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.

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QUICK NOTE: AFTER COMMUNICATIONS WITH HENRY MARKRAM DIRECTOR OF BLUE BRAIN PROJECT I HAVE NOW RETRACTED MANY PREVIOUS CRITICISMS THAT WERE HERE DIRECTED TOWARDS DR HENRY MARKRAMS HUMAN BRAIN PROJECT. HBP WILL BE ATTEMPTING TO MODEL EVERY LEVEL (WITHIN REASON) AND ARE ALREADY RESEARCHING AREAS LIKE DIFFUSION FIELDS IN COMPUTATION  (21/07/2012).  I LEAVE THIS POST HERE TO MAKE GENERAL POINTS ON THESE TOP DOWN BRAIN MODELS AND BRAIN SIMULATION.

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.

Introduction

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.


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