Tuesday, 28 May 2013

Application of Casual Mathematic Logic (CML) to brain simulation

WHATS THIS ALL ABOUT ? :  This website summarizes the progress of controversial "Dipole Neurology" theory (summarized here) which has exceeded my original expectations by developing from a spare time academic project (in neuropsychology) and has now moved into the search for a common physical pattern/topology for intelligence/consciousness (starting from the mammalian brain).   The starting presumption (to be falsified) is that intelligence/consciousness has to possess a specific type of dual process physical structure I propose the brain has. More importantly that this structure might be roughly applicable to the successful operation of most general learning systems. If the project continues as it has, it is moving to being able to make future testable predictions for the viability of Artificial General Intelligence (AGi) systems. That is whether AGi will have to possess something like a brainlike physical  form as their complexity increases. Many science futurists predict that from 30 years from now, most primary technologies and later down the line, the core of most life itself will be reliant on complete AGi and mind understanding. So its pretty exciting to be able to explore this pioneering field at such an early stage.

For this project I usually work alone as a new insight (or self criticism) comes to mind. For objectivity I co-author peer reviewed publications with academics senior to myself and expose myself to group discussions and when affordable will present at a major neuroscience conferences with the aim of soliciting high quality criticism.  To explain the concepts I often generate simplified physical models for the cortico-limbic system as this appears to be the brains “information engine”, but do not take this to mean that other complex facts are ignored. Look at the cortico-limbic models here as the final overall "computational superstructures" in the same way we look at cortical columns as mini-computers.  My most recent co-authored published application of physics to this simplified approach (supplied below) tries to test whether fundamental thermodynamics is consistent with computational principles from entire structural morphologies. The theories history summarized on this website will show a sequence of specific predictions has progressively lead from development of hypothesis to actual current theoretical framework. My proposal is that to develop and innovate a well worked physical topology of intelligence will be pivotal.  Complex self developing AGi will eventually have to self organize into an optimal physical topology and I propose it will not be too different from the simplified form presented here.

See this blog post for a brief introduction - my 2013 paper for the first draft formalization of computational principles and my 2009 paper for the background neuro model. The next work planned will derive specific key facets, such as re-enforcement learning, spatiotemporal perception and self-awareness from the current simplified thermodynamic basis.

PLEASE NOTE: Although the brain structures are shown to be EM in nature, and the proposal here is the intelligence has a definite physical form this is not a quantum mind theory, quite the reverse. The formalization of this approach leads us to the conclusion we can have QM "style" processes in classical scales and define this via regular logical approaches (based on manifold topology) and thermodynamics.

Two of our papers were accepted in the same issue of the Journal of Artificial General Intelligence special edition on WBE and Connectomics. These are available as open access. There are about nine papers in this edition on Whole Brain emulation all tackling different aspects of the mind upload problem. Two been written and co-authored between myself and Prof Sergio Pissanetzky (who came up with CML).  Each paper is focused for different aspects of the problem, The first with primary author Prof Pissanetzky is the introduction for CML. The second where I am primary author represents the full realization of the approach described on this site, and is the progress from previous papers regarding how to approach deriving general mammalian computation from the biophysics of brain structure.

But this had halted with a major problem. How consistent was this approach with both fundamental physics and information theory. i.e. If principles of intelligence are intrinsic to the evolution of brain structures that fuse complex amalgamations of proteins and biophysics, then these structural principles should be scaling in some kind of regular manner from fundamental principles of information. Regular neuroscience had no bridging theory for this problem but Causal Mathematical Logic has. CML describes how information algorithms self organize from the most fundamental physical principles (such as least action and entropy).  Images below are from the primary paper "Causal Mathematical Logic as a guiding framework for the prediction of "Intelligence Signals" in brain simulations. Authors: Lanzalaco, Felix; Pissanetzky, Sergio"

Here we show how CML theory orders information in a manner analogous to cortical columns, simply by coding the principle of least action.

The application of CML to the "go for everything" approach in the cortico-limbic system summarized on this site, now produces the first step towards a  complete "information engine" model that unifies computational neuroscience and Artificial General Intelligence.  Its also the first well defined approach to dual process theories in physical/neuro and AGi terms. Some of the justification from the paper follows.

We summarize the brains oscillations, where they are located in the brains topology and whether these phase lock. The finding is most phase locking is limbic system and the clock sources are coming from the brains central structures. Much like an oscillation tends to settle into the equilibrium at the center of a thermodynamic system.

Using CML theory we find the relationship between action (high to low) and entropy. This should predict the primary contents for brain signals, that we had previously formalized action as ERD and entropy ERS. You will need to check the paper for an explanation.

There were some problems (check the paper).  But the relationship is verified as similar. Bear in mind this is still a correlation that rests on the papers more refined presumptions about physics of brain structure. Read the paper to find how the physics basis is already existent in many other works.

An attempt at a formalization for this approach in thermodynamics the recruits some definitions from integrated information theory (IIT). This formalization is veerinng away from the direction proposed by Prof Pissanetzky and is influenced by some aspects of Alex Wissner Gross Freer. The idea is that we make action and entropy equivalent.  Something which is strongly resisted in the computational sciences right now, primarily because describing and formalizing the processes of order in information is so important. But I dont buy it. Coming from a neuro view I think Alex Gross description of entropy hits on the physics formalization for one part of common dual process formalization for intelligence. For a video of Alex see here, and for a wiki summary of dual process theory see here.

And where next ?  This approach has to explain all these brain features in terms of physics and information theory. Many of the above are being considered essential for Artificial General Intelligence.  I have been collaborating with other colleagues in regard to this. Again watch this space.

The point regarding brain simulation, is the basic idea is that a brain simulation (or emulation) has to run on a thermodynamically responsive system. Especially as brain simulators become more generic in form. All the process of transcribing the details of the neural parts can be done by current methods, but if its going to be conscious (a word i don't like using).. or better term to "process integrate/ dynamically" and reflect as we do.. it will have to produce EEG to generate Event Related Potentials.. and these are best described or understood by CML (or something similar) in terms of thermodynamics. The good news is this predicts no esoteric quantum physics will be required.. and a thermodynamic brain simulator (or emulator) will hopefully work fine without such extreme details.

The Quantum mind objectors will say that Quantum mechanics is still thermodynamic. My reply is we only need to go for the physical theory which describes the thermodynamic resolution the information operates on.  Almost all of the neural mutational complexity produces mechanisms at the synapse to glial level.  If the thermodynamic description is enough for the entire physical description at this scale, then that means this proposal is the start of a new general physical model for simulators/emulators based on mammalian structures.

The other paper where Prof Pissanetkzy is the lead other here  describes more of the mathematical and philosophical foundations. There is also a supplementary section for the above paper  linked to here called "Can CML predict solutions for outstanding questions in Whole Brain Emulation (WBE) ?"   This will be updated also with the accepted peer review points.


I was not successful in procuring the labtime from Edinburgh University, to falsify which mechanism produces the dipole flow in development. We need this mechanisms falsified. If is verified we then have a strong justification to propose that the FET flagship Human Brain Project would need to model the cortical column model in the context of the dipole model. This work is competing with many traditional applied projects that are directed towards curing medical disorders. Good news is it appears other researchers in Scotland have been discovering dipole flow in neurodevelopment. i.e. This study  from Prof. Timothy Newman at Dundee, College of Life sciences.

So it appears the dipole-multipole concept lab results are appearing as I predicted way back which is vindicating.  But as with the work from Vincent Fleury's Lab in CNRS Paris. I am still not in agreement with these labs on the mechanism. That was the point for my lab request.  I predict that Ca2+ flow through the Connexin, Pannexin network in and out of the Radial Glia provides Magnetohydrodynamic flow giving rise to generalized cortex wide guidance pulses for electrotonic components. i.e. Guidance molecules, Intracellular ion gradients and astrotactin adhesion for neurons moving along the Glial fibers. There is more on that on this site here.  For those new to this concept, I justify the application for this dipole force in development applying to mammals based on the indirect data meta-analysed from my 2009 publication, and the evolutionary roots of the cortex, which pre-date Clade Avialae (birds !) back to the roots in phylum chordata (sea creatures). A post on that issue here, based on the bio-informatic regression of synapse carried out by the genes to cognition project of Seth Grant. There have been other studies summarized by a scientific american article which reached a similar conclusion.

Sunday, 27 January 2013

Critique of Tony Wrights Left in the Dark theory

Not put anything up for a while.  Open University is keeping me occupied with math and programming, which will hopefully develop the computational models for my brain structure theories.    In meanwhile I found this lost critique I had made of Tony Wrights theory on the development of human hemispheres (my specialty !). Tony has a new book which again does not address my objections to the previous one, so here they are again.

For an overview of Tony Wrights projects.

I specialize in theory about how brain structure both develops biohysically and the resulting computational principles from this structure. My critique of his work has been re-covered from the following news article citing his video where I made these points to him in a discussion we had but these were deleted later, not by Tony it looks like, but the magazine reformatted all its articles and discussions were lost.

How the Left Hemisphere Colonized Reality Douglas Rushkoff at 7:10 am Sat, Oct 9

Word version of critique here


Wednesday, 26 September 2012

The "percepto-bit", can we run our experience in silicon at mesoscopic scales ?

The 2 posts on brain simulation are now rolled into one. Entire Blog on one page at www.Lanzalaco.org.

My whacky idea of the week is a concept called the "percepto-bit" for brain simulation / emulation.  That we can base our box models for consciousness at the mesocopic scale where there is the emergent level of the entire variety of brain functions. Neuromdulation nuclei which can alter emotions to a noticable degree start at mesoscale. Recently I was given a brain tissue lesion counting study for a university project. It turns out it is also mesoscale which is the point we actually start to perceive any problem due to the disappearance of brain areas i.e. lesions, plaques, tangles or vascular blocks when they interrupt memory, emotion and phenomenal consciousness are tiny holes in tissue at mesoscale, but we are not impaired (as far as i know) by these problems when they are microscale level tears in brain tissue. What is modeled below "perceptobit" scale will have to contain the full richness of data flow in a real mind, but since this is below perceptual access substrate, the processing and algorithms can be whatever internal approximations we choose. Only above percepto-bit scale has to be brain accurate. As long as the percepto-bits communicate the known internal processes within these bits to each other, why should we perceive any difference to our own substrate ?

I am not currently sure how original this idea is but it would seem controversial to the low micron level proposed as necessary for simulations/emulation. Last month I decided to just rattle of  a document on what i thought were the main grounds to cover in brain simulation/emulation. Dr Randal Koene an expert in brain emulation then uploaded a video of private discussion to experts in this field, and all the same points had been covered.   

Is mesoscale sufficient to build the substrate of the mind from ? If anybody is familiar with my interest in astroglia field models for columns and Ca2+ waves in neurogenesis, they may be aware, that their properties come together at the mesoscale. Some biophysicist argue that this existance of quantum fields means simulations should be more extreme. i.e. model the brain at nanoscale. If the functional properties are coherent at mesoscale, thats not the case. They actually help bolster the concept to model at larger than micron scale. Perceptobit level is the mesoscale for which any modelling above that is not brain accurate interferes with perception. This is the ground resolution from which information is passed and has to accurately represent brain like signals and physics between each perceptobit. Simulation wise its still pretty intensive to be so physically accurate even at mesoscale, but far easier than microscale.

This had me consider the application of an area I like to specialize in. That the top down biophysics of neural structure might add another simplifying perspective.  I don't propose here not to model low micron scale, just that if we cannot perceive a knockout to anything at that level, then whats in that scale only has to input and output the correct kind of perceptual information to other "perceptobits". So we can just use traditional or new sustrate independent modelling methods within that bit. It is a very strange idea that we could exist and enjoy experience unscathed running in silicon modeled to brain accuracy at sub millimeter fine tip of a pinhead scale. A Very strange idea, even to me, but then so were the previous concepts i came up with and look what happened there ! This reasoning is further explained here at Randal Koenes carboncopies group.  

Can mesoscale simplify proposed nano-scale field effects for information processing ?

Common objections raised to brain sims regard ephatic EM and magnetic fields holding nano-scale information.  This is something I focus on pretty heavily if anybody is familiar with my biophysical proposals for brains structure. If we look at the papers for magnetic mechanisms (folder here), the proposed fields produced by Ca2+ in astrocytes emerge at the mesoscale and are proposed to structure the column itself, while also acting as switching capacitors to sustain sensory signals without them having to constantly re-spike. If we look at some more recent papers for ephatic fields in hippocampus (folder here), and try to build a scenario they appear to be converging towards organizing at theta oscillation in stem cells which eavesdrop for non synaptic go signals.  i.e. Again, these do not appear to be fine grained information fields. Their function like the Ca2+ in the columns may just be to provide a generic mesoscopic coherence. What would be its function ? A linear sort across the entire septal temporal hippocampus axis in sleep is proposed by neuro-computationalists. So the idea is a field activation of Ca2+ flow can restart all the stem cells on that axis at the same time.  Such coherence has a macroscopic function. To re-enstate a line of fresh neurons, facilitates a linear all at once network re-sort along this entire association zone of the brain. If coherence dropped there would be fragmentation of the autobiographical sequences (composed of episodic codings) which forms our sense of self.

The idea is in sub perceptobit level we cannot perceive if the processing is brain like or not. We can implement each bit distributed on hardware in a grid. Even at Mesoscale each bit will require a lot of processing, perhaps an entire cluster by today's standards. There are so many signal types to be accounted for, Glia, modulators, Neurons, enzyme effects, field effects etc.  However one sub perceptual architecture means the requirement of each perceptobit is to present brain like information to its facing neighbor. i.e. Achieve perception by mapping the brain like signals dynamically to each other perceptobit. It can be achived by non brain like computation that fits with the current evolution of computer architecture.

This is speculation of course. I did derive these concepts for field function however by tracking (hypothetically) what I propose are the role of Ca2+ waves in the developmental origins from the radial glia forward in time to when they fade to  become the cortical astroctyes and adult ventricular stem cells. Both my proposals for development and those of the independent researchers adult models citied above are "fairly" consistent with each other.   There is however a recent sub micron electrostatic field model for CaMKII coding the neurons internal cytoskeleton structure by craddock 2012.  If its correct, and the above fields are integrating with this, that maybe problematic. However the end result of that is just the macroscopic neurons structure, so that should be dealt with by sub "pecepto-bit" substrate independent modeling.  A more pressing mesoscopic issue to be considered is that of Glia holding the key to large scale structural plasticity. 

More reason to up the sim resolution. Glia reframe the computational structure of the neuron model.

What does this concept offer if the idea has some truth in it ? What it cannot do is solve the hardest mind upload problem of data acquisition. A top down view, may help in the second major problem of mind upload/simulation. That is the problem of hardware requirements for real time computation in brain emulation/sim can be brought forward by about ten years if mesoscale is enough to run consciousness. Freeing up computational resources can then deal with many simulation /emulation problems like glia and all the unknowns in neuroscience that are vastly underestimated in this field. i.e. Modeling neurons as the calculation factor for a roadmap is not realistic. The current computational evidence shows us neuron graph models structurally are flatpack without glia.

See the neuron graph models with no structure guidance (A) then with structure guidance (B,C,D).   Glia are responsible for self organizing a guidance for the weird morphological structure in the brain, not neurons.  You can get basic signals out spiking neurons networks, but no actual computational brain structures.  

With glia, neurons are constrained by overarching top down principles imposed on them to develop interesting structure. i.e. They can start to build the dynamic, macroscopic complex computational structures we are familiar with. The latest big research from brain transcriptome centers are unable to determine known cortex structures from the genetic mapping of neurons. What does this mean ? The neuron as the basic component for simulation/emulation is to a degree a faulty concept, but the degree to which it is faulty has amazingly not been realized and our largest simulation/emulation projects are "trying" to run on it ! We do get oscillations, and crude spike co-coalitions, but anything else remotely brain like has to be arranged for the network by programming structure. It does not derive a large brain like network structure from internal basic principles. Why ? A great problem is the various glia have their own unrecognized structure building principles (the focus of my PhD research) which is forcing the greedy growth principles of neurons into large complex structures that are macroscopic scale. So there is then no single entry point or morphology to pick for brain simulation. Ideally then model all the morphology at all levels but being practical thats too power hungry. So a proposal is to realize the problems with the neuron substrate model and compromise to a box substrate model comprised of the baseline size for perceptual bits which communicate the known internal processes (including glia, Ca2+ waves ,+ room for unknowns etc) within these bits to each other. 

This mesoscopic scale may not be sufficient, or there may have to be variable scale focus for different brain regions, but if the top down concepts can simplify this problem and bring simulation/emulation timelines forward, why not put the idea out there for consideration.  I don't even know if this is an original concept. The idea does seem pretty obvious.
Some introductory  articles on glia. For more on ramifications of glia see this site. Much of the theory on this site is also about the structure that glia builds.

Without Glial Cells, Animals Lose Their Senses

This article shows how neurons collapse without glia, pretty consistent with the computational graph models

Glia Guide Brain Development In Worms

Even in worms the Glia are guiding structure. It is the radial and various glia which gives larger brains their structure.

Brain's Connective Cells Are Much More Than Glue: Glia Cells Also Regulate Learning and Memory

No surprise there. The Ca2+ astrocyte wave models are proposed as a key to short term memory, as well as hippocampal neurogenesis.

Thursday, 16 August 2012

New paper Modality Independent Neuro Development Substrate for Artificial General Intelligence “MINDS for AGi”

Here is my application of the dipole / multipole framework to AGi for AGI-12, 5th Conference on AGI @ Oxford, Dec 8-11 2012  -- POSTER LINK

Its called The Integration of a Deep Structure Neuromorphic Framework for AGi: Modality Independent Neuro Developmental Substrate for Artificial General Intelligence “MINDS for AGi” - LINK

2b edited 30 pages! 

Whats it about  ? If you have the entire structure principles, you have generalized map for the entire computational principles. When filled out with low level detail its perfect for the highest level modality free neuro models for AGi. Needs refinement to specific in depth computation, but a start towards high level principles. 

Also brief 2 page outline for application of Dipole expansion framework to brain emulation / copies - LINK

Felix Lanzalaco (in picture) presents his long awaited abstract to a paid audience outside in the cold scotland climate.  Some youtube levity was needed after writing such a heavy going paper, but is she on to something ? Even more to the point, am I on to something, OR did I slip into groupthink and/or pseudoscience (yes the video was improvised).


UPDATE 16/09/2012 : As predicted by all the paper was way too long for AGI-12 as its a Springer proceedings Journal with fixed length.. The content itself passed review on all 4 measures used (That surprised me !), but both reviewers rejected on the editorial issue of length. It has not been possible for me to reduce it to 10 pages right now for these reasons (aside from my final neuroscience exams for this year also).

1. Since producing it I have realized the concept still needs fleshed out further, i.e. Its still an over-simplification too far. 

2. To shorten to a core would need reference to its original larger work, and this has not been submitted to a repository.

To my surprise one of the reviewers proposes high level architecture is harder to get right than sub system components. Thats what I advocate also, but other AGi programmers think differently on their quest for simple principles. They haven't experienced enough neuroscience in my view. This review point is the primary reason I could not compress the conceptual framework right now. The unique insight I propose to posses into the highest level structure is not enough. There has to be more work to produce consistency with both known principles and known unknowns in neuroscience at all key levels of scale before a compression to high level principles. I already had the plan, and carried out groundwork in how to go about this, thats what the PhD is about. Its no easy task and requires a thesis just to cover the projects scope. 

Sorry to say but my current opinion is if the brain is anything to go by even with a full understanding at all levels the final principles for general intelligence are going to have to include the integration of a lot of sub principles, and that would be after such principles have been well untangled from the biological substrate. Maybe there is an E=mc2 for the brain, but I have doubts it can capture the true systems functionality. My hope is the neuro-computation aspect of the biophysical understanding I bring will be similar to the resultant success I experienced with the developmental predictions made almost 10 years ago (from the proposed top down models here).  That is that having the final high level insight integrating with the lower-level work of todays neuroscience can make predictions which can inform us in our AGi (brain derived) system research regarding both known/unknowns and the hardest of all, unknown/unknowns.

Thursday, 19 July 2012

Brain simulations should start with top down models

NOTE: I have yet to make an easy read summary and / current status of the cortex dipole part of my complex suite of top down neuro models.  This post (--Here--) is an essential quick review which deals with common criticisms by summary of some of the primary evidence for a cortex dipole premise. I have to state this dipole neurology concept was conceived and researched for many years and sponsored academically before the cortex morphology images were found. This is NOT an entire theoretical framework based on looking at a couple of pictures. 

The images below are a fast intro for the cortex Dipole, both human developed and track marking in chick cortex neurodevelopment (from an independent lab).  Another lab in Scotland has also duplicated these results.  These labs are not in agreement with me over my magnetohydrodynamic (MHD) mechanism. i.e. The model i propose for Ca2+ waves through radial glia Connexin/Pannexin yet, but it will be interesting to see what happens over the long term.  HERE I point out why its unlikely to be electro-osmosis dipole/multipole (unless they adapt it be MHD).

Far left and Right, From V. Fleury, 2011. A change in boundary conditions induces a discontinuity of tissue flow in chicken embryos and the formation of the cephalic fold Eur. Phys. J. E (2011) 34: 73 Top middle, Cortical Dissection (Mirrored) Williams and Glunbegovic.,1992. Bottom Middle, Dipole simulation (Belcher., J., 2005 MIT education). Note that the MIT magnetic simulation is relatively simple maxwell plot. The cortex is more complex dipole (poster here), developing in the closed space of the skull by radial glia, so feeds back on itself, giving rise to broad domain wall, magnetic domains (which are surface folds and cortex columns), as well as clear asymmetrical  (Yakovlevian) torque Asymmetry was taken out cortex image above by sagital mirroring to make more clear the toroidal lines in cortex. All the above is consistent with  majority of neurodevelopmental mechanisms and models known (to me) so far.

More to the point, for anybody involved in neuroscience and computational neuroscience structures are a big issue, which I will try to address in this large post. What you are seeing, is no arbitrary morphology as we thought.  It is proposed as a major top down neuroscience issue for framing the entire system. To start with we need to face this question.

Is this or is it not a cortex dipole structure ?

Simple and reasonable enough question if we can even proceed to conceive of it. If it is (and the evidence is piling up over time to back this up), this has ramifications for understanding human neural processing at its highest most integrated level. I propose the brain cannot be fully or even well understood without this approach. Why ? Because these structures are not arbitrary bottom up developments with meaningless weird shapes that reflect principles of neuron growth. They operate from the top down via the coherence of radial glia biophysics. My predictions are they have a resultant over-arching computational function  which governs the brains internal architecture to the resolution of high level function (i.e. functional asymmetries is one) which recruit neural sub structures and their components.  That concept requires a fair rethink of traditional neuroscience. If you are ready then please read on. However bear in mind this is still in rough stage. 

This page is for intro to limbic system harmonic models. That is although you see the cortex dipole structure above, there is also a boundary inversion model for the limbic system structures and this is equally important as the dipole for this whole framwork, because the approach has to be one consistent complex multipole for all large brain structures or it fails. The Cerbellum model is not online, but is under progress and uses similar mechanics, but i am fully occupied with cortico-limbic models.



If you read the above link on evidence for Cortex Dipole premise ( highlighted in yellow) ?  Ten years on and now I receive far less resistance now to my original claim the cortex has case for a dipole structure.  Its kind of a quiet period, to see what happens next which often happens after there is an increase in evidence.  So after submitting to the possibility I am on to something the question often becomes, "Well so what if it does, what does it mean or apply to?". The answer is from a pure bio-science view it promises to give top down leverage on function by providing a single framework with  computational rationale derived from biophysics to make sense of general neuromorphology and computational function (in terms of generalized organizing principles) above the spinal cord.  Its not exactly a fine detail theory here, but we already have the body of neursocience for such detail,  Then we also have masses of data to see how consistent this approach is.   It appears to be pretty consistent with small scale neuroscience, but not everything, because there is still a lot of brain organization and function that does not appear to me to be directly linked to this large structure. The overall generalized blueprint is still pretty interesting though in just how much it can integrate high level functions, so this and the next post will try and introduce a summary of these concepts. For those who followed the progress, noted improvement and think there might be something worth looking into, the following is now a summary of how I can briefly explain what these structures mean for neuroscience. No it cant explain everything.

First of all, where did I have problems making this model work. Initially there was the barrier of how it could be consistent with current knowledge of brain complexity detail. In that sense there has been a high degree of success in a single explanation for every major large scale cortical feature, including a promising integration analysis for the primary distribution of neurons. Also the functional structure for the limbic system improved better than I hoped for with recent success from independent verification. The neuromodulation system (and brainstem) does not model well from the top down. i.e. It appeared to in my 2009 paper.

The tyrosine derived fitting nicely into left hemisphere consistent with their electrostatic chemical properties, and trytophan derived in the right hemisphere, both seemed consistent with the electrostatic properties (see paper discussion sect 1.6) for the cortex dipole model.  However there has been question over the scientific validity of the papers from Kurup RK and Kurup PA I used, which although not completely necessary, did fill out the data picture. Sure perhaps that can be accounted for later, but another big problem there is no morphological structure to these modulation systems that accords with dipole/multipole expansion. i.e.

Sure it can be said the cell bodies are in the brainstem which i don't claim to be able to model, but the innervation patterns are still operating from some type of bottom up pattern into rest of the brain.  If this is what I find, what will others find ? So I only claim large top down principles, but still these are pretty powerful and I have been successful with majority of the cortex and to some degree, a great deal of the limbic structures. Why this failure ? This model basically starts where HOX genes stop at the brainstem and Radial glia takeover brain formation and structure.


Brain simulation projects (see this excellent review) are a massively important part of human progress on many levels. Human disease, Progress of computation to brain like abilities, and even the hard to comprehend feasibility of humans being able to finally defeat mortality of the self through mind uploads. I also agree with most of Markrams proposals (if they are well implemented).  1. Integrate the masses of neuroscience which is now a collapsed tower of babel. 2. Create an environment to reduce animal experiments 3. Model the entire brain system to understand the entire system.  The idea of understanding the entire system is trained out of most neuroscientists at university and they acquire a high motivation to work on parts, and almost none to look at large scale solutions. Immersed in  such complexity the actual concept there might be some simple organizing solutions actually taken just from the evident structure is not considered possible.

Brain simulations aim is to model the entire brain structures, yet they currently have no model to understand the development such that they can frame the overall computation and function of neuro structures from a top down perspective. Their top down models are (so far) not computationally relevant to actual brain structures, and are superficially similar “moulds” or shells. My project which received first academic support in 2003 has consistently looked to large scale top down morphology for solutions to brain function, so I have produced more in depth models for the missing “shells” these projects require than is currently known. 

In Blue brain these shells are an isomorphic representation of cortical column. In brain simulation corporation these will just be generic scans of an average cortex or thalamus, filled with parts. They don’t actually have the complete developmental detail for the top down that is present for the bottom up, neurons, axons etc. That’s due to a fundamental missing conceptual idea in approach, simplified by this unstated assumption in neuroscience which my work attempts to falsify. "the large scale morphology of the brain does not drive its computational function".  How serious is this ?  It is very hard to perform top down structural reformation as systems become more complex. that's why radial glia fades in early development. Try altering the large structure in an Architectural project late on and the computers will grind to a halt. But changes in say electrical wiring (analogous to changing some axon functions) are more easily handled. 

Blue Gene systems neuro evidence shows that the top down model is more important than the bottom up model

Incomplete computational neuroscience models misrepresent the brain by claiming the shells or morphological surfaces are an emergent property of the parts, when it is the other way around and the shells create the function of the parts by the radial glia in a multi-level manner (see my post here from the conference integrative approaches to brain complexity) then read my paper for more in depth expounding of such concepts. The basic concept in a nutshell is that the complete distinct morphologies  (cortex, cerebellum, limbic layering) are the computational components of function ( they have a computation function I will soon try to explain), while the substructures start to make sense within the large scale structure (but you need to read the paper) . Columns, neurons, fiber bundles, axons, dendrite, synapess etc are the resolution of these large computational structures.   The information  to understand the origins of this concept is also consistent with findings from blue gene supercomputers in 2008 (Grant, 2008), but the insight for those controlling resources to realise the findings of their work requires a particular perspective (i.e top down morphology organizing principles).  I did try to argue a case for high level structure at the conference "integrative approaches to brain complexity". Seth Grant and others were not at that stage prepared  to deal with such a radical general approach (Seths team from the genome center will form a key node for the UK part of the human brain project). Part of the problem here is that high and low level features need re-conciled, these top down shells the simulation projects place parts into are completely missing the relevant information and functionality for them to produce a brain. Without a top down model to understand their formation, they might as well place neurons and axons in boxes or pyramids ! 

This might as well be the morphology for brain simulation projects with their current rationale.

Ok, just joking.  Obviously you can create various networks types by particular structure, and clearly bluebrain does that. Bizzarly  the toroidal supercomputer it specified is similar to my cortical toroidal model,  Neurons and axons have their origins nearly a billion years ago and never organized much of significance by current evolutionary standards. It was the emergence of radial glia which transformed them into brain structures. Neurons (leaving out dendrite computations) are parts with simple principle of space filling, growth and connectivity no matter where they end up ( see this by an ex employee of these projects).  What does Hermann's work there tell us ? To look at the morphology and it will reveal a lot if you study its 3d structure AND use all the traditional methods at the same time to get inside the system.  This is the same approach here, except look at the entire morphology for its physical function.

Modern parts of the brain, that actually produce complex computations, i.e. the cortex, the cerebellum, the limbic structures have arisen due to the way radial glial evolved to produce large top down MHD (magnetohydrodynamic) structures that can influence how neurons and axons self organize from the bottom up (SEE lab work of Fleury and my description of mechanism ). Let me be clear on two points.  1. That everything I propose is consistent with current neurodevelopmental mechanisms and even explains some mysteries in neuron migration. 2. That these structure are not fractal type co-incidence that look similar to dipoles etc, but are such magnetic structures with appropriate production mechanisms.  Dipole formation for cortex and cerebellum, linear multipole for limbic system which are proposed to have their own intrinsic and very powerful computational functionality. Such morphology is based on fundamental biophysical principles dictating how radial glia produce structure. This has a powerful set of advantages. 1.  Coherent magnetic field pulses can pass through biological tissue and so build large structure (i,e, the magnetic astroglia models) and 2. Such structural fields can assist with traditional guidance systems. i.e. organizing immature neurons (which have extra ion concentrations) and unmyelinated axons (which are ferroelectric) in development. 

The core top down model of two primary structures (get into specifics next). Cortex is an MHD dipole, limbic system proposed as linear quadrupole. They are inversions of each other due to boundary effects in development. Cortex surface would requires an MHD pulse to overcome the static earth background field, because if development does not pulse in a non linear manner it could not evolve the required field strength. So this then fragments the timing of the original Ventricular Zone linear harmonic modes into asymmetry (Dipole). We then get dipole-linear multipole interaction. As predicted from this theory and born out by later lab results.  Importantly when these two primary linear-non linear structures interact, they will produce more complex structures with higher EMF energy as a result at the Hippocampus, where EEG reaches up to 600hz and the signals are highly complex.

Something simple about this model to clarify it is basically two inverted MHD functions in development which give rise to a balance between integration (connectivism etc) and differentiation (fragmented function) multiplied by the complexity of neuron duplication it can organize within itself.  This balance is considered by some respected consciousness researchers (Koch and Tononi) laterly to be the exact sought after basic mathematical principle for any definition of consciousness.  in a machine.

Basically what this equation is defining is that transitions (including dynamic ones) between states where entropy remains low is dependent on a balance between increase of complexity with these inversions of function they cite. They stress functional integration based on the work of Olaf Sporns. So this model I propose which is based on actual brain structure and function is consistent with the state of the art in our understanding of the mind,  There are many high level computational neuroscientists trying to flesh out such models. I mention Izhikevichs attempts below.  The one thats going to work is the one thats actually reflects high level brain organization accurately, which i think these models proposed here do.

Computational structures and functions proposed to be the result of radial glia

It is a major conceptual barrier to consider that something as simple as a MHD dipole or linear quadrupole structure has a computational function.  To make it simple for this website I use images, 

1. The spherical harmonic based limbic system.

How something as simple as a linear multipole expansion (quadrupole) as proposed for the limbic system developmental morphology, is already used as a standard reference part for spectral or quantum computation. (Not that we are proposing the brain is QM computer). There is good evidence by Chris Elisasmith that the brain can implement broadly similar types of probabilistic computation via highly parallel classical methods.

(1) Ion traps use AC linear quadrupole for spectral reduction or quantum computation (2) which performs similar linear orthogonal function. (5) the limbic systems, semi complete discs fully completed (presuming no jaw intrusion) approximated to spherical harmonics, 3rd and lateral ventricles blue, thalamus and caudate (brown). All the limbic morphology is similar to linear multipole structure (see this post for more detail on this). (7) The areas around the midline 4th and 3rd ventricle areas are responsible for production of symmetrical continuous linear waves, alpha, delta, with theta from lateral ventricles. Which is interesting as these ion traps are also used to generate continuous oscillations for atomic clocks.

After I released my model for the lateral ventricles as spherical harmonics,in may 2009 (from my 2003 thesis), independently Monica K. Hurdal and Deborah A. Striegel released a paper in September 2009 that proposes the lateral ventricles can be described in neurodevelopment by spherical harmonics. Paper is called "Chemically Based Mathematical Model for Development of Cerebral Cortical Folding Patterns".  Somewhat of a relief, as I always had the greatest doubts over this validity of this proposal. Bear in mind though I take this further than lateral ventricles and apply it as developmental principle to the other limbic structures and the 3rd ventricle. This layering of spherical harmonics in linear manner is known as linear multipole expansion.

The limbic system linear multipole expansion structure is proposed to utilize the harmonic interaction principles (you need to read this post to get this concept and also see independent verification work) and also for more detail (section 3.3 of paper).  These types of processing are sought after in quantum computation. Again.. this is NOT a quantum mind theory.

Linear multipole expansion is the proposed model for the limbic system morphology. See links in following text for more explanation. For now whats important to bear in mind is that MHD evolving the facility to layer such expansions allow distinct subcortical structures to evolve for adaptive functions, while still retaining whole information coherence through symmetrical continuous waves moving in and out of phase in a linear manner. i.e. Delta, Alpha, Theta, Mu, Beta interactions are fairly even lateral symmetrical phase locks (unlike sporadic asymmetrical cortical gamma). Its elegance is also its simplicity. MHD dipoles and Linear multipole expansions are easily produced by radial glia. 

We are all unsure if or where any quantum level coherence exists in the developed brain but even Henry Markram and Christof Koch have found new evidence to support localized aspects of it which interestingly operate in the same frequency as the latest findings of high frequency Ca2+ waves (1-10hz) in the brain areas they studied.  The only known remnants of the radial glial system (for gap junction models based on MHD Ca2+ development fields) in the limbic areas post development are the thalamic reticular nucleus and neurogenesis areas in the ventricles (including the 3rd). However I would predict these areas follows the same principles of NAAMF (Neuronal associated magnetic fields)  that i propose form the cortex dipole, due to the same morphology of a gap junction loaded synctium as found in the reticular nucleus. i.e. The neurogenesis areas are just reduced components of the radial glia calcium ion synctium which is proposed gives rise to the entire magnetic formation for brain morphology. OK simplified that means there could be some areas of the brain where there are field diffusion effects, but whether they are fine grained i cannot predict. So much recent evidence is coming to light in fluid dynamics as well as probabilistic parallel processors that hint there may actually no big classical/quantum divide anyway. 

Even if the limbic system has no area with that quantum coherence (as in late 1990’s quantum brain theories), the remnants of there being large scale multipole coherence from a MHD gial field in early development (see fluery) could produce a similar “style” of adiabatic processing. i.e. The form of adiabatic processing functionality would follow the structure that gave rise to it before neurons become mature (but then when neurons mature it operates at the reduced speed of neuro-transmission) even after the developmental structure fades and the proposed MHD fields of radial glia are long gone. In other words a  quantum harmonic oscillator from an MHD field gives rise to a classical harmonic oscillation based system operating at the piezoelectric speed of axons, which have bent into a form that operates from the MHD fields configuration nodes.  If fluid dynamic researchers are right and quantum effects are just pilot waves easily instantiated in regular classical fluids, then we could feasibly have a quantum "style" system in the brain.

In essence then the ventricles in development are giving rise to the structure of a Qbit generator, except the resolution of the qbits spherical space, is not one quanta, but equivalent to e.g. the number of neurons on the thalamus held within the reticular nucleus (the nucleus could provides a coherent gap junction connected sheeting for overall coherence by EMF screening operating on the phase principle for screening). This may be extremely slow by comparison to a quantum computer, BUT... the spherical and internal structure is correct for a brain wide parallel cross association of information in adiabatic probabilistic processing style. i.e. Phase interactions in EEG oscillations were always fine for that purpose.  For note, I also transfer this principle partly to the basal ganglia and septal end of the hippocampus, but this posting is just a brief introduction to the concept.

2. The Magnetic Dipole formed Cortex.

This controversial but increasingly well known concept briefly is that the magnetic or ferroelectric dipole creates asymmetrical spin at its poles with mutual inhibition at the midline (domain wall). The pull force in neurodevelopment can produce convergent (left pole), divergent (right pole) recursive windings in columns (which are also magnetic types of structures – see poster) So this is why we have the larger interpatch collumn gaps in the left temporal pole. The large coding concept so far is that we get induction type sparse code function control. i.e. The ability of the cortex to process large sets of information with hierarchical invariance. The mutual inhibition of the corpus callosum follows the stochastic amplification principle of each hemisphere, that seeks to gain control of the  information suited to fit its asymmetrical lower energy states which are predicted invariant representations at the top of each hemispheres hierarchy, i.e. the left hemisphere will naturally extract convergent information (minimum description lengths) from sensory inputs and the right hemisphere divergent information (maximum entropy estimations). So the hemisphere partitions information,, literally the split structure is a computing partition, reducing the computational complexity below n^2, while the limbic systems phase locks increase towards n^n. Somewhere in between these processes the brain is a general learner which can maintain a good balance on average for complexity reduction.  This is the proposed reason why we have opposite type asymmetrical processing and powerful feature extraction/re-integration in the cortex hemispheres most notable at the temporal poles. (see table 1 and 2 in paper).

Bear in mind dipoles have a single magnitude vector which is divergent / convergent, but is this symmetrical or asymmetrical  ? (NOTE: poles above are wrong way around for proposed cortex poles)

To illustrate mutual winding asymmetry arises in ferro-electric structures like microtubules, with reverse converging / diverging patterns (from growth point of MT).  The converging force angle is stopping at a more lean precession winding than the diverging force. 

Now to think of the effect of such difference in cortical column if dipole forces are at play, thinking of the asymmetric forces being sparse throughout many columns (like HTM theory) in what are called interpatch spaces (space between dense areas of columns). The study from hustler above looking at left hemisphere language area show that there are lateral differneces in this sparse coding.  From my paper -- Greater separation of connectivity in cortical columns gives improved extraction of information from auditory inputs and separation of processing streams (Hustler and Galuske, 2003). In the right hemisphere auditory cortex there is greater number of interconnected columns (Hustler and Galuske, 2003). Dendrite spreading (Carter, 1999) in lower order dendrites from spiny stellate cells, which link together information within sensory areas, (Kalpouzos et al., 2005) are longer in the right hemisphere (Shiu and Nemoto, 1981). In other words there are more concentrated separations of neuron assemblies in the left hemisphere which are proposed to converge processing of sensory information.

NOTE: A physics objection raised is that dipoles forces are symmetrical at the poles based on iron bar magnets. Perhaps this is true for iron dipoles as the binding energy from the nuclear structure of iron gives rise to a symmetry point between all known fission and fusion in the universe which has me suspecting that the precession between electrons and nuclear force in iron are balanced. I am consulting with experts on this presently (01/08/2012). The dipole produced from Ca2+ flow would be predicted asymmetrical as the larmour frequency (torque due to nuclear configuration) should give rise to asymmetry in precession (spin angle in own large scale field) between its electrons and protons.  It is only recently we have been able to calculate the larmour frequency of Calcium for Nuclear Magnetic Resonance purposes (even then its Ca43, not Ca40 or Ca44 in biophysics) so for now exact asymmetry in precession between north and south poles in calcium ion magnetohydrodynamic fields have not been calculated.  

These extreme opposite invariant hierarchies inhibiting each other and interacting fit with both numentas HTM and Solomonoff induction type reduction schemes for general unsupervised learning. My first round analysis (unpublished) seems to indicate that both cortical biophysics and computational induction based on updating schemes will be consistent, with each other, but only more work will reveal what is the case. What it is important to bear in mind here is that if you understand the power of asymmetrical mutual inhibition of the dipole in terms of general partition functions, you understand that this means (like the limbic models above) it has a powerful computational function magnified by the number of neurons and recurrent connections within it convolving at a higher resolution than any feasible current neural network. The idea of the cortex structure being isomorphic and understanding it on that basis is wrong. Studies have found columns are not isomorphic at the temporal poles. The cortex and its columns can only be understood fully by its dipole asymmetry.  Each cortical collumn is a discrete bit within a wider cortical sparse code outlined above. So the magnetic formation of the dipole structure is the reason that columns exist in the first place. My point here is that to not understand this is to not fully understand the function and role of cortical columns in computation.   

To complete the cortical processing and consciousness model we may have to entterain some of the magnetic astroglia models and research primarily of Banachlocha NAAMF and others who follow up on his work.  These are abstract biophysical descriptions still in a rough stage, only further work in neuroscience can refine them for brain modeling, however without these there is only a minor possibility neuroscience can dig deeper into their many ramifications. For example looking at key integration points of the models. Ever wondered why the hippocampus has the highest electromagnetic activity in the brain ? This is explained elegantly using integration's of the above models. Now be ready !

Integrations of 1 and 2 (above)

It is interesting that the result of 500,000 years of radial glia evolution results in such a clear dipole (cortex) /quadrupole (limbic system) interaction as the engine for human/mammalian processing. However powerful computational functions are proposed to arise as the result of how these structures integrate with each other. This is expounded more fully in section 4 of the paper.

Also see the end of my post here from the conference integrative approaches to brain complexity… for how the limbic and cortex integration points produce complex large scale computation at hippocampus and striatum.  OK this next part is getting radical and I dont hold back when it comes to graphics, but bear in mind how stem cells work by calcium ion flow, and even adult neurogenesis appears to require the stem cells to sense ephatic fields before they are stimulated into growth. Sure, there could be other reasons, i.e. Cilia beating in the VZ is an adult stem cell modulator. Currently there are so many interacting factors for adult neurogenesis its a tricky one. It might be an idea to have the basic developmental models sorted before starting on those.

Each systems neurodevelopmental field poles (the dipole and quadrupoles) are structurally moving in opposing directions from each other, but exist on the same axis. These opposite poles  attract, while driving in opposite trajectories. The result is a spiral of intensely wound and dense neural activity of mixed field gradients. Hippocampus is then where limbic quadrupole meets cortical dipole. By analogy to other examples of two interacting dipole systems the S shaped twisting structure of the hippocampus is similar to the S shaped structure of binary stars, which are also two interacting dipoles in separate systems with their own trajectory. 

Simulations of two interacting dipoles moving on differing trajectories also produce the same S shape formations. (Schnetter, 2008) Binary star’s can produce the most energetic oscillation’s known as a result of this dipole/dipole interaction. Again by similar analogy, the hippocampus is marked with the brain’s highest energies, marked out by the temporal lobe tendency towards epilepsy where spikes can rise to 600hz and in normal function produces high frequency ripples of between 100-300hz correlated to electrotonic activity. (Traub et al., 1999) (references in my paper)

Putting it all together, the crude model for limbic system and cortex. linear multipole expansion and dipole proposed as inverted MHD functions of each other, with high energy convolution  integrals at the hippocampus. The large scale function provides an elegant solution for understanding of cortico-limbic computation. A hypothesis in 2003. Since Fleury's results (2011) and other evidence, mechanisms now a theory.

The hippocampus can integrate these two powerful functions along the temporal axis, as that is the shared axis for both dipole and linear multipole expansion in development (also other researchers verified part of myresult for this in 2009).  The hippocampus then has an integrated structure/function to  encode/decode through the hierarchy of cortical invariants at the same time it works with brain wide limbic parallel cross associations from the striatum for pattern completion, pattern selection.  That is why it is so complicated, important and energetic a brain area. NOTE: I missed out cerebellum here for simplicity but this also has a crude multipole model.

Impact of this top down structure on neurons and axons

This has just been a crash introduction to this complex project which I admit is a "Crude model". All of these concepts about large scale top down influence on neurons and axons by radial glia and the resulting morphology and the resulting large scale computational models in the brain is very controversial considering the paradigms by which we unravel biology so far. However dealing with brain complexity is not like that of other organs, but it appears we approach it that way so the top down morphology is ignored by large scale brain simulations projects (and many neuroscientists).

   These top down features are not suddenly going to emerge from modeling all the parts in it from the bottom up into an arbitrary empty shell. For that to happen you require the top down developmental system of the radial glia and its remnants to be completely modeled and interacting with the bottom up parts to achieve these aims. Without this it is unlikely current cortical models will ever do more than produce faint shadows of the large features they are disconnected from. If I am correct how can they develop asymmetrical feature extraction without the code outlined above.. and how can these models provide top down control to short term memory (I am not referring to frontal working memory here) ?  How can the limbic model gain the facility to process, cross associate and sort information using the phase interaction principles that such structures facilitate? Such projects will not fail completely but it will be missing some pretty major parts and principles.

A big post here. And still this is just a very brief introduction. Bear in mind recent lab works verifies my predictions for both the dipole and linear quadrupole, years after their prediction.  The idea is to accurately model the entire brain right ? Why ? Structure dictates function, and that knocks on to the structure of brain simulation projects, because they are building the hardware based on neurons rather than large scale morphology of the brain. If the brain simulation projects proceeds on the basis of building up a brain, from columns to cortical regions, basal ganglia parts etc..  then  (if you understood my models) the plan is partly wrong. The top down structure is the largest part which creates the map from which to frame the development of lower down modules, but you have to accept or even entertain the possibility the cortex has a dipole structure (limbic system linear multi-pole) to even conceive this rationale.