2003
Bill Howell, January 2004

	1.  Introduction & Contents

Nik, you'll probably get a laugh out of this.  It rambles on a bit (actually, a hell of a lot), and gets WAAYY off track, but maybe there's a point here and there that may provide encouragement, provoke a reaction, or be of interest to you.  It's all speculation and playing with ideas, most of which are related to your book or which your book reminded me of, so please don't take it too seriously.  Funny, without my change in job responsibilities last October (I'm no longer managing 24 R&D personnel) I wouldn't have had time to comment like this, as I no longer spend almost all nights and weekends doing my "real job".

I suggest that you selectively skim through, glancing at themes of interest to you, ignoring the rest.  I certainly wouldn't hope that even half would be of interest to you.  Perhaps the most interesting would be:
- Lamarckian versus Mendelian heredity
- Multi-[phase/function/object] Neural Modules
- Mindcode (brief intro)

Bill Howell
bhowell@nrcan.gc.ca

Table of Contents
1.  Introduction & Contents	1
1.1  Overall Comments	2
1.2  What I liked about the organization & style of the book	3
2.  Themes that are more-or-less directly related to the book	3
2.1  Localised learning	3
2.2  Problem Decomposition/ Modularisation	4
2.3  Hybrid Systems – especially fuzzy+neural, also evolutionary	4
2.4  Centroid versus Boundary Approaches 	5
2.5  Genetics – and computational neurogenetic modelling	5
2.6  Medellian vs Lamarckian Heredity	6
2.6.a The case for Lamarckian heredity in biology	8
2.6.b Where might Lamarckian evolution best express itself?	9
2.7  Critical Role of Evolution in Learning (David Fogel)	9
2.8  Nature vs Nurture Debate (p211)	10
2.8a   The Trouble with Dichotomies	11
3.  Themes not directly related to the Book	11
3.1  Decisions & Selections, Mini-Max Theory	11
3.2  Mindcode – the project I never seem to get around to	12
3.3  Communities of substantially pre-wired Functional NNs	13
3.3a   Multi-[phase/function/object] Neural Modules (here called M&Ms) 	13
3.3.a.i  Multi-phase structure	14
3.3.a.ii  Multi-function capability (functional overloading)	14
3.3.a.iii  Multi-object capability	15
3.3.a.iv  Messaging between modules	15
3.3.a.v  Training of M&Ms	15
3.3.a.vi  Stability of M&Ms, and communities of M&Ms	16
3.3b   Logic vs Understanding through Knowledge of Functional Behaviour	16
3.4  Design Patterns (Gang of Four), Problem Decomposition/Modularisation, Re-usability of NNs/ECOS, 	17
3.5  Post-calculus mathematics & science	17
3.6  Constraint-Seeking ?modular NNs?	18
3.7  Stability Analysis	18
3.8 Other Themes Not Discussed	18
4.0  References (some, anyways)	19

	1.1  Overall Comments

I am very glad to have read this book, as it is a powerful overview and coherent presentation of the benefits of "Evolving Connectionist Systems" (ECOS).   Localized, incremental [learning, clustering] really stood out especially in the form of   By comparing the results of ECOS models to other standard classical and NN approaches, the ECOS advantages in terms of speed, accuracy, real-time on-line learning, and expressive power for complex environments/ problems become crystal clear.  Other approaches that are closely related to ECOS (or which are different forms of ECOS to the author's own approaches) are also described, again putting the author's work and concepts into context.  This approach significantly increases the reader's confidence in the ECOS concepts.

I suspect that many others in the neural network community are in a similar situation as I was.  Although familiar with radial basis functions, Adaptive Resonance Theory and its derivatives, Support Vector Machines, and only passingly aware of Growing Cell Structures and Neural Gas Models, I was not giving sufficient priority and attention to the importance of localized learning models and their suitability for incremental learning (although this has long been a strength of ART).  In a sense, it is like a wake-up call to abandon the allure of the "Grand Unified Theory" of problem solutions – and seek robust local representations that DON'T have to be tightly linked (as real problem spaces can be like that).  Further generalizations/ consolidations are often possible (for example – the author's "sleep learning" description), but sometimes might be best done with time after a robust/ reliable collection of local solutions is found by ECOS-like approaches.

A strong mental shift towards local/evolving learning models isn't trivial – it is very significant and powerful. It is also liberating in no small measure – as the local learning perspective gives you far more flexibility, and allows you to easily use a combination of tools, each of which is suited for different parts of state-space.  Furthermore, it appears to greatly reduce  the concerns over being trapped in local minimum and other learning problems (stability versus plasticity, ?? vs??).

	1.2  What I liked about the organization & style of the book

1.2.1  Broad coverage – of ECOS models, and classical & NN alternatives is an important strength of the book, without which it would be difficult to put the subject in perspective.  Key subjects covered include:
•	clustering/ classification/ recognition – including audio, image, genomics, etc.  Both unsupervised/ supervised learning are covered. knowledge discovery ...
•	signal analysis – especially phoneme/ speech recognition
•	ensembles of ECOS systems – several ways of combining them were described
\
Not covered (but the book naturally leads into these) are:
•	control theory -
•	advanced means of building structured/ hierarchical ECOS systems -  while the author does cover this subject   need to cover – "finding variables", communications, "overloading of modules and ensembles to get more for cost of networks, data compression (see also Slotine & Lohmiller – composite variables)
•	the use of evolved functional NNs/ hybrid systems - fixed-weight neural nets, Extreme Learning Machines, ..."reusable EFuNNs"
•	deriving estimates of error/variance for the models -  not only an estimate of the value of a function, but the uncertainty associated with it is needed (?wasn't there an example in the book?)

1.2.2  Context -  As mentioned elsewhere in this review, a real strength of the book is that it puts the concepts into context and perspective – comparing results with classical algorithms and standard NN approaches.  Standard problem sets are often used – making comparisons and verification/ follow-up much easier for he reader.

	2.  Themes that are more-or-less directly related to the book

	2.1  Localised learning

The following benefits of the ECOS approach are well illustrated by the book:
•	stability (avoiding catastrophic loss of memory when learning new data).  Stability isn't a trivial issue – robustness and reliability are far more important than optimality for a survivable system.
•	on-line learning or one-pass batch learning, 
•	expressive power for complex environments, 
•	a natural or more transparent self-explanation – rules naturally fall out (EFuNNs) or system behaviour may be easier to derive/interpret (other ECOS) thanks in part to local learning

Furthermore, the route to high-order abstraction and/or more generalize models is explored and described (eg consolidation of local regions/ reduction of nodes as in sleep learning).

Boundaries (for a discussion of this theme see "Centroid versus Boundary Approaches ").

Overall, the approach and examples illustrate well how important it is to avoid falling into a "top-down, grand unified theory" approach to modeling the entire state-space (which may never result in a satisfactory model, or may take forever to get there).  In general it is likely preferable as a starting point to take a "bottom-up, local-to-regional" approach, where good models develop over regions, but may never cover the full state space.  Instead, a collection of good, robust local/regional models covers state space.  After this first step, perhaps approaches to further generalize/ model/ abstract will kick in (hybrid approaches, not necessarily leading to "grand unified theories"), but depending on the objectives, that may not even be necessary or worthwhile.

	2.2  Problem Decomposition/ Modularisation

It seems to me that as individuals, or as project teams tackling complex problems, a key approach that we often use is to simplify the problem analysis and solution by "divide-and-conquer" modularisation.  This basic activity should also be key for NNs, and while clustering etc are commonly achieved by NNs in the "problem domain", there doesn't seem  to be as much effort directed at doing this with NN architectures and learning methods themselves.  

Localised learning (bottom-up) alone can achieve a first step towards this.  Likely a combination of mid-to-high level "models, understanding, classification, ..." and recognition of input/ or output patterns is needed to go further (quite likely including abduction as one of the key tools – see the next sub-section below).

???...

	2.3  Hybrid Systems – especially fuzzy+neural, also evolutionary

Although you do a good job of describing hybrid systems and their advantages, I wasn't paying enough attention to contrasts between the Fuzzy-Neural (ESOMs, EFuNN) vs Neural-Fuzzy (HyFIS for Wang's method(?), DENFIS for Takagi-Sugeno inference).  Your more recent work with DENFIS seemed to indicate that is your current direction, but EFuNN seems still to the "basic" approach?  Maybe I should get back to this at a later time... I feel like I've been a bit lazy on this, especially in terms of posing questions and thoughts in this area.

While you have given comparative results showing the advantages of fuzzy input/output (EFuNN) and "cores (HyFIS & DENFIS),  what might help me would be a more specific set of illustrations showing the power of fuzzy components.  Those concentrated on fuzzy systems intuitively know and feel this, some of the rest of us do not have the intuitive feel and need to be reminded.  My current incomplete and naïve understanding is that (apart from the deeper equivalence of fuzzy and neural systems) an easy way of looking at it is fuzzy systems are very robust with respect to imprecise or insensitive data input, and aid in categorising outputs according to what "really matters".  Learning and abstraction are therefore facilitated, and can be faster, and there is perhaps a better understanding of what is really important.  This becomes even more critical for one-shot or real-time learning as with your ECOS.  It would be interesting to see examples that more clearly "isolate and illustrate" these effects (as the more general, overall functioning/ benefit of fuzzy systems or components is easier to see and has already been well illustrated in your book).

???Valdes' "neuro-fuzzysnap"
???Question of building reusable modules to accelerate learning, improve performance – and the question of logic versus "black box" (section below)

	2.4  Centroid versus Boundary Approaches 

Your book brought out a small theme that intrigues me – that is either a centroid-focussed (radial basis functions etc) versus a boundary-focussed (SVMs being first example that comes to mind of boundary perspective) approach to modeling a domain.  

Centroids – grainy borderline clusters, limits "connectedness" of generalizations, defocuses attention/ fragments the domain, speeds up initial learning
Boundary – powerful descriptions of boundaries, possibly better or more generalised description/ abstraction within irregularly-shaped regions.  Can it be that successful "strategizing" in a competitive environment requires a very acute sense of a "change in phase/ state/ situation/ risk scenario" for which very effective boundary sensing is crucial?

In the case of ECOS, your description of "guard nodes" during aggregation is very good – it wasn't entirely clear to me if that was a development of your own group, but if so it should have been clear stated.  

?Perhaps "sleep learning" would work on the "dual" boundary, and seek better generalizations (partly due to more connected, albeit irregular regions, partly due to abduction)  (?what was I trying to say here?)?

	2.5  Genetics – and computational neurogenetic modelling
There are hints throughout your book of the oncoming of your "computational neuro-genetic modelling" concept.  Unfortunately by now I've forgotten the detailed points – likely most (but certainly not all, if I remember correctly) is in Chapters 7 "Evolving Connectionist Machines – Framework, Biological Motivation and Implementation Issues" (p 146,.

One of the things that this reminds me of is the ability to "mask" or to "activate" capabilities and features that are genetically available.  To some extent this occurs with normal physiological processes, which are controlled by signaling and expression, but perhaps one can consider differing degrees (which I think you also mentioned in your book in a loosely-related way):
•	normal, routine physiology of cells/ systems
•	exceptional, panic, or stress modes of cells/ systems (different "programming")
•	diseased or pathological behaviour
•	"at conception selection" – could it be that at birth, rather than "only having" selected genetic code, that it may be quite common for a multiplicity of competing code to remain on the inherited genome, but that at conception only one of several competing "codes" is selected for life?  In this way individuals would have "multiple inheritance" and could pass on feature neither parent "could express" but in fact possess?  

Your (and Benuskova, Wysoski) paper "Computational Neurogenetic Modelling: Gene Networks within Neural Networks" has already addressed all of these points.  It seems to put much of the emphasis on "meta-learning" via parameters that evolve over time, the modelling of gene networks for biology, and on spiking neuron behaviour.  Is the spiking behaviour a means also of seeking ways of "searching state space" effectively for systems of EFuNNs?  The paper is necessarily only a starting point, so it will be interesting to see the thinking evolve.

And what about the mind – given that the tolerances for flexibility are probably far greater than for other biological systems (eg at one extreme controls for the heart, then "less-critical" functions like facial appearance, then the building blocks for thinking)?  See mindcode next section – or maybe move it to this section?

	2.6  Medellian vs Lamarckian Heredity

The crux of my theme in this section is that Lamarckian evolution may be a very nice description of processes that guide learning and adaptation of individuals during their lifetime, and of your concepts of "Evolving Connectionist Systems" and many of the short-term processes used for their "learning".  Other hereditable traits might also be interesting to consider (actually, I mostly think of father/son passing of skills, education systems, religions, and schools of thought in management, economics, market analysis, military weapons and strategies – as these are passing on entire concepts/belief systems and the appropriate actions to take).  Furthermore, while it may or may not be the case that "Lamarckian heredity" is passed on biologically (right now the evidence would suggest not  see below for a very speculative "discussion"), to me it is clear that "Lamarckian heredity" DOES occur at the level of human groups, organizations, cultures & religions, and societies (for simplicity or writing I'll use the term "society" to refer to all of these levels).  What has been learned and developed with time CAN BE passed on to other individuals, groups, organizations, cultures & religions, and societies.

This "Lamarckian heredity" theme constantly came to mind when reading your book.  It was also intensified last September 2004 (just before I dove into your book) when reading the "History of Psychology" ?author name?.  A small section in that book went over some of the thinking of the time. 

I'm a great fan of going back to earlier historical debates, which often cast light on the constraints and limitations of current thinking on an issue.  It has struck me for several years that the "Lamarckian" perspective on heredity/ evolution may have been prematurely declared dead in the latter part of the 1800's following the simultaneous publication of the theory of evolution by ?Michael Wallace? and Charles Darwin.  In the early 1800's (*18??) Jean-Baptiste Lamarck had expounded the view that changes in individuals over the course of their life were passed on to their progeny.  This perspective was adopted by Herbert Spencer and became an established view.  From what I have read, Charles Darwin favoured what I will call the "Mendellian heredity" explanation.  I refer to it a "Mendelian heredity" from a "retro-perspective", as Mendel's work wasn't recognised during his life and was only really "discovered" by the broader scientific community in the ?1920's? when the new genetics community was looking for supporting evidence for their theories.  After several ?decades? (18??-18??) of debate, the "Mendelian" viewpoint "beat" the "Lamarckian" camp.   

I think that it is appropriate to differentiate and use the term "heredity" rather than "genetics", as these are NOT exactly the same concept, and genetics were not really established until much later.  Glial cells are now suspected of having a more profound influence on brain function than previously thought, but so far do not change the "heredity versus genetic" distinction.  However, epigenetics deals with non-genetic influences over heredity, and who knows where this current line of thinking may end?

Furthermore, I also think that it is extremely important to differentiate the terms "genetics", which refers to DNA coding for genes, and "DNA code", which includes the so-called "junk DNA", which may turn into one of the great misnomers of scientific history (see the "Mindcode" section below), seriously mis-directing thought for a generation or two of scientists.

The real question is whether using the label "Lamarckian" really helps any analysis, or more likely in shifting mind-sets or thinking.  I think the answer to this is "yes" and "no".  It will help our thinking to make the distinction, but I suspect that we have, since the beginning of history, employed and perfected Lamarckian evolutionary principles in all areas that would be affected by it, including parenting, education, organisational principles (bureaucracies, companies, tax systems, trading conventions, market operations, legal, religious, state functions).  But is there some change in mathematical analysis, arising perhaps from computational intelligence's evolutionary theory, that could help generalize or improve all of the areas listed?  I suspect that there are changes that will benefit us, but that these will occur without invoking a Lamarckian perspective.  Perhaps changes could occur faster and better from a Lamarckian perspective... but one would have to demonstrate that the math does  make a difference. 

[Notice also that I have presented this theme in terms of a dichotomy – "Mendelian vs Lamarckian" – see "The Trouble with Dichotomies"]

	Biological	Computational (?and Psychogical, Organizational, Sociological?)	
Heredity of "learned or developed" skills, knowledge etc 	not believed to happen (no measure to detect low-level changes? - see below)	could easily be passed on, if desired or "liked/ preferred"	
Threat to "life" if 			
Effect on diversity of traits			

	2.6.a The case for Lamarckian heredity in biology

Simply put, I am not aware that anyone has experimental evidence that can support the concept of Lamarkian heredity in biology (not including learning, psychology, sociology etc), so this sub-section is purely speculative (but fun).  Perhaps there are long-forgotten results from the early 1900's, or from more recent studies that can provide some support, but I'll assume for now that this is simply a "what-if" conjecture.

While the experimental results do not support "directed DNA change" (Lamarckian biological heredity, here taken as it may affect gametes), it also seems clear that there has never been a practical means of measuring DNA changes for individuals over time.  With the human genome project, we now can measure single/multiple nucleotide polymorphisms, and there are limited means of relating single or simple changes to disease conditions (genotype – phenotype correlations).  This is a huge and important area of research activity.  But but as far as I know it probably still would be very expensive and difficult to track DNA changes with time for an individual, and vastly more difficult to differentiate "Lamarckian" from mutational changes in individual cells (like neurons) or groups of cells.  So we may yet see some surprises....

Worse yet, how can one conceive of a mechanism for changes to DNA in neuron cells to be reflected in DNA changes to the gametes?  At first look, this doesn't seem to be workable, but perhaps one can imagine several processes to accomplish just that in a semi-credible (not totally outlandish) manner.  Keep in mind that "credible" means according to our current theories about DNA & biology, a very immature and developing area which could hold many upsets and surprises that revolutionize our concepts:

•	true change to DNA – (single nucleotide polymorphisms).  I don't see any easy way to implement this within the context of my understanding of modern biology/ genetic theory! (I do not have a deep understanding of these areas).  It would require the discovery that chunks of DNA can be inserted/ removed outside of the context of DNA crossover during conception.  Presumably phages (as with bacteria), or random mutations aren't on the table.

•	masking/ demasking of DNA expression & "variable heredity" -  This line of thought involves a combination of iffy concepts:
1.	First assume that fixed [phase/function/object] networks exist (see "Multi-[phasefunctionobject] Neural Modules");  
2.	Further assume that our DNA codes for a "community of personalities", or "diversity of capabilities/ tunings" (with some random variability), only one "vector" one of which is expressed at a time because "the other personalities/ capabilities" are masked?  This would be a bit like "vestigial organs" (organs or structures left over from a species evolutionary history, but with no useful function); 
3.	Given conditions 1 & 2, then standard mechanisms for cell signaling (and expression of specific DNA code) could potentially be used for a "maternally-directed, post-natal transmission" of learned "tunings", IFF the cell signaling proteins could be transmitted to the gametes (mother and/or father) or the foetus (mother), possibly in a manner that isn't fully deterministic (some randomness).
Note that this description starts to resemble (remotely) some aspects of your computational neuro-genetic modelling.  But it is extremely iffy (unlikely) stuff.

So summarizing, there isn't a case for Lamarckian evolution in biology that I can see, but perhaps we should keep one eye open... realizing that this is a very low probability concept, but if it existed it would be an incredibly powerful capability and competitive advantage to the species possessing it.

	2.6.b Where might Lamarckian evolution best express itself?

Even though the sub-section above concludes that Lamarckian biological heredity is a far-fetched concept, it's fun to think of the relative roles for Lamarckian vs Mendelian heredity, if the former was possible. 

One might expect that brain function would benefit the most from Lamarckian evolutionary processes, as compared to an echelon of other functions as listed below in order of decreasing necessity to be stable/ unchanged.  This is because much thinking (often dysfunctional even in the best of times), is far less life-critical than, say, basic metabilic processes.  On the other hand, highly variable thinking may confer very strong benefits, as for example, in game theory where too high a predictability in thinking may make it too easy for predators/ competitors to anticipate thinking and behaviour.
•	separate mitochondrial DNA – perhaps there is so little tolerance for change that this DNA isn't even involved in sexual "reproduction"! Or perhaps it is important to isolate DNA with a "high-damage" susceptibility from the core or nuclear DNA. 
•	critical organs/ processes – death being the penalty for serious variations from the norm, there might be a lower tolerance for DNA variance.
•	muscles -  variability here would presumably be "moderately tolerable".
•	immune systems – high variability (differentiation) in the details is important, so that the whole population isn't vulnerable to the same disease/ parasite.  However, a strong stability (immune system's general capability) would be vital to retain.
•	cosmetic features – neither life threatening nor particularly important, a high variability could be tolerated.  However, perhaps strong cosmetic variations are taken as being indicative of underlying strong variations (and therefore big potential problems) so that when selecting a mate by "judging a book by its cover", this becomes important.
•	brain function – given the extreme power and robustness of brain systems, and the potentially huge benefits of special (not uniform) capabilities, one would suspect that a huge variability would be not only permissible but advantageous in non-life threatening brain systems/ processes.  As mentioned above, apart from learning itself, the brain would likely benefit the most from a Lamarckian-style genetic heredity, giving an extreme advantage to species if this was possible.  As a warning though, it would still be useful to retain long-established capabilities so as to not drift into an "evolutionary corner".

A recent Scientific American article describes DNA sites that have exceptionally high genetic variability, and speculates as to why that is the case.  It provides a very interesting context for this sub-section.

	2.7  Critical Role of Evolution in Learning (David Fogel)

Its funny that you quoted David Fogel's "Blondie24" book, which I found to be fascinating from a number of perspectives.  A major impression that it made on me was his statement or lesson or whatever that true learning MUST involve evolutionary processes.  My way of interpreting that is that creative processes must search for more effective structures and models to explain observations.  Moreover, as can be seen from the discussion in the next section, I feel that "abductive" (or "transductive" as used in your recent paper and NN literature – although the sense of transductive may not include metaphors/ similies – it is mostly model-based or Bayesian supervised learning?) logic must be a component of advanced systems, and there is a therefore a need to derive and recognise metaphors that aren't matches at the data (object) level, and which may only apply in some examples at very high levels of abstraction.

I won't belabour this point... but do find it to be important, and to correspond to messages in your book.

	2.8  Nature vs Nurture Debate (p211)

I feel that this "debate" is terribly misleading, something that I feel applies to most "dichotomies" in general (two sides to each story – see sub-section below).  One line of thinking is given below – perhaps you have reactions to that... if so, it would be interesting to hear.

Basically, from the time that Watson & Crick (and other teams on the same trail) specified the structure of DNA, it has been probably been clear that the codons on the entire DNA (3.2 giga base pairs (Gbps)) could NOT specify in detail all neurons (~10^12? I forget the approximate order-of-magnitude and am pulling this out of the air for now?), synapses (10^15?), or a host of other influences on brain operation (glial cells, etc).    At the very least, one HAS to consider the process of ontogeny (growth/ selection/ partial die-off of neurons) as a "third leg of the stool".   I maintain that ontogeny is NEITHER "nature" nor "nurture, although it is likely highly influenced by both.  But it is highly unlikely that these influences are anywhere close to complete, and they would not eliminate ontogeny's strong independent influence on the characteristics of individuals (at various levels of heredity).  Others don't agree with that opinion – and I can appreciate their concerns, but still maintain that a separation of the concepts uis useful and enlightening.  Nor is ontogeny likely to be the only "missing leg of the stool" – there may be many other quasi-independent processes of importance that will have to be considered.

We cannot yet measure ontogeny, as we cannot accurately map or measure the extraordinary number  of  connections in the brain.  What we can probably say, is that the current "nature versus nurture" debate is heavily dependent on identical twin studies, and the assumption that identical twin brains differ because of "nature" (DNA in current terms!) or "nurture" (environment - including learning, parenting, friends).  "What is not nature" is "nurture" is clearly wrong, perhaps dramatically so – probably leading to a vast over-estimation of the role of the environment for "abstract intellectual capabilities" that we often try to measure.  In other words, learning clearly influences what you know and how you apply it, but when trying to explain why learning seems to consistently vary between identical twins in the same environment, it is far too rash to ascribe this to "environment/ nurture". (It may also be too rash to excessively ascribe commonalities between identical twins to "nature").

???  simple "switches" to kick in very different behaviours.... genes are there, only some expressed, structure may be there as well, but either not expressed or down-regulated.

See also the section "Mindcode" below for related issues.

	2.8a   The Trouble with Dichotomies

Most complex, real challenges are not limited to "two sides to every story"- they are usually multi-dimensional, with many many options and a great deal of flexibility in approaches and interpretations.  However, it is difficult to communicate too many intracacies to those not investing a great deal of time into an issue or area, so they can help to illustrate points of contention, and to communicate to a broader audience.

It seems to me that there are two main reasons for using dichotomies (note my dichotomy!):
•	pedagogy – to stimulate thinking in a new or advanced area, dichotomies stimulate thinking – contrasts, relevant features and challenges, ...  But one must soon go beyond the dichotomy
•	marketing/ propaganda – beyond the learning stage, the main purpose of dichotomies seems to be to "sell" concepts, points of view to others.  They provide simple, clear, compact messages. Unfortunately, they are likely very misleading, and seem often to be used for deception.

Notice that these aspects apply to the dichotomies that I have employed in this letter as well! <grin>

[This subject leads into a much bigger, broader area that also intrigues me – how can one explain the genesis and propagation of dysfunctional believe systems, especially in the scientific community?  This arose mostly from watching environmental/ health issues over a long time, and has less to do with whether a school of thought is right or wrong, but how ideas are "clung to", defended acrimoniously, and having people "married to" their beliefs – especially influenced by conspiracy theory and jealosy, and how we "devilize" other people/ communities.  We all think via models/concepts that are flawed, but there is a difference in how strongly some (many, of all of us?) people cling to ideas and refuse to change or let go – but now I'll drop this subject, as I'm much too far off track, and it is not at all related to your book.]

	3.  Themes not directly related to the Book

Although the following themes are "off-topic", it seems to me that they may still be of some potential interest as the ECOS approach is quite general, and it is natural to seek complementary or contrasting approaches.  At any rate, they are some of the issues that have gnawed at me for some time...

	3.1  Decisions & Selections, Mini-Max Theory

Sometimes there is perhaps too much emphasis on making a decision/ selection when that may not be necessary, may be too early, or may be counterproductive.  I think that this has been discussed elsewhere as well – instead of using a vote by a community of experts, or "winner take all" strategy, retention of several "de-correlated" possible solutions may be useful, as information is updated and the environment changes.  I won't go into more detail here...

As a separate issue related to decision-making, the conspicuous absence of mini-max theory in most NN literature is very peculiar (particularly controls-related literature), it mini-max would seem to be a basic requirement for any system that deals with a competitive environment.  Perhaps the issue is that most controls papers deal with "physical" environments rather than competitive "agents".  But that shouldn't entirely exclude "mini-max" – albeit an inert environment is a very simple "opponent".  Still, a lack of strategy-building is strange.  David Fogel's book Blondie24 is very interesting, as that is one of the basic starting points for the design of Blondie.

	3.2  Mindcode – the project I never seem to get around to

"Given that computer code is used to program computers, then MindCode ..."

"...Assembly language code is the lowest form of computing language normally used by programmers.  Genetic code is likely the homologue for biology and may be nature's joke on us – using our own terminology – it is literally "assembly language programming" (for proteins), and likely is one of the lowest forms of programming on the DNA ..."

DNA has to specify [cell and brain systems/ structures, brain processes and organization/ communication, data, procedures, operators, operating systems].  If I remember correctly, you allude to non-genetic DNA somewhere in your book (or at least, others have, more and more recently).  (?and perhaps "inherited" epigenetics, and perhaps much of the brain coding could be on the junk (non-gene) DNA?)

Anyways, "Mindcode" assumes as an unproven starting point that brain structure, ontogeny, processes, data, procedures, operating systems, and other (some of which are much higher-level, unknown abstract concepts) are specified by DNA coding, or "Mindcode".  While I won't go into detail in this letter, many questions arise:
•	how is "Mindcode" expressed in the DNA?  Presumably this differs radically from the "assembly-language programming" of genetics (gene coding).  
•	are there any differences to the mechanisms and principles of reading Mindcode, compared to genetic code? How does this differ?
•	how does one "measure" Mindcode, and start collecting data about functionality, memory and processes that cannot always be seen in cells/ structures?
•	in contrast to the above question, to what extent will we eventually be able to "read" the functionality of neural structures, and relate this to the underlying data, procedures, processes etc?
•	how is Mindcode used – data/ procedure "extraction" etc (i.e. use like CD-Rom, in concert with learned information)?
•	is there some way to modify Mindcode – this is very much your computational neuro-genetic modelling!  
•	Is there some way to implement Lamarckian hereditary mechanisms? (see the section "Medellian vs Lamarckian Heredity")
•	presumably there are hugely diverse and powerful "connectionist programming principles" of which we have no idea (and no equivalent abstract concepts as a basis of comparison) in Mindcode, and which will be an extremely rich and fertile learning opportunity for us (just as NNs have always been and continue to be – a distant and challenging goalpost far in the distance!).
•	how can one "re-program" Mindcode: in gametes, in mature brain "modules", and perhaps 
•	how can one take advantage of Mindcode for hybrid neuron/silicon systems (brain implants?) as a basis of design?  

I feel that there must be many other computing concepts that DNA  specifies, many of which are at much higher levels of abstraction, and of which we are not even aware yet.  Language and ontogeny are examples of capabilities that we can only begin to characterize, but we have so much further to go – perhaps many factors or orders of magnitude more to go than the progress to date.  DNA cannot specify (should not specify!) everything, so there is likely a codification of the "computing concepts" at greatly varying levels of abstraction.  Very specific data is probably used in critical areas, whereas other areas (such as language) may encode much higher abstract levels.  After all, one of the most powerful gifts of language may be our ability to work with symbolism – dynamically binding words and semantics on the fly to test out new concepts etc.  Recognizing every word in a language at birth should be "child's play" for the brain, as even old computer systems and full-vocabulary voice dictation software could do that.  If we aren't born knowing every word, it may be because it couldn't be a language if its words/sounds are all static (especially semantically).  There should even be flexibility at the grammatical and ?? levels, but at the end of a day, as Chomsky apparently said (I think, because I haven't checked this!) we can learn languages amazingly well.

Clearly this will relate to your computational neuro-genetic modeling, even though your current emphasis is on gene-network modeling, and building NN architectures that benefit from "genetics" (especially with respect to their evolution – Lamarckian or Mendellian!).

	3.3  Communities of substantially pre-wired Functional NNs

It is my belief that NN design/ learning will migrate towards assembling/evolving (Lamarckian & Mendellian!) hierarchies/networks of substantially pre-wired NNs that are functionally very powerful and fast, and that by their "reusability and identity":
•	provide a level of explanation that logic can never attain
•	provide a maximum rate of learning (instantaeous in many cases – where only small amounts of data are necessary to "orient" the NN)
•	facilitate the evolution of component NNs – one cannot expect the best performance of a very complex system to be learned/ achieve without evolution across a spectrum of problem sets, with a vast number of permutations/ combinations of structure and functionality

Examples that come to mind
•	Fixed Weight Neural Networks analysis- including ?original work, Prokhorov, partially by Jochen Steil, Roberto Santiago
•	perhaps Echo State networks (there is a special session at IJCNN05 Montreal, but I haven't had a chance to look closely at this)
•	Guang-Bin Huang's Extreme Learning Machine

I can't help thinking of this in the ECOS context, either.  It's not immediately clear exactly how – but your "computational neuro-genetic modelling" seems very closely related.  Perhaps you have been working on re-usable modules (for example EFuNNs) with substantially fixed portions.

	3.3a   Multi-[phase/function/object] Neural Modules (here called M&Ms) 

Since the mid-90s, I've been expecting to see a big growth in specialized NN modules and their integration/ mixing and matching (meta-[learning/ adaptation/ evolution], into larger systems of NN ensembles.  This fits my own background and bias as an engineer, and I found early indications that this would be a trend in the work of Steven Grossberg (I mostly remember his models of groups of neurons in the visual cortex – which show the great detail extant in even simple neuron models), and ?author?  papers on neural ensembles and modules, where the emphasis was on problem decomposistion and modularisation.  While some of this type of work has continued, perhaps not in the ways or to the extremes that I was hoping.    
But do you feel that automatically you are drifting in this direction with ECOS systems?

Within the context of fixed (or at least partly fixed or constrained) NNs, at least four aspects related to the development/ characterisation of neural modules (M&Ms) are described below.

	3.3.a.i  Multi-phase structure

Here is a simplistic metaphor/ application of abductive (?transductive?) thinking – but it helps me to force thinking a bit.  But is it of any use?  If so, then ECOS models that you have developed would seem to fit in with this (a "head start" on training/ setup).

The thinking here is that different parts of an M&M could behave quite differently during learning – from not changing at all, to changing chaotically or in a discontinuous fashion (even disappearing!).  The analogy is to describe different parts of a module in terms of how the structure of those parts, or phases, change during learning/ adaptation. 
•	"crystalline" phase – fixed weights, static-structure
•	plastic/ amorphous phase – static structure on short term
•	liquid phase  - weights change on-line, evolution of structure 
•	gas phase – simultaneous changes (chaotic, discontinuous) in structure, weights
•	Information theoretics – as with thermodynamics, info-theoretics may help to decide which parts of M&M) should be in which phase – doesn't have to be a static decision, may change with problems and evolution of the NN (i.e. "temperature" rises, meta-learning processes) – reminiscent of Harold Szu's derivation of Hebbian learning.  In your book you also zero in on Information Theoretics (sections 7.4.4 and 7.4.5), so thins must be of interest and influencing your thinking as well.  You have also worked on chaotic systems.

This would include M&Ms for which portions only "activate" for specific functionalities/ objects.  An additional structure-related issue is that of ties-ins to other M&Ms (which will affect part iv below). 

	3.3.a.ii  Multi-function capability (functional overloading)

It seems to me that functional "near-optimality" of modules is essential to getting hyper-fast, super-accurate, and risk-optimised:
•	evolution of networks/ensembles/hierarchies of M&Ms
•	data filtering/ normalisation, and recognition of the characteristics of a problem domain or the environment, and switching "attentional focus" to the appropriate M&Ms
•	learning, including the evolution/ abstraction of concepts related to behaviour of the environment/ competitors
•	strategising – evolving strategies, action plans and visions 
 
However, systems HAVE TO be parsimonius (actually they don't, but it makes me feel better), and it only makes sense that where minor "twigging" of connections (phases) in an M&M can give rise to different near-optimal functionality, then this "functional overloading" will be taken advantage of, and the means of rapidly switching context will be required.  That doesn't necessarily mean that closely related functions share the same M&M, it could be that radically, completely different functionality 

cheat by intelligent design, hand twigging
simply pick out M&M based on literature results for specialized NNs (ready-evolved M&Ms!)
autonomous evolution of M&Ms for functionality (and structures, objects)

	3.3.a.iii  Multi-object capability

Modules should be able to recognise and self-configure to handle objects with different characteristics, while still making use of their functional specialties.  Perhaps this might apply to some aspects of sensory information in biology – for example switching from day (central) to night (peripheral) vision, involving some different capabilities while still making use of many common functionalities.  Paul Werbos spoke of his concept of Object Nets few years ago, and this is illustrated by a rencent paper by Kumar Venayagamoorthy.  So far, the emphasis seems to be on establishing the concept of object-specific neural models (in itself a great advance in its early stages)

Your ECOS models sometimes do this – using or not using data types according to availability, so that was very interesting to see.

	3.3.a.iv  Messaging between modules

Here's a challenging and very interesting problem – how would an ensemble of M&Ms effeciently set up and optimize "messaging" between M&M modules?  This really is a challenge in "meta-learning"!! (learning how to learn to message between learning M&Ms).  Hebbian learning, evolutionary processes (Lamarckian or Mendelian), and information theoretics come to mind, but... that's a pure guess.  I suspect that some sort of function-interfacing analysis/ approach would be useful – but I don't have any analogies for that!

	3.3.a.v  Training of M&Ms

One question is whether fundamentally different types of learning for M&Ms may be more effective than conventional NN learning approaches.  Guang-Bin Huang's application of linear matrix operations for learning in his "Extreme Learning Machines" (ELM) concept shows how simpler learning approaches (than backpropagation, SOMs, ART, ECOS) may be applicable, and earlier I referred to information theoretics (but how might it be different/specialised for M&Ms?).  On the other hand, what unknown challenges/ opportunities are there?

I don't have any visions for this yet, but keep thinking of your ECOS work, Huangs ELM, and Julio Valdes of Canada's NRC "neuro-fuzzy snap", and Walter Freeman's "stochastic chaos" as a means of efficiently searching through local state-space, perhaps in a manner that provides in-built learning advantages for the most common problem sets that and M&M is exposed to.  See also the section below on "Design Patterns", which seems to have even deeper implications.

As a trivial, unsupported comment, this evokes images of chaotic attractors and particle swarms wandering through statespace – sensitive to "resonance" with specialized NNs that "migrate" to fit a  problem, snapping into place & switches inputs to appropriate "feed" to NNs.  

	3.3.a.vi  Stability of M&Ms, and communities of M&Ms

I haven't really looked closely at this (see "Stability Analysis" section below).  But what are the peculiarities of M&Ms?  In particular, can the "hand-twigging" or evolutionary process be constrained to produce "stable modules and/or communities" in one form or another (such as "contracting systems" referred to below)?  Is there a means of easily "patching on" M&Ms onto "non-contracting" M&Ms without affecting functionality, but in a way that makes the community a contracting system? (from a first glance – no, as it seems that a guarantee of stability requires the component parts to be stable – but perhaps that is a sufficient criteria, and not a necessary one.

	3.3b   Logic vs Understanding through Knowledge of Functional Behaviour

A common complaint about neural networks, and one addressed to some extent by your use of hybrid fuzzy-neural systems, is that NNs tend to be "black boxes".  

Potential advantages of M&Ms:
•	problem compression, dimensionality reduction
•	abstraction, in a coherent way that preserves functionality
•	extraction "true" meaning, rather than superficial, lossy and incomplete logical explanations

I would suggest that "throwing out the black box" may be wrong.  If communities of M&Ms have well-defined if abstract functionalities, then they may do a far superior job than logic at describing problems and solutions, as long as we can recognize and describe the component M&Ms and their hierarchies/ combinations, and recognize and describe the communications between them.  This should give rise to a much more "profound and describable" understanding than simple logic.  In other words, by labelling a set of well-known, key "basic M&Ms" and learning about their functionalities, limitations and the way that they can be combined with other "basic M&Ms", their name would evoke an understanding that goes deeper and beyond simple logic, and that would inherently be more powerful.

That would seem to apply to your ECOS systems, if you can show that one may modularise and "re-use" them effectively and predictably.

To finish with a very aggressive and unsupported statement:
"...Logic is one of the tools in your mental toolbox, but it is only one of the tools.  Quite possibly, it may be one of the weaker tools that you possess..."

I don't believe or disbelieve the above – it just helps to keep the thinking open...  At the very least, the easy success of early computing with logic shows how inherently very simple logic is (sometimes simplicity and predictability of components allows one to build very complex and unfathomable systems).  Problems for which we use NNs often don't have adequate descriptions to apply logic in a profound and meaningful way (the failure of early symbolic logic for some problems that now employ NNs).

	3.4  Design Patterns (Gang of Four), Problem Decomposition/Modularisation, Re-usability of NNs/ECOS, 
One haunting theme that always seems to recur is that of "Design Patterns" and related patterns, first developed for architecture, but strong in computer programming now.  The idea is that the overall end-objective and style of use of a program/sub-program/module can immediately be used to help narrow down a reduced set of design options, each of which have consequences (flexibility, power, etc) for subsequent program modules or for the integration of program modules.  

With respect to M&Ms (as described above), "Design Patterns" become particularly attractive, or perhaps could become particularly attractive if and when:
a)	a solid base of different M&Ms become available;
b)	a variety of messaging techniques also are available;
c)	experience in building a wide variety of ensembles of M&Ms provides a starting point for "Design Patterns"

But could at least some of the "Design Patterns" literally come first?  In other words, many of the patterns could perhaps be borrowed straight from other types of computer programming, architecture etc, and ti may be necessary to develop only some that are specific to M&M ensembles? Intuitively my feeling is that there will be vastly more patterns for neural net architectures (such as M&Ms, hybridized with conventional programming) than for all of the conventional programming, given the extreme diversity and power, and the very much higher levels of abstraction that will likely develop (for which there is no comparable analogy in conventional systems).

Some of your comments, and descriptions (pseudo-code), seem to lean in this direction, but maybe I'm seeing things.

	3.5  Post-calculus mathematics & science
Here is a very immature train of thought, but one that intrigues me.  It seems that many new tools in NNs and other fields (particle swarms, some forms of Kalman filter training of NNs, some training of fixed weight NNs using classical matrix math) are not dependent on gradients for locating solutions to fairly complex non-linear problems.  There has always been mathematics to describe discrete/ discontinuous systems, but are we seeing a trend to less dependence on calculus in some areas, albeit still using it in hybrid systems of calculus/ discontinuities/ chaos/ non-linearities for non-gradient-based learning?  Are we going to be able to have much better representations, models and solutions for real-world problems which "usually" have non-linearities and discontinuities, and which often have chaotic regimes?  In the same way that Liebnitz and Newton's calculus revolutionized science and engineering (maybe less so math, which is so broad anyways), will we see an explosion in "new science & engineering"?  Note that the issue isn't to abandon calculus – far from that!  It is to retain the tools we have, and build on new capabilities, which to some extent rely on modern computing (like NNs).

Also, when there are major breakthroughs in science or other areas, this often translates into interpretations that are applied to the near-impossible-to-measure-and-model "soft sciences" such as philosophy, psychology/ sociology – which are not my comfort area.  Unfortunately, this also leads to the wackier side of how concepts get applied.  For example, I fully expect to see a tidal wave of junk science sprouting from fMRI (in addition to the fantastic real science), because it seems an ideal platform for the successors to astrology, palm reading, and biorythms.  It will provide an ideal, flexible platform to abuse for politically correct thinking.

	3.6  Constraint-Seeking ?modular NNs?

This theme arises from your concept of "knowledge-seeking" systems, and the earlier theme of "Centroid versus Boundary Approaches" and fits well with your "localized learning ECOS systems – real-time, etc.  The idea is that as more abstract levels of understanding are built for state-space, it may become more and more important to seek more data closer to boundaries and "anomalous regions", which are rich for learning new concepts & abstractions. 

This section again evokes the theme of multi-functionality NNs – in that functionality differs according to inputs over range of "functional competencies" of the specialized NN  (functional overloading).  That certainly doesn't negate the need for unrelated NN modules, but to the extent possible understanding and parsimonious construction of effective ensembles would seem to call for multi-[phase/ function/ object] NN modules assembled into more complex systems.

	3.7  Stability Analysis
Slotine & Lohmiller - contracting systems & stability 
Lyapunov stability analysis

This is related to dynamical systems [...I've run out of time to elaborate, so I'll just give a simple introductory paragraph...], and the author's theme that stable, complex systems of many modules can be built by carefully constraining the stability characteristics of each component according to their "contracting systems" analysis *differential basis to theory), which they claim is more general and powerful than traditional Lyapunov (integral based) analysis.

Whether this is practical or not, I don't know.  Apparently, if I understand it properly, dynamically stable systems would have only one equilibrium point in their (circular) region of stability.  Does this exclude too many interesting/ critical systems?  Can one "stabilise" combinations of inherently unstable system that possess vitally important capabilities?

It seems that the ECOS approach could"constrain" the localized NNs/FNs to be stable throughout known state space? Perhaps that might not work at the boundaries of the NNs/FNs.

	3.8 Other Themes Not Discussed

Time limits my coverage of other areas of interest, in particular:
•	language
•	recurrent NNs
•	ICA and wavelet transforms etc

Of course, there are no end of interesting subjects in the NN field, especially on the biology/brain side!

	4.0  References (some, anyways)

Note the references to IJCNN04 Budapest are abbreviated, and one should refer to:
Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25-29 July 2004, CD-Rom: IJCNN2004 CD-ROM Conference Proceedings, IEEE Catalog Number: 04CH37541C, ISBN: 0-7803-8360-5

Marin Fowler  "Analysis Patterns: reusable object modules"  1997 Addison Wesley Longman Inc.  Massachussettes USA  ISBN 0-201-89542-0

Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides "Design Patterns: Elements of Reusable Object-Oriented Software" 1995  Addison Wesley Longman Inc.,  Massachussetts USA ISBN 0-201-63361-2 

S. Grossberg's Visual Cortex work – many references (I haven't listed)

Guang-Bin Huang, Qin-Yu Zhiu, Chee-Kheong Siew, Nanyang TechU, Singapore "Extreme learning machine: A new learning scheme of feedforward neural networks" IJCNN04 Budapest

D. Wang, La TrobeU, Melbourne Australia; G.B. Huang, Nanyang TechU, Singapore "Protein sequence classification using Extreme Learning Machine"  ?in submission for IJCNN05 Montreal, I think?

N. Kasabov, L. Benuskova, S.G. Wysoski, Knowledge Engng & Discovery Research Institute (Kedri), Aukland UofTech, New Zealand "Computational neurogenetic modelling: Gene networks within neural networks" IJCNN04 Budapest

David B. Fogel, Natural Selection Inc. "Blondie24: Playing at the edge of AI" 2002 Academic Press, San Diego CA  ISBN 1-55860-783-8

Shaoning Pang, Nikola Kasabov Knowledge Engineering & Discover Research Institute (Kedri)
AucklandU Tech, New Zealand  "Inductive vs Transductive Inference, Global vs Local Models: SVM, TSVM, and SVMT for Gene Expression Classification Problems" IJCNN04 Budapest

J.-J.E. Slotine, W. Lohmiller, Nonlinear Systems Laboratory, MIT Massachussettes "Modularity, evolution, and the binding problem: a view from stability theory" Neural Networks 14 (2001) 137-145 

Thomas H. Leahey "A History of Psychology: Main currents in psychological thought"  sixth edition 2004, Pearson Education, New Jersey, (Prentice Hall)  ISBN 0-13-111447-6
•	Jean-Baptiste Lamarck pp199-200, romantic view of self-directed evolution, passed on to offspring
•	Herbert Spencer & evolution: coined the phrase "law of survival of the fittest" in 1852

SciAm article – sites for high genetic variability (I've lost the author/titles!!)
Scientific American, April 2004, pp?~85?-91 (I have pages 87-91, but am missing the beginning)
see website of Stephen J. Freeland at www.evolvingcode.net

Roberto.A. Santiago, NW Computational Intelligence Lab, Portland StateU, Oregon "Context discerning multifunction networks: reformulating fixed weight neural networks"  IJCNN04 Budapest

Jochen J. Steil, Neuroinformatics group, UBielefeld, Germany "Backpropagation-decorrelation: on-line recurrent learning with O(N) complexity" IJCNN04 Budapest

Ganesh Kumar Venayagamoorthy, UMissouri-Rolla, Missouri USA "Dynamic optimization of a multimachine power system with a FACTS device using identification and control ObjectNets" (Yikes – I just have the paper and forget which conference!)  0-7803-8487-3/04/$20.00 © 2004 IEEE

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