Neurons’ “antennae” are unexpectedly active in neural computation

After adding a supply of virtual microglial cells to nibble the buds just right we’re likely into 10,000 hours of work to get almost there, but we have to start somewhere.

Thousand Brains type thinking simplifies the problem by being possible to use metabolic network level SDR’s and whatever else is needed from HTM theory to approximate overall synaptic and RNA behavior that leaves metabolic residue or whatever. All by itself a single neuron is expected to have a small but impressive brain of its own that makes good predictions, but where it’s for that reason expected to not be easy to model it would be a shame if it were easy as an electronic logic gate. At least requires no watering at all.

TBT depends on this older HTM working as described; this is assumed as being in place.

As far as glial cells not covering synapses - that just makes sense. If they are to be exposed to axons to make contact it would not work very well if the were covered up.

With all theories being to some extent “tentative” it should at this early point in time be no problem to where need be take “HTM” right out of TBT. You may have already seen my somewhat related reply to Matt for the last hangout.

There only needs to somewhere in the cortical mass be found the whole bunch of smaller brains with a moving straw views of the outside world. In fact I was quietly hoping that the theory would end up needing to include at least some of the individual neurons. In that case TBT only becomes even more awesome.

I wouldn’t say this at all. The sequence memory algorithm we explained in our 2011 white paper is essential to all further Numenta theory. If we find that this model is inaccurate, we will have to rethink all the sensorimotor theory and TBT.

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Yes, you can “rethink” your theory. That was more or less what I was saying. The whole thing does not necessarily have to be thrown out just because one part needs updating.

From my perspective TBT would be a shame to abandon now. For that reason Bitking’s mention of “TBT depends on this older HTM working as described” was a little bit alarming to me.

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This is the first in-vivo evidence for dendritic spikes, right? That can be a big thing.

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It is one thing to adjust details of HTM sequence memory as we better understand biology.

It is another thing to throw the whole mechanism out because it is not working at all like we thought.

I have not seen any evidence of this 2nd thing yet, has anyone else? Even this paper that started this thread seems to confirm it.

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@rhyolight I don’t think that it is the issue - it’s just that the connection to the older papers get more tenuous as time goes forward and there is a large conceptual distance between today’s TBT and the papers it is based on.

I am always grounded in these older papers and the connections though to todays work but it does take a certain depth of understanding of the theory to see these connections all the way from the synapse mechanism level through the mini-column level all the way up to the impact on the higher level representation level.

This does go further, to the map level representation, onto to the map-to-map level, and global interactions between the maps and the sub-cortical structures. I have been working at these levels for many years so for me; theory tweaks at different levels always have to be considered through the lens of what impact it has on all these levels.

While this can be complicated it also has the advantage that known behaviors at various levels puts some constraints on possible answers at other levels. This sanity check does give a nice go-nogo check on proposals when I encounter them.

This does make it very hard to find someone to talk at a peer-to-peer level at parties.

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I think I know what you are saying. For me the still biologically unknown HTM related details (but fair amount on cell intelligence that now exists) makes it so that when I look for evidence of a direct connection I end up back at complex cells already having some ability to piece together a moving straw view of (depending on what they are best adapted for sensing) what is of most interest to them in the outside world, which in turn gets expressed by column level behavior. TBT thinking this way holds true, but I cannot use HTM as further evidence to support it. And if that’s true then the problem is that the HTM model for a neuron has to have at least the inherent behavior of a slime mold then to be most useful to biology need cell to cell RNA exchange communication into well networked germ cell line chemistry.

I see a safety net for what Jeff described with a straw and most defines TBT thinking, which is not dependent on the fate of HTM. With that considered what you described seems more like the need for something that takes a fresh start, like a HTM-2 reboot that features the best of the old school HTM School videos.

Once you add the action of the lateral axonal connections to the basic HTM model you get the powerful features of the TBT or Calvin tiles.

There is no need to go beyond that to mechanisms like RNA exchange.

Yes, each cell in the mini-column has a very restricted view of the world but it shares that view with the neighboring cells using these lateral connections.

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Times like this I start thinking about how nice it would be for you to have a full HTM neuron in a dish type visual of all that working.

The forum sees interest in using HTM for genetic applications, so I’m not alone in most wanting that sort of thing someday included. The ultimate would probably be a database connected model that molecular biologist would all want to code the behavior of their discoveries into. But don’t overly mind my sometimes thinking way ahead. Years from now after what is still a mystery has been explained there is at least that left to work on, to help keep the forum active working on something, never know.

RNA also just happens to be one of main origin of life interests, connecting through multiple sciences then finally to neuroscience. Whenever it seemed to me that HTM theory alone was not enough to support TBT there was my faith in that, to see me through. Although in my approach you have to endure my need to connect TBT to as much biological accuracy as possible, like metabolics and especially make sure to explain where any antennae are. Our from different approaches arriving at the same conclusion is better than one alone.

It’s excellent that we are fully on the same page, my view exactly. It’s why I indicated something that the cell has, influencing behavior column level behavior. Problem is in finding a fast easy way to model how all this going on inside the cell, to people who think we’re both nuts to think that way. What ever happened to the old school few rows of circles with lines to all others where magic Weight formula does all the rest?

This is exactly the point where you connect to cell chemistry then are next into RNA behavior anyway. To help I will include text I compiled from what I wrote for Reddit. It covers current planetary chemistry origin of life theory on up to our brain level, neuroscience. The vital RNA World OOL molecules are narrowed down to small number in this summary:


https://www.nature.com/articles/d41586-018-05098-w

Where you start here DNA becomes a came after thing that happens on its own by having RNA behavior right, not something coded in. This means there is a way to skip all the DNA related complications, for those looking to apply HTM to origins related genetics of some kind. Once you let them know about that, HTM is already into the most exciting RNA science around. In this case it’s real easy to get to the very starting point, where all begins. This is what I have so far to go with it:

As opposed to being like an alive on its own substance DNA is an inert material with properties of a crystal, RNA molecules use to “etch in stone” for future generations. Folding and unfolding packages it up for replication. Exact pattern reflects path taken through geologic time, what its RNA creators learned, instead of (as with snowflakes) exact path through atmosphere.

You may have earlier seen this that shows I had no problem finding all of the (things to look for) requirements for intelligence in self-replicating RNA:

Clues to the origin of intelligent living things are found in rudimentary molecular systems such as self-replicating RNA. Since these are single macromolecules that can self-learn they are more precisely examples of “Unimolecular Level Intelligence”, as opposed to “Molecular Level Intelligence”, which may contain millions of molecules all working together as one.

REQUIREMENT #1 of 4 - SOMETHING TO CONTROL

The ribonucleotide sequences are a memory system that also acts as its body. The motor muscles of RNA are molecular actuators, which use the force of molecular attraction to grab and release other molecules. The catalytic ability (chemically reacts with other molecules without itself changing to a new molecular species) of ribonucleotide (A,G,C,U) bases combine to form useful molecular machinery. Where these bases are properly combined into strands they become a mobile molecule that can control/catalyze other molecules in their environment and each other, including using each other as a template to induce each others replication. Unlike RNA that exists inside a protective cell membrane (as our cells have) these RNA’s are more directly influenced by the planetary environment, which they would have once have been free to control. Modern examples include single strand (ssRNA) viruses that can control the internal environment of their host and may now have protective shells with sensors on the outside for detecting other suitable host cells to enter and control, for the purpose of reproduction. In some cases after invading a host cell other sensors can detect when conditions are right to simultaneously reproduce, thereby overwhelming the immune system of their hosts, which could otherwise detect then destroy them.

REQUIREMENT #2 of 4 – SENSORY ADDRESSED MEMORY

On it are molecular sites, which can interact with nearby molecules to produce repeatable movements/actions. Its shape can include hairpin bends that are sensitive to the chemical environment, which in turn changes its action responses to nearby molecules and to each other.

A variety of properly ordered molecular species can easily be produced by wind/water motion or wet/dry cycles, resulting in quadrillions of different combinations all being tried in all the environments where the stuff of life in great quantities constantly accumulates, such as deep basins and via “skimming” onto ocean shorelines. Their combined activity also changes their molecular environment, much the same way as living things have over time changed the atmosphere and chemistry of our planet.

REQUIREMENT #3 of 4 - CONFIDENCE TO GAUGE FAILURE AND SUCCESS

Molecular species that can successfully coexist with others in the population and the environmental changes that they caused are successful responses that remain in the population. Molecular species that fail are soon replaced by another more successful (best guess) response. The overall process must result in collective actions/reactions that efficiently use and recycle the resources available to multiple molecular species or else there is an unsustainable chemical reaction, which ends when the reactants have consumed each other, resulting in an environmental crash.

REQUIREMENT #4 of 4 - ABILITY TO TAKE A GUESS

For a rapidly replicating molecule RNA editing type mechanisms can become a significant source of guesses. Also, molecular affinity will favor assimilation of complimentary ribonucleotides but where some are in limited abundance another ribonucleotide may replace what was previously used. The change may work equally well, or better, for their descendants.

Doing the same for DNA results in describing what the RNA and support molecules cause to happen in a fur ball that on its own does nothing. DNA is then more precisely a long term memory system that to RNA’s would be analogous to a library or internet, a transcribable storage for what has been learned through time by ancestors except for RNA is billions of years older and can be directly transcribed into unique RNA individuals that serve a vital specialized purpose in the complex molecular society of a living cell.

Behaviorally speaking there is a fractal unfolding that begins with RNA behavior. There is then a cell that has antennae and such like multicellular animals do. Unlike DNA alone it’s easy for the system to meet all circuit requirements. After that is unfolding the same to multicellular behavior level.

After replication DNA genome 3D cross linking and chromosome territory intermingling returns back to the same places. For the most part RNA controls the self-assembly and self-disassembly of the crystal lattice structure. Surrounding membranes that self-assemble around the DNA nucleus further help hold its shape.

In this reply I showed what to expect of a DNA alone (by tangling like I did or more controlled) cross linking it would when wet be strong enough to stay together on its own, not bowl of overcooked spaghetti that falls apart upon contact with water.

After chemical extraction from the rest of the cell DNA is most like a crystalline skeletal remain. In it are the imprints of RNA behavior that uses it to sustain a living system through time.

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News that indicates cells are more animal like than most people expected is all good for a theory that requires a thousand times more brain power from each neuron.

If there is a 2nd thing (in addition) going on then it’s most like what my long time favorite on Cell Intelligence has been experimenting with, mostly pertaining to stem cell behavior:

http://www.basic.northwestern.edu/g-buehler/FRAME.HTM

It’s (at least currently) beyond the purpose of HTM theory to answer questions at that level of detail, but it does not hurt to have some evidence for stem cells having a brain of their own too.

Along with that is of course the additional surprise that the origin of the RNA programmer of DNA has been well enough pieced together to start in chemistry, towards a more complete neural model that includes genetic behavior.


Even for supercomputers an atom by atom molecular dynamics simulation is too much of a number cruncher. For the complex RNA’s (and their associated molecular subunits usually made of protein) the important detail produces more like virtual critters, where it can be useful to tweak behavior by making them intelligent enough to adjust their own behavioral parameters to match experimental results, based on RNA nucleotide and/or protein sequence and folding environment.

In my opinion the Thousand Brains thinking and biologically relevant HTM neuron makes Numenta an early pioneer of the emerging science of Cognitive Biology by specializing in human brain level cognition. Your work is able to influence the work of those who get more into the chemistry level details.

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I also found something in the previously mentioned Cell Intelligence information that seems to relate to the limitation mentioned in the Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex paper that reads:

One limitation of the HTM model presented in this paper is that it does not deal with the specific timing of sequences. An interesting direction for future research therefore is to connect these two levels of modeling, i.e., to create biophysically detailed models that operate at the level of a complete layer of cells. Some progress is reported in Billaudelle and Ahmad (2015), but there remains much to do on this front.

First there is this:

Chapter 2 will use the apparent symmetry and identity between the branches of the phagokinetic tracks of dividing cells (an example is shown below) to argue that cells are programmed to measure angles and time durations.

Then Chapter 2 concludes:

Not only are the tracks of sister cells related, but the shapes and internal architecture of their bodies (i.e. their cytoskeleton) appear as symmetrical or identical as their tracks are [See ref 1,ref 2]. This suggests that the programs that determine the future movements of the cells are implemented by building and re-building the inner architecture of the cells [See ref 3].

A HTM neuron already has paramaters that approximate chemistry related cytoskeleton architecture changes. It would be a task of adding in an approximation of angle and duration behavior of self-motion. In social amoeba experiments time durations are by a almost all soma quickly learned, to ahead of time prepare for an air blast they are sensitive to.

This got me to thinking how placing a cell in a neural environment can make outside world stimuli directly applied to its antennae would become the new self-motion it senses. In this way a cell could become uaware it has become stationary, only sense a like through straw like view of itself moving around in the outside world “we” the cell colony call “us” are through our eyes seeing. A HTM example would be sensing the moving up and down with energy consumption, while the cells make predictions useful for preparing for what happens to them next.

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Here I try to argue why neurons must be actually treated as antennae, not as cables:

https://www.researchgate.net/publication/332818326_Nonconservative_Neurobiology_A_geometric_perspective_upon_synaptic_transmission

https://www.researchgate.net/publication/325568644_Dynamic_Aspects_of_Finite_Architectures

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@dpatirniche - welcome to the forum.

Please forgive me for asking what may seem like a clueless question:
How does this differ from the very old and well-established concept of the action potential?

Also - are you familiar with the basic principles of SDRs and how that related to the learned patterns of synapses on the dendrites as described in HTM theory? If so - how does what you are describing differ from this theory?
As I understand HTM theory, it is not like a “directional antenna” but more like a pattern detector.

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Put us in mind of three tuning-forks (fig. 6a/b), but to your point, pattern detection seems so very fundamental to recognition and so may be necessary for such an antenna to operate correctly.

Perhaps you should read and understand the basic model that is the core of HTM.

All the papers are here:

The paper that fills in the details of how the dendrites recognise and respond to patterns is here:

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Most helpful thanks, especially if you might be able to cite in the literature where pattern recognition might rule out such an antenna or if you prefer, why you feel they would necessarily be conflicting hypotheses?

I just did - the entire paper is necessary to convey the concept.

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@Bitking - thanks for the welcome!

The theory that I have put forth is a geometrised biophysical perspective upon the electrical operation of cellular complexes. As the body of a living being is perhaps the only “objectively” observable aspect of the biological reality, the theory introduces topological objects (i.e. lynns) that may be assembled in such a way as to create a bijection between the material architecture of a biological organism and the mathematical model-space. Lynns and lynnganisms are thus concepts that may be used to partition a given 3D biological volume, into a compact set of non-overlapping spatial objects.

Viewing any closed dielectrical sheet as representing the boundary of a lynn, a biological organism can be understood as a finite, numerable set of nested conductors that are electrically insulated from one another. For example, each phospholipid bilayer found in a brain is instantiating a lynn with a precise position and shape. Note that not only cellular membranes are captured by the previous statement, but also the membranes of cellular organelles, vesicles, etc. as well as the membranes that create the blood-brain barrier, the vascular system, etc.

Knowing that transmembrane structures are embedded in each biological membrane, permitting the exchange of molecules/ions across the dielectrical interface, transmembrane electrical currents can be inferred to exist within these constructs. As all electrical currents flow in circuits, and since the conductive biological substrate may be unambiguously known, an electrical theory that plays out within a biological volume must be able to trace the paths along which currents flow.

Note also, that partitioning a biological organism via a scheme with explicit spatial extents, and using this partitioning scheme as a foundation for an electrical theory, forces you to drop some artificially introduced boundary-conditions from the modeling formalism. What do I mean by this? For example, in the classical treatment of electrical events that are said to occur in biological spaces, one often incorrectly assumes that the extracellular space is a pool of infinite extents at a fixed electrical potential. Assumptions of this type linearlise electrical currents, wrongly concluding that an electrical flow exist between two points which are different electrical potentials due to a local ionic imbalance, without having (or ignoring) a return path.

Even if one schematically places a circuital electrical current within a well-bounded conductive substrate, it is immediately clear that a magnetic field must be elicited by this circulation. Formulating thus the electrical operation of biological volumes within an electromagnetic framework, it is natural to expect that inductive effects (i.e. non-conservative fields) play a role during the biophysical operation.

If one tries to understand how an action-potential is propagating within a minimalistic framework that comprises of three lynns: the presynaptic and the postsynaptic neurons, engulfed within the extracellular space of finite extents, from the perspective of a stationary dendritic spine, the approaching action-potential appears as a time-dependent increase in the magnitude of the Poynting vector. Since in the period preceding the release of a synaptic vesicle, the postsynaptic ligand-gated channels may be assumed closed, the time-dependent change of the magnetic field that is caused by the currents that sustain and propagate the AP, will induce an electromotive force in the postsynaptic neuron.

Different from an electrostatic theory, when one allows the magnetic field to be part of the physical formalism that describes the operation of a biological organism, one will find that in order to compute the induced quantities, one has to integrate over the entire domain. This global perspective is what I mean by nested electromagnetic antennae. If one starts investigating more closely these aspects one might observe that exotic states (e.g. slow light, holography, cloaking, photonic crystals) can not be excluded, and that cables, as understood nowadays, are but naive projections of a much richer physical reality.

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