Tuesday, 28 May 2013

Application of Casual Mathematic Logic (CML) to brain simulation

STICKY: I had questions on what SFNR is. The EU FET flagship Human brain project, requires prospective satellite bodies to be setup locally. The possible contribution to the project is  then established and from there affiliate linkup can occur. 

WHATS THIS ALL ABOUT ? : I basically seek highly integrative models for the cortico-limbic system as this appears to be the brains information engine. Approaches are fundamental thermodynamics, computational principles from entire structural morphologies and reading just about everything else that outs there.

UPDATE 21/11/2013 : Both CML neuroscience papers are accepted for JAGI special edition on WBE and Connectomics pending revision. The revised versions will be available as open access, and the review process should see some nice improvements.

Two papers have been submitted to Journal of Artificial General Intelligence special edition on Brain emulation and Connectomics.   They 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. What is that ? The approach I took to derive computation from the biophysics of brain structure had a major problem. How consistent is this approach with both fundamental physics and information theory. i.e. If principles of intelligence are intrinsic to the evolution of brain structures, then the 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 fundamental physical principles (such as least action and entropy).

The application of CML to the "go for everything" approach in the cortico-limbic system proposed here produces the first approach to a  complete "information engine" model for the brain (and AGi). Pretty controversial stuff. This paper where I am lead author is linked to here  "Causal Mathematical Language as a guiding framework for the prediction of “Intelligence Signals” in brain simulations"

The other paper where Prof Pissanetkzy is the lead other, 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.

Other current plans are an  application I have submitted for labtime 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. That will be called (if it goes through) : Investigation of  Ca2+ flow in radial glia to differentiate magnetohydrodynamics from the proposed chemotactic mechanism for the dipole formation in development of the chick forebrain.    (UPDATE 21/11/2013) I was not successful in procuring the labtime from Edinburgh University. This work is not only semi-controversial it is competing with many applications that are directed towards curing medical disorders. Anyway it appears other researchers in Scotland are finding more about dipole flow in neurodevelopment. i.e. Prof. Timothy Newman at Dundee, College of Life sciences.

Overall the dipole-multipole concept lab results are appearing as I predicted which is vindicating.  But as with the work from Vincent Fleury's Lab in CNRS Paris. I am still not resolved with both labs on the mechanism.  The reason I put in the lab request is 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 before the cortex morphology images were found, or to be very,very  clear, this is NOT an entire theoretical framework based on looking at a couple of pictures. As always my open challenge for anybody to try and break the theory (as plausible) exists as always (email in the link above). The postings on small scale neuroscience politics has been moved here now.

The images below are a fast hit intro and shows you the cortex Dipole, both human developed and track marking in chick cortex neurodevelopment (from an independent lab).  Another lab 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 this is a major issue, which I will try to address in this large post. What you are seeing, is no arbitrary morphology.  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. Now 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. They operate from the top down via the coherence of radial glia biophysics, and 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) 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 consistent 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.

The color highlighted texts in this blog are mostly hyperlinks to relevant blog posts on this site that expound the point. The link I made yellow above  because it is a vital read not to be skipped for those new to this concept.  The 2009 paper of this theory is here, but a lot has happened since, some of which is updated on this site.

-------------------------

QUICK NOTE: AFTER COMMUNICATIONS, 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 (that was the part highlighted in yellow) ?  Ten years on and now I recieve far less resistance to my claim the cortex has case for a dipole structure.  This approach also works for all other neuro-morphology, but for introduction strategy i hit with the cortex first as the evidence is becoming stronger and leave it there, as even this is just too much for newcomers to deal with this alone.  So after submitting to the possibility I am on to something the question often becomes, "Well so what if it does?". The answer is from a pure bio-science view it promises to give entire top down leverage by providing a single framework with  mathematical rationale derived from biophysics to make sense of all 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, so thats even better as then we 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, but its not easy to be brief, this is a very large PhD in progress, but be prepared, its unlike traditional neuroscience, yet I am making some very big claims.

From this position I am in (lack of resources) it is not possible to bring an A1 complete solution which hits every target (due to complexity of the problem). So it is vital for me to release works in incomplete stage so predictions are made and progress followed.  For those who followed my progress, seen 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.

OK, I just wanted to put limitations at the start. Now I try to present the background and concept on video, its not great, a future attempt will be scripted, but i was asked to make a live video presentation, so here goes.


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 is not considered possible.


So brain simulations rightly receive massive tax payer and private funding. Their 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. 

Many brain simulation projects wish to try and achieve their aims of entire system description, the top down aspect of the complexity is a “shell” which is no more than a fleshed out but non functioning mould taken of the large scale morphology. i.e. In Blue brain these 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 ?  If this assumption is wrong such projects will have a problem (my opinion).  If I am correct it is pretty Important that we determine if this assumption is true or not at the very start of the project. Why ? Because its very hard to perform top down structural reformation as simulations become more complex. Nature has problems handling this, 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

These 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 (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 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).  Although I did try to argue a case for high level structure at the conference "integrative approaches to brain complexity", Seth Grant was not quite ready 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).  If you clicked on my link (there is not enough room here to post all these facets) to read my summary of the conference perhaps now you get the gist that 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 a little.  Obviously you can create various networks types by particular structure, and clearly bluebrain does that. But if we have the insight for the full top down network structure (that it would now be obtuse to deny has  a case to consider), then it will need looked at. There is some top down approach going on. From the work on connectomes ive seen so far attempts at determining networks do produce some results, but they are still missing the astoundingly obvious dipole/multipole expansion structures. As mentioned in the conference report, 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 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. 

Here is where i start hitting you with the  neuroscience contraversy. 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 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(hope you are clicking these links !) 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 (not hard to accept if you are up to date on 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.  I have moved further into the area of computational neuroscience to develop this further and my 2009 paper summarizes  my first attempt to elucidate this computational function of top down morphology (and try to explain a lot of other stuff too).




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, so this then fragments the timing of the original Ventricular Zone harmonic modes into asymmetry (Dipole). Importantly when these two primary structures interact, they will produce more complex structures with higher EMF energy as a result. i.e. Hippocampus.


Notice something simple about this model ? Its 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 it can organize within itself.  This 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. 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, except these researchers have struggled and produced these general vague models as a result of decades of not being able to find concrete solutions in biology, although Tononi does try to apply to cerebellum (as do i).   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

If you have recovered from the above, in part 1, brace yourself for some more. It is a major conceptual barrier to consider that something as simple as a MHD dipole or linear quadrupole structure has a computational function. I have tried to explained this in my 2009 paper, it was still no easy introduction. It has taken me six years just to fully understand this myself. I produced the harmonic concepts for limbic system development in 2003, so these are still not as convincing as would like, but its the best i can do for now.  To make it simple I am going to hit you with images, because not many people want to read a 15,000 word technical paper. Since a great deal of this subject is about visiting neuromorphology with a magnetohydroynamic development model in mind, this is also a fast way to introduce the concept. The mathematical models etc, are slated for the next lot of papers.

1. The spherical harmonic based limbic system.

Here I will start by showing 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).



(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 continous 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. I realise we are in the thick of strange territory to add shock number 3, but 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.

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.

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 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 processing style. i.e. Phase interactions in EEG oscillations.  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 contraversial 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 collums (which are also magnetic 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). 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, 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. 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. 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 accept 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. 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 two types of sparse 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.

So thats 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 ? If what i say is true,  getting structure wrong from the start has an exponential increase on simulation costs. So the end result of saying this approach is too theoretical and undeveloped results in  wasting enormous amount of money, going off track and holding back progress to achieve these important aims for all of human endeavor.

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 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.


------------------------------------------------------------------------------
 

Wednesday, 27 June 2012

The politics of small scale vs integrative neuroscience


A question I wonder about. The dramatic changes in recent human history facilitated by technology allow us to look at the small scale of the world and as a result we derived such massive insights almost all of science and education is driven by paradigms and methods that we think it is inevitable to burrow down in this manner, or try to derive understanding of systems from bottom up digging out parts and simple elegant formulas in the hope it all integrates.  Well it did, up to a point, and that is the incomplete neuroscience we have.

This golden age is over, but the way we think about science in this manner pervades in such a manner it presents many barriers to try and persuade scientists to look at the large scale top down approach to brain complexity here.  These are clear over-arching structures which organize function proposed here, and they are controversial because lets face it its kind of nice to be educated with tomes of complexity that seem beyond us. So we hack at parts happy to do our bit for the next generation.  Well we have done enough. Now is the integrative time to start bringing parts together. What does the top down approach I bring say intrinsically ? That there may be no single low level model to explain all neuroscience. And by implication this also has ramifications for the computational neurosciences which require these insights from neuroscience.

This post is kind of a rant, but its not really because I have known since I started publishing in this areas that neuroscience is going to be increasingly integrative and so will end here with a recap of that. One of the first journal editors to approve my work was Denis Noble CBE. I was kind of amazed at how open he was to it, till I realized he had earlier in his career pioneered small scale biocomputation and physics approaches but had later moved towards top down system thinking. Not only that he was providing funding advice to European Bioscience policy to be more integrative something like 10 years ago and we can see this is what is now happening. But its a good idea to lay out where we are and why the approach to neuroscience here still has maybe a decade before its generally acceptable. So start with some nonsense to lighten things up.  Sometimes in my presentations to others, (particularly high level neuro conferences) I notice this can be the result ! While my work is constantly evolving, I cannot always get a good dialogue with the audience.


ABSTRACT: Longitudinal studies (lanzalaco et al., 2014) elicited the above startle response after a group of final year medical students were exposed to dipole neurology theory while follow up studies indicated evidence of PTSD in some students. . CONCLUSION: 0.5 litres scotch whisky with gradual exposure to the theory may be the effective treatment for postgraduate neuroscientists exposed to dipole neurology theory.

Ok kidding aside, this is a hard approach to hit neuroscientists with. If you pull my paper, you can see there is no problem getting into the nitty gritty minutiae  of neuron function and complex bioscience mappings, such that i was able to attempt to falsify my 2003 proposal that the small scale neuron distribution is consistent with large scale structure (sure I understand I could have been trying to make everything fit, so I had it peer reviewed more than is usual). I don’t have anything against traditional approaches and keep up to date with small scale neuroscience. As a supported of Whole Brain emulation I hope we can scan neurons to the nanoscale and beyond. What I do question is that the methods and approaches currently used to unravel brain complexity are not adequate to complete the job fully. I propose that by using the method of letting nature tell us what unfolds from the origin of entire brain structures in biophysics, this will contribute an enormous high level understanding.  As there is nobody else doing this,  I concentrate on that and the result is the style you see here is very different to neuroscience you know. I doubt you will see one neural component on this site, although there is plenty in depth on those in my 2009 paper. Because basically so much small scale work has been done, this approach I propose fails if it not consistent with the bulk of it. I couldn't build this theory without the decades of small scale neuroscience available to integrate which is fine work indeed and proceeds to be so.  Dividing up complexity has self organized to create a massive amount of grants and job stability within a global community. The interactions are endless ! 

There are already very many high level neuroscience theories but nothing like this. What we did previously was we would usually leave an attempt at high level maps to senior professors, thinking only a lifetimes experience reflecting on all thats been done can lead to any worthwhile high level review, and we hope such a review will reveal the entire system.  But since it never happened for so long we are kind of trained to think that this cannot happen based on the confusion with the current amount of small level data.

Now think about what I said previously. We are also kind of addicted to believing in science that good answers are in the small scale breakdown.  Well sure, clearly a lot of the small stuff is of course revealing a lot.  It also cannot be denied model as proposed here are the highest level integration you will find (if you accept its premise). The brain does have clear morphologies, and I hope my case is clear (or will become so) thats where the highest level functional answers are to be found.  Interest in the high level brain function derived from structure is amazingly not present in the experts in the field, even when given prolonged exposure and all questions answered as to any doubts. We really believe the solution is to be found in neurons or other bits and pieces. Is it not clear yet that's not working ? 



What took neurons from jellyfish to building complex computational structures ?  Radial glia.  More complex structures will have more complexity around the glia evolution. Images of glia and goldfish visual system.  http://webvision.umh.es/webvision/Nona.html

The final completion which puts it all together is in developmental mechanics and Radial Glia.  A good percentage of the neuroscience community is supposed to at least try and understand how this system works at the highest level possible, yet it does not appear to happen.  Looking at current plans at the top level of neuroscience, the plan is now to increase resources to pour into even more high resolution detail. Clearly the system needs understood at micro and lower levels and the components are cells, BUT this could get ridiculous in terms of being blinded from seeing the forest. Not from the trees, but the branches and leaves and cells and their DNA/molecules. The awe with which we introduced to the small world, never leaves.  Yet nature has no rules which says complexity has to be focused on the small world.  It keeps piling more levels one substrate on top of another. DNA - cells - building of larger lifeforms - Radial glia is the top level of the complexity of brain evolution and development of structure. A recent report "Cis-regulatory control of corticospinal system development and evolution" defines what differentiated mammals from the rest of clade craniata (creatures with skulls). The SOX transcription factors assist reelin a key molecule in maintaining radial glia structure. See "Populations of Radial Glial Cells Respond Differently to Reelin and Neuregulin1 in a Ferret Model of Cortical Dysplasia". Physical evolution has been slowing down and the changes in our brain are centered around humans being the substrate for cultures and tribal/national civilization. We are now seeking to use our consciousness to develop ourselves as the substrate for advanced information systems which will operate on top of civilization (see the new science of substrate independent minds).  i.e. Nature is constantly developing mechanisms to build at the highest level of complexity and scale as is possible. Look into complex systems and they will explode with increased functionality when a new means of top down control and organization evolves.

The current situation in neuroscience is akin to a native culture coming across an incomplete wheel, amazed it its engineering detail, as well as what it is they start concentrating on the spokes, then breaking the spokes apart. Years later they start having meetings on various theories about the parts ensues. More plans are made to melt the parts or perform chemistry on them. When all we had to do was rebuild the high level structure of the wheel to understand its functional properties.  The same applies here. Just look at the large structures (and their integration), then complete them (as you see in my morphology images) and you get the high level principles. From there you have a wheel and you can spin it and understand its function. The answer was so simple all this time.  Complexity has defeated us. We have become blinded by the huge amount of data we created tearing apart the system. Want to compound that ? Then go for more of the same. 




Current state of neuroscience. We dont know what this objects function is, so lets invent increased levels of high resolution micro-scanning to figure it out. The answer is rebuild the large structure to complete it, then you can spin that and understand its highest level function. What do i mean spin the brain or rebuild the structure ?   You can read this paper which introduces this theory and completes the structure, or this which gets more into the high level functions.i.e. Actually spin the structure in theory means understand then run the computation of the high level magnetic dipole/linear expansion structure of the radial glia (or for developed minds glia and ventricular zones) filled with neurons and traditional small scale structure/function.   

An expensive simulation will be required to achieve this in practice, but the idea to get into this is so simple.  This is why I attack the premise of ground up brain simulation projects, even with every part known it will be incomplete, because the brains structure like that wheel is incomplete (read the papers or more of this site). Yes a brain has small scale function of no doubt, thats what neuroscience today is focused on. Its a bad idea to stick to that when a high level solution appears. This is a multi-level problem, both top down and bottom up integration at the same time is more intelligent than keep burrowing smaller because thats the habit. The majority of neuroscience today is bottom up. Understandable as there is more complexity there, but how much of that is to run the brain on OS (operating system) Biology. i.e. How much is redundant in comparison to the evolution of high level structure ? It may turn out to be lets take a guess at a 50/50 ratio of top down/bottom up if we get rid of the DNA/cellular substrate. I Dont actually know, but it requires looked at.  Another problem is whats considered top down in neuroscience is not from the top at all, but about half way to a 2/3 up. We will get into this later in these lengthy posts.

Sure there are countless cases where the small approach is justified and those scientists or groups have strong beliefs and/or rationale, but we are discussing the general situation in an average lab here. Why ? usual reasons, group politics, current investment, resource issues. Of course all the disease mechanisms require that detail. And of course Whole Brain Emulation which i support. However I still think there is also intuitive re-activity to stepping onto a top down perspective. 

Is this really over-simplified ?

For ten years I had denial (often obtuse) these are not the structures I claimed. Now with recent independent evidence, the next issue I deal with from scientists is denial of computational ramifications. i.e. its moved to along the lines of "Ok, so lets say those structures are as claimed, they are probably meaningless structures with no computational meaning or relevant organizing principles to neuroscience" .   These dodge the issue, and are similar to those i dealt with before I had some lab evidence the cortex dipole concept was plausible.





What dominates science today, social status, position and access to resources

More bio-resolution creates more questions which creates demands for more funds=more jobs, more status which builds the current situation. So we get less answers about more and more. Not that we arent finding out a lot, especially for disease and low level functions. So clearly there are good reasons. The result is nobody understands the system, and even more prevalent is giving the idea up of understanding it becomes part of education.  If status is about looking to be seen to go for a reduced version of understanding in a certain way, thats what today's scientist will do to survive. There will be no high level quest. There cant be when no project can understand all the data we produced.  To get back to the high level, requires a break from the way we are going about breaking the system. However I can finish with not complaining, because actually its not so bad where I am. In Europe this has been accepted and stated for some time now. In European funding the pressure is to integrate and this has influenced the USA.  SO the good news is that at least large scale integration is proposed to be the next big scientific approach. But will it actually happen. In my next post i try to highlight these projects will still not be top down enough. They are going to need entire morphology principles to complete the big picture.