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

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.