“The relativistic Brain”: Nicolelis & Cicurel have replicated the finding of NAAMF brain structures and its hybrid physics derivation the” information engine”

In a highly unexpected breakthrough for this work, world renowned neuroscientist Miguel Nicolelis and Co-author Blue brain project mathematician Ronald Cicurel have published a book which explains how their research replicates the central set of hypothesis in Dipole Neurology theory. 

They have validated

• The concept of the brains white matter having a magnetic mechanism called Neuronal Electro Magnetic Field (NEMF). NEMF is similar  to the developmental mechanism I modified from Neuromagnetic Researcher Dr Marcos Banaclocha called Neuronal Activity Associated Magnetic fields (NAAMF).  Dr Banachlocha was one of the pre-reviewers of my 2009 works on this area where I co-authored with Dr Wajid Zia. In 2009 we presented our analysis of the brains neuronal content (in magnetic distribution terms) to the sanger institute conference “integrative approaches to brain complexity” (See Lanzalaco & Zia 2009a, 2009b in references)

• That the white matter which we can observe has a magnetic structure and function which defines the principles for neural oscillations as an intrinsic white matter property. (see my table of white matter oscillations used in several of my papers here,  (See Lanzalaco 2015; Pissanetzky 2013 in references)) 

• They then concur that we should conceive of the primary (cortico-limbic) structure in terms of a division into grey/white matter to derive a similar type of fundamental physics hybrid dual process “computation engine” (see Section 4 Lanzalaco & Pissanetzky 2013 in references). Like the action entropy “information engine” (which refers to grey/white matter) I worked on falsifying with Professor Pissanetky it computes just by providing it a means to follow the most fundamental laws of physics. Relativism and Action-entropy are different interpretations of the spacetime frameworks..

• That the computation engine is of such a form we can use it to guide brain simulation and AGi projects. However we veer paths here as it is still not clear to myself (even as a higher order system) why these propose this problem is uncomputable (as apposed to less tractable). See my latest paper to Future of Humanity Institute (FHI), where I mention the white matter in terms of synchronization and computability issues (See Lanzalaco 2015 in references)

So from similar premises, the same set of logical conclusions result. However there are also differences between our works but overall these are two sides of the same argument. i.e. Differences about: The specific mechanisms then, conclusions on neural coding and computability. There is not space to go into all this here, but this is a table which summarizes all this.

Showdown time ! Comparison chart of these two similar general physics

theories for the brain. But there are differences

Relativistic vs  Langranian thermodynamics, different sides of the same coin via the Hamiltonian ?

A point to note here is that relativistic view of spatiotemporal events or an action-entropy classical view are two sides of the same coin, for neuroscience purposes anyway.  Relativity is derived from action as are EM and Magnetic fields and we can vice versa derive an action principle for Relativistic MHD (See Physics derivations in references). The reason I opted to put the formalization (Link to our 2013 JAGI paper) in terms of action-entropy was stated in the introduction of that paper. Basically Action-Entropy approach covers all bases. It allows us to  make some distance from the original EM framework to determine if other aspects of it can be tested independently by the Causal logic approach used. Second, that action is closer to thermodynamics and we can also derive EM from it, giving more options. And lastly that the least action principle was used by my Co-author Sergio Pissanetzky for his biophysics approach to AGi and general computation which we applied to formalize, unravel and test the dual process hybrid physics information engine.

Spatiotemporal events are still present except action is defined in groupoids as casual sets of spatial vectors and entropy is the arrow of time. We can still define a quantum harmonic oscillator with action in groupoids, but relativity could be more useful to help decode synchronous firing. For example, in the brain time is integrated into oscillation bound information so there is a relativistic aspect there. Information bound to beta oscillations will be multiplexed in a different frame reference rate than information bound to theta oscillations.  The brain does have mechanisms to resolve conflicts between and integrate these different frame rates, and  there are some very specific models for how Dipole neurology predicts the representation of space and time across complicated brain structures, which are alluded to in the previous papers and this website. The entire approach including details for specific brain modules (as well as an educated guess for the brains neural coding schemes)  is planned for the next publication.

Magnetic brain models and structure, where we are now with EM fields ?

I have posted a summary of the latest research on axon solitons, white matter, glia and magnetic or ephatic fields here. Some of these are a big area right now, so a quick summary is required of what is being proposed here in biophysics terms.  Initially I followed a magnetic model, but have for some time since 2009 primarily concentrated on a neurodevelopment theory for NAAMF. For that I have allowed myself to be guided by the experts in regards to how far on a limb to go with adult neuron function fields. The idea is to be careful not to go off into quantum mind territory, because the problem is always this. Current MEG readings do provide brain wide magnetic field, but these are derived from the sum of individual isolated neurons. There is no mechanism to amplify the magnetic field fall off in a tranverse plane across axons of any significant distance?  Many are being developed , but even taking all the newest ideas into consideration the maximum number of axon solitons in a field would be a dozen or more and not brain wide.  However, we know that we can cut of the neurons and the white matter oscillations still persist but this could be a network effect that was not controlled for (references in this post).

Mostly we have learned in neuroscience biophysics is all very finely controlled by complex proteins interactions.  Does all this finesse rule out any crude generic magnetic field across white matter ?  The complexity of ion channels and mechanisms even  in the candidate for a magnetic mechanism, (the axon myelinic synapse) suggests these new concepts still obey regular neuroscience complexity. Douglas Field in his address pitched towards the Obama brain initiative does mention the white matter glia could assist slow wave timing coherence, but not by a field model. However as my last summary reminds me, there is a lot still to be found about the axons, and the reasons we have not is due to the lack of tools for that, and these are being developed now. My position is the fields are primarily large during developmental stages then fade to persist in glia but under restriction of myelin as we think. I keep an open mind on what could be going on in developed brains though as the brain has EM structure all the way through it. 

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. 

Where do we go from here ?

In their most recent interview on singularity 1 on 1, the authors describe that the labwork, long sought after by myself and many colleagues involved with NAAMF (Lester ingber, Alfredo  Pereria, Paul Nunez, Marcos Banachlocha) could gain the  financial backing from Duke University to finally settle the biggest validation required for this nearly 15  year old controversial theory. To track the actual MEG readings from white matter to brain wide neural synchronizations. This requires highly expensive equipment and expertise which Professor Nicolelis appears keen to ensure is provided. The publication of their book has prompted a project for many of the worlds leading magnetic brain researchers to form into a “Neuromagnetic research team” setup to further the major challenges of this frontier research, raise the media profile and push for funding. For that reason it is good time to summarize the proven track record of dipole neurology theory in terms of improving with new data.

A scientist is only as good as their last contribution, but there are plenty of unreleased improvements in the pipeline.  I aim to contribute the following to the research team for this project. With this replication of results occurring at such a high level in popular neuroscience, hopefully there will be an increase in publication and some more funding towards a general theory for the brain (which works !)

• Complete MHD time and space representation at the detailed level of brain modules
• Further work on Axon solitons in terms of MHD flux tubes
• Multiscale brain models. i.e. From NAND gates to neural partitions with general filters
• From dendrites and nodes to population burst codes to entire structural operation i.e. Place-grid cells
• Further neurodevelopment and glial models, in the full detail required for that discipline
• The suspected underlying high level AGI, computing class and neural coding scheme

REFERENCES

Physics derivations

Dalrymple, D. 2012. The principle of least action. Available electronically online at

http://www.edge.org/response-detail/11722

Deriving the Nonrelativistic Principle of Least Action from the Schwarzschild Metric and the Principle of Maximal Aging

Edwin F. Taylor 

http://www.eftaylor.com/pub/GRtoPLA.pdf

Sylva Poirier

http://settheory.net/least-action

The Least Action Principle and Relativistic Mechanics

http://arxiv.org/ftp/physics/papers/0512/0512110.pdf

An Action Principle for Relativistic MHD

http://arxiv.org/abs/1501.07829

http://www.scholarpedia.org/article/Principle_of_least_action#Relativistic_Systems

See also summary of least action derivations from EM fields in

http://www.degruyter.com/view/j/jagi.2013.4.issue-3/jagi-2013-0006/jagi-2013-0006.xml?format=INT

Lanzalaco, F., Pissanetzky, S. 2013. Causal Mathematical Logic as a guiding framework. Journal of Artificial General Intelligence.

Lanzalaco, F & Zia, W. 2009a. Mechanisms for a cortical dipole structure by VZ calcium waves. arNQ

Lanzalaco, F & Zia, W. 2009b. Dipole Neurology an electromagnetic multipole solution to brain structure, function and abnormality. Wellcome trust, Hinxton, Cambridge, UK.

Cicurel, R & Nicolelis, M.A.L.  2015. The relativistic Brain. Kios Press.

New paper “Neural foundations for the classification of AGi and Superintelligent systems”

Available online : https://drive.google.com/file/d/0B_wcM5ZfmEE5aWlKN3U1WUtlbk0/view

“Abstract

A proposal is made to justify the utilization of simplified models for self awareness to be used as a classification for Artificial General Intelligence (AGi) and Superintelligent (Si) systems. These models are derived from entire neural topologies and their respective neural markers such as cognitive processes and biophysical signals. Self Awareness is defined generally and then in network terms. Current proofs for AGi-Si development are reviewed and these cast doubt on the predictive power of current algorithmic methods to guide the control and understanding of AGi-Si development. The benefit of computational neuroscience methods are expanded upon further in terms of their detail and depth representing likely actual AGi-Si development. It is concluded that evidence exists to justify exploring the use of general guiding frameworks for AGi-Si classifications which are derived from computational neuroscience”

This paper was inspired by the press hype around dangers from artificial intelligence and summarizes some of the ideas I have on whether brain structures can tell us anything deterministic about the nature of general intelligence. For that we need to look at various proof systems.  It is proposing a classification system for all general intelligence system may be deterministic and was submitted as part of a research grant application to FHI Oxford.  The central concept disagrees with FHI press position to some degree. Primarily because my work tells us simplifications of brain structures tell us something pivotal about the dual process nature of general intelligence. And not only that, but uf general intelligence has an optimal physical form then AGi has certain types of topology.   The work is still in rough shape. I will upload to ARXIV when I Iron out some of the conclusions and re-do the proofs. It has some rough similarities to “TheUniverse of Minds” by  ROMAN V.YAMPOLSKY in terms of reference to mind classifications and computational equivalence. I wasn’t aware of his paper till later however, but my proposal is still different in that I insist we impose a physical grounding, especially so for self improving systems that will have more of an issue when dealing with physical limitations.

Digging out more from MHD brain theory for general computation

Although the first general framework was sketched in causallogic terms in the 2013 paper this framework is a general physics grounding, and it was always stated the MHD theory would have to get the game up and explain more complicated implementations at multiple scales in brain structure and function. This is a big project in progress right now, but its over-due to make some general statements on where it is going, without getting into which neural coding schemes are being evaluated.

If we look at the most prominent neural processing features, they scale across three primary neural levels. In order in diagram above (Hausers dendritic computations)  1. The logical NAND functions and filters within neurons. 2. These can then form entire libraries of logical arrays and analogue style resonant filter banks at the population level (some of the classifications izhikevich)  3. Across the entire brain at macroscopic level general filtering is thought to occur in multiplexed action selection, where the hemispheres increase speed of switching sides to deal with more difficult problem tasks.

None of this reveals any general coding scheme in detail, and I propose we will require physics models for that. For now there what we comment on is that the highest level general multiplexing in action selection completely belies the massive number of underlying neuron banks capable of doing something similar.  There are analogies to these filter banks and logical physical components used in Deep learning, not surprising considering the neural roots of deep learning.   What is going on in the coritco-limbic “information engine” at the overall level is still something many of us are working on. We have some rough ideas though and suspect large scale architecture is the key to our general abilities on deep problems.   

There is a body of literature to suggest that human performance on NP problems is good although not optimal approximate results do occur (See Humans on NP in references).  Knapsack, Travelling sales problem and graph colouring are something we evolved for to travel, hunt and deal with finite resources.  It also appears that in comparison to the same difficult level on P space problems (i.e number of nodes) we may be better at NP problems, which is the inverse for classical von Neumann architecture (with the same working memory to node ratio). So we may be using a generalized NP engine for all problems including P-space.  However this is a complicated area and contentious to propose right now, as the landscape of computational complexity had many overlapping facets.

What we do know is the brain architecture is very different due to its parallel topologies. The class of NP problems is also very amenable to parallel matrix computations and quantum computers also leverage parallelism. i.e. Hyperconnected quantum states provide traction on these dense graph type problems, which is not surprising (left and middle in image above). However classical computer architecture is also evolving towards similar hyperconnected states (supercomputer toroidal setups, right in image above) so it could be there is no mystery about quantum computing, as it basically facilitates hyperconnected states and we then leverage this at some given resolution. The gap could be currently closing between the two broad classes of serial and parallel hardware. What does this mean for the brain ? As I have stated repeatedly on my papers and this site for too many reasons to go into, it is not a quantum computer. But the fact is has a magnetic structure (which has quantum structure) arising in neurodevelopment has endowed it with hyper-connected parallelism as part of a hybrid entropy-action system (derived from white/grey matter respectively)

What we can see is that the corpus callosum has the toroidal structure. And the limbic system also has similar network properties (see this summary).   If we look at the association areas of the brain they are wide ranging and use the largest white matter loops. This probably facilitates wide across network breadth searches while also maintaining columns with local order. So even with massive internal complexity that grows across species, this basic magnetic structure type physics via billions of connections through axon solitons, allows overall computational coherence and fast, synchronized integration of signals. The coding scheme itself we are still figuring out, there are many candidates to be tested. The good news for this project is that MHD structure does reveal one of the most powerful natural coding schemes known. This will be highlighted in a future publication.

REFERENCES

Discrete optimization using quantum annealing on sparse Ising models

http://journal.frontiersin.org/article/10.3389/fphy.2014.00056/full

AI-Complete, AI-Hard, or AI-Easy: Classification of Problems in Artificial

http://www.academia.edu/1419272/AI-Complete_AI-Hard_or_AI-Easy_Classification_of_Problems_in_Artificial

Human performance on NP problems

Human Performance on Hard Non-Euclidean Graph Problems: Vertex Cover

https://web.uvic.ca/psyc/masson/CMS12.pdf

Measuring Human Performance on Clustering Problems: Some Potential Objective Criteria and Experimental Research Opportunities

http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1001&context=jps

Human Performance on the Knapsack Problem

https://books.google.co.uk/books/about/Human_Performance_on_the_Knapsack_Proble.html?id=SF5TQwAACAAJ&hl=en

Human performance on the traveling salesman problem. Percept Psychophys. 1996 May;58(4):527-39.

http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1090&context=jps

MacGregor JN1, Ormerod T.

MacGregor, James N. and Chu, Yun (2011) "Human Performance on the Traveling Salesman and Related Problems: A Review," The Journal of Problem Solving: Vol. 3: Iss. 2, Article 2.
http://dx.doi.org/10.7771/1932-6246.1090

Neuro images

http://pages.cs.wisc.edu/~tvrdik/6/html/Section6.html#AAAAAEmbeddings%20into%20meshes%20and%20tori

http://brain.oxfordjournals.org/content/brain/128/10/2224/F1.medium.gif

http://brain.oxfordjournals.org/content/brain/128/10/2224.full.pdf

Application of bio-inspired algorithm to the problem of integer factorisation

http://cecs.louisville.edu/ry/Application.pdf

EM solitons proven in the brains axons. One of the primary tenets predicted by Dipole neurology framework.

If this theory is proposing a magnetic brain structure from the entire morphology that includes Magnetohydrodynamic (MHD) field lines, then there is no getting away from the prediction that the axon bundles will if at least be obeying laws of a larger MHD system.  Or maybe even axons or their glial cells are actual MHD generators. Other authors in this area have proposed  magnetic fields might occur at a long range (even across hemispheres etc) in adult brains.  Marcos Banaclocha's Neuronal Activity Associated Magnetic fields (NAAMF) 2003, then expounded 2007. Following on, were increase in publications on biophysics of glia I refer to:  Ingber & Nunez, 2010 , Bokkon & Banaclocha, 2010 ,Pereira 2012, Størmer & Laane, 2009, Størmer et al,, 2011. Prof Ingber of Caltech supports the proposal( recent paper) .  Pereira & Furlan provide a more neuroscience based overview for glial magnetic processes. However this is something I have restrained from proposing beyond neurodevelopment except in the limited form. Primarily due to the lack of evidence and various theoretical restrictions from my framework. Primarily that the MHD field drop off is to steep to go anywhere (without non neuron mechanisms) and the fact that dipole neurology is predicting an adult field should be very long range. However a question has always puzzled me. It went like this

Does MHD behaviour play a role in the morphological form and function of axon bundling right across the large scale symmetry of the brains white matter ? Particularly if in the developed brain some complex context that can be understood in terms of these four complex levels most neuroscience theories will have to be explained in.

1. Neuron-axon electrophysiology. i.e. Ion channels, gap junctions, plasma fluids, membranes, synapses etc
2. Is the biophysics proposed at Developmental vs Adult level
3. How does it incorporate Glia of different types and vascular system
4. Gene transcription, Cellular messaging. Cellular energy and protein transport

The big problem is biophysics has always been found to be deeply intertwined and controlled in these contexts of increasing priority (1-4). And also facets of complex system theory and computational theory are involved here, which are also different in form from morphologies we derive from simple physics. For example if proposing magnetic mechanisms for an entire brain, then the big question is, are axons a direct morphology from function in the sense of representing magnetic field lines in some way ? If they are then there are simply no primary structural morphological features left in the corticolimbic system that are now not well explained in terms of magnetic fields playing an increasingly major role in neurodevelopment. e.g. See this poster. Of course we are still left with so many outstanding theoretical holes, but some new evidence also which will be summarized here.

The first confirmation of MHD structures (as HD in this study only). See this post for summary

What I did find evidence for in terms of long range biophysics to explain white matters magnetic structure  appearance was entire hemisphere Ca2+ pulsing through the radial glial scaffold. (see in references Weissman, TA et al; 2004) .So the dipole neurology theory since that time has been driven by study of glial biophysics, rather than the neuron/axon. The big news here is that axons have recently been proven to be MHD in form and function.  Due to the development of an extension to the Hodgkin Huxley model for nerve transmission called the soliton model . This resolves some long standing problems over axon thermodynamics. There has been some controversy over the Axon soliton model since proposed by Thomas Heimburg and Andrew D. Jackson in 2005, but last year the Membrane Biophysics Group at neils bohr institute confirmed that solitons do pass through each other as predicted by the theory. Here we should emphasize that the axon soliton is equivalent to an MHD flux tube soliton. Primarily because by their very nature EM fluids that are symmetrically confined are plasma MHD solitons due to the perpendicular magnetic field.  The MHD soliton can be an adiabatic sound wave (see table 1 here) just like the axon soliton and so the axon is now consistent with every other observable morphological neural aspect being magnetic (except for neurons themselves).  How does this scale up to magnetic properties across the entire brain structure ? The MHD tubes are the field lines.The first question is to what degree is there, if any coherent magnetic fields across MHD solitons ? Because bundles of tubes wit a single MHD soliton (with some dipole moment) does not always translate to that field strength facilitating an entire magnetic field coming from a white matter structure.

The first confirmation of axon solitons passing through each other - top left, (see “axon solitons” in References). MHD plasma tubes are also solitons in the same way. Amongst alternative neurophysics theorists we are reluctant to entertain others who go to far into quantum mind, however the fact is Matti Pitkanen of University of Helsinki, has got it right here (top right in image above) at least in this particular case. The images below are mathematical calculations to determine if solitons can cross axons in a transverse manner, as there is now intense interest in axon models which use solitons or other means to generate tranverse waves across the axon bundles (see “axon solitons” in References)

Can there be magnetic fields across white matter bundles or the entire brain ? We do get MEG readings for such wide areas and they can be synchronized oscillations, (see “Magnetic fields at population level” in References) but our evidence so far is that each neuronal component contributes isolated activity. The framework for oscillations to occur across the brain is laid down in neurodevelopment and its this framework that is triggered by neuronal ensembles. The mechanism for Dipole neurology theory providing a magnetic field to create entire field structure is in the Ca2+ waves of the radial glia scaffold in development, there is no myelin when the brain is developing, so fields provided by the Radial Glia would be less restricted. The axons are made primarily of Ferroelectric microtubules and ion channels so would conform easily to any complex MHD field. After the pathfinding has taken place the Oligodendrocytes (white matter glial cells) start to coat the cortico-limbic nerves up to 20 at a time. So in these circumstances there should be a reduced magnetic field. 

Magnetic field lines turn out to be flux tubes and can be solitons if the form is plasma,  but do these help us to understand axon formation ?.  Axons are marked by their regular linear bundling (note these bundles are not from the corpus callosum shown above)

In the developed brain radial glial scaffold fades to the existing astrocytes and oligliodendrocytes, and we are left speculating over whether there is any magnetic field in developed brains, primarily because neurons, axons themselves don’t provide such long range fields.  Much of this speculation and various models are documented on this site and the 2009 paper. Most of the recent developments on ephatic fields etc again appear to rely on the glial mechanisms, but without more work we cannot be sure. As axonal ephatic models show (see “axon ephatics”  and “axon solitons” in References) the more synchronized axons are added in a coupled state the greater the decrease in conduction speed, and this may be a principle of mutual information in physics. Because relatively speaking within the entire brain system itself lower speed = greater entropy due to the competitive dynamics increasing speeds locally.  This may be the reason that the brain wide oscillations tend to be slower, and the local fast oscillation tend to decoherence. This is most notable at the brains primary poles where the fast ripples are the sharpest waves. This is one reason I define oscillations in terms of entropy (see JAGI article 2013).

Artists impression of dipole neurology framework simplified in terms of oscillations. Note how the most integrative long ranging entropic oscillations tend to slow down settle around the midpoint of a system, and the fast oscillations produce the sharpest waves at the hippocampus

We know a lot about the brains neural oscillations and the major role they play in integrating information. There is still a lot that is not known about how they arise, and why there are more of them where there is more white than grey matter. See my table of oscillations located to matter types here in this blog article.

White matter computation

First there was dendritic computation, then synaptic, now axonal.. what next glial ? It appears so. Now is a good time to summarize the fact that white matter in particular its glial cells is a current frontier field of neuroscience. Led Primarily by Douglas Fields, author of The other brain (see “douglas fields” in References). Douglas titles one of his papers “white matter, matters” !  He summarizes how it possible to modulate impulse speed and so in theory effect neural synchronization in white matter, by injecting current into oligodendrocytes. This is called the axon myelinic synapse (see “axon computation” in References).

Douglas fields at Glial Biology in Learning and Cognition, held at the US National Science Foundation in Arlington, Virginia

“The complex branching structure of glial cells and their relatively slow chemical (as opposed to electrical) signalling in fact make them better suited than neurons to certain cognitive processes. These include processes requiring the integration of information from spatially distinct parts of the brain, such as learning or the experiencing of emotions, which take place over hours, days and weeks, not in milliseconds or seconds”

There is now evidence for non synaptic computational properties between axons which do the following (see Debanne and Sami Boudkkazi under “axon computation” in References)

1.  Integration and amplification 2. Routing 3. Inhibition 4. Network resonance 5. Synchronization 6. Plasticity

 Left: Diagram from Douglas fields showing how action potentials can be inititated across the glial cells.  Right: This has been named the "axo-myelinic synapse", (see “axon computation” in References).

The outstanding question is still what are the biophysics here ? We know that we can cut of the neurons and the oscillations still persist (see “axon computation” in References). We have touched on ephatic coupling in the myelin being MHD solitons, but what would amplify the magnetic field fall off or enable resonance across axons ?  There is clearly still complexity controlled by proteins with a finesse that appears to rule out a typically crude generic magnetic field across white matter, or even fine grained quantum field containing information (in a generic linear sense) because the axons etc are not organized at that level of precision. The complexity of ion channels and mechanisms  in the axon myelinic synapse suggests this is still regular neuroscience complexity and the magnetic field which assists magnetic structure formations are developmental. Remember development does not finish until reach age 25.  Even for neurodevelopment alone why would this field be present at all ? It is because there are so many desirable properties of MHD solitons to be recruited in the complexities of brain development and computation. Note these MHD properties are also properties present in the white matter.

• Oscillation
• Symmetry
• Domain walls across entire structure
• Rope or “tube” flux structure
• Magnetic Connection across tubes

What computing functions do all this give rise to ? Hyper-parallelism with deep information convolution across layered structures is my primary idea. The hyperconnected graphs that are good for solving generalized problems in approximate ways such as discrete optimization problems appears to be the reason. But that is another subject, and  more of that in the next post.

REFERENCES

Ca2+ waves in development can propogate through entire hemisphere radial glial

Weissman, T.A., Riquelme, P.A., Ivic, L., Flint, A.C., Kriegstein, A.R., 2004. Calcium waves propagate through radial glial cells and modulate proliferation in the developing neocortex. Neuron. 43, 647-61

Axon Solitons

http://phys.org/news/2014-09-nerve-impulses-collide-unaffected.html

http://medicalxpress.com/news/2014-09-spikes-collide-foundation-neuroscience.html#nRlv

http://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.031047

Penetration of Action Potentials During Collision in the Median and Lateral Giant Axons of Invertebrates

https://www.researchgate.net/publication/263789234_NON-LINEAR_SOLITARY_SOUND_WAVES_IN_LIPID_MEMBRANES_AND_THEIR_POSSIBLE_ROLE_IN_BIOLOGICAL_SIGNALING

NON-LINEAR SOLITARY SOUND WAVES IN LIPID MEMBRANES AND THEIR POSSIBLE ROLE IN BIOLOGICAL SIGNALING

http://www.igi-global.com/article/propagation-front-waves-myelinated-nerve/52977

http://arxiv.org/abs/nlin/0610071

Propagation of Front Waves in Myelinated Nerve Fibres: New Electrical Transmission Lines Constituted of Linear and Nonlinear Portions

http://arxiv.org/abs/nlin/0610071

Pulse Dynamics in Coupled Excitable FIbers: Soliton-like Collision, Recombination, and Overtaking

Axon ephatics

http://www.ncbi.nlm.nih.gov/pubmed/19887308

Thresholds for Transverse Stimulation: Fiber Bundles in a Uniform Field

http://link.springer.com/article/10.1007/s00422-003-0430-x

Conduction in bundles of demyelinated nerve fibers: computer simulation

http://link.springer.com/chapter/10.1007%2F978-0-387-22463-3_8

Ephaptic Interactions Among Axons

http://www.macs.hw.ac.uk/~chris/HWM99-40.pdf

Ephaptic Coupling of Myelinated Nerve Fibers

Douglas Fields

White matter matters.

http://www.cs.unc.edu/~styner/public/DTI_tutorial/1%20Scientific%20American%202008%20Fields.pdf

http://www.nature.com/news/neuroscience-map-the-other-brain-1.13654

Neuroscience: Map the other brain

https://science.nichd.nih.gov/confluence/display/snsdp/Regulation+of+myelination+by+neural+impulse+activity

Regulation of myelination by neural impulse activity

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2756778/

Oligodendrocytes Changing the Rules: Action Potentials in Glia and Oligodendrocytes Controlling Action Potentials

Axonal computation

http://www.pnas.org/content/103/42/15646.long

A beta2-frequency (20–30 Hz) oscillation in nonsynaptic networks of somatosensory cortex

“Surgical separation of deep from superficial layers at the layer IV/V border abolished neither rhythm”

https://www.researchgate.net/profile/Sven_Hendrix/publication/226551250_Regeneration_After_CNS_Lesion_Help_from_the_Immune_System/links/0046352b9a3a6dba40000000.pdf

New Aspects of Axonal Structure and Function

See Chapter 4 New Insights in Information Processing in the Axon Dominique Debanne and Sami Boudkkazi

http://www.nature.com/nrn/journal/v5/n4/abs/nrn1397.html

Information processing in the axon

http://www.ncbi.nlm.nih.gov/pubmed/18634564

Modulatory effects of oligodendrocytes on the conduction velocity of action potentials along axons in the alveus of the rat hippocampal CA1 region.

http://www.sciencedirect.com/science/article/pii/S0166223611000920

The axo-myelinic synapse

Magnetic fields at population level

http://link.springer.com/article/10.1007%2FBF01191640#page-1

Task-specific magnetic fields from the left human frontal cortex

http://link.springer.com/article/10.1007%2FBF01190388#page-1

A four sphere model for calculating the magnetic field associated with spreading cortical depression

http://link.springer.com/article/10.1007%2FBF00229864#page-1

Source analysis of magnetic field responses from the human auditory cortex elicited by short speech sounds

http://www.ncbi.nlm.nih.gov/pubmed/18652805

MEG correlates of bimodal encoding of faces and persons' names.

http://www.nature.com/srep/2012/120509/srep00401/fig_tab/srep00401_F2.html

New MEG sensors can detect axons

http://www.nottingham.ac.uk/magres/research/meg-research.aspx

Large scale distributed brain networks identified using MEG measured beta band oscillations.

Application of Casual Mathematic Logic (CML) to brain simulation

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

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

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

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

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

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

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

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

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

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

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

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

OTHER DEVELOPMENTS

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

A 'chemotactic dipole' mechanism for large-scale vortex motion during primitive streak formation in the chick embryo.Phys Biol. 2011 Aug;8(4):045008. doi: 10.1088/1478-3975/8/4/045008. Epub 2011 Jul 12

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

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.

http://en.wikipedia.org/wiki/Tony_Wright_(sleep_deprivation)#Left_In_The_Dark

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

http://boingboing.net/2010/10/09/how-the-left-hemisph.html

Word version of critique here

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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.
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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.
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