Neurons’ “antennae” are unexpectedly active in neural computation

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.


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:

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.


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.


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.


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.


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:

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.


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:


@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.


@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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Thanks for joining the discussion Dinu!

When I think of biological antennae I first picture something like this for detecting self-motion, (chemosensory) odors and taste:

Dendrites of neurons contain chemosensors, also chemical and electrical synapse:

For other cell types:

A radio antennae is also a good analogy, but the antennae are then limited to detecting electric/magnetic signals:

I’m now not exactly sure which antennae the article most has in mind. Thankfully I at least know that you think of dendrites as more like a radio antenna, which (except for motion and chemosensory) works for me too.

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I am an electronic engineer so I do have some familiarity with the principles of electromagnetism. To create an electromagnetic wave you have to consider both the amount of charge, and the speed and distance of movement of this charge. In this case, we have a very small amount of ions moving over a very short distance over a relatively long period ( very low speed). I am at work at the moment and pressed for time but I think that if I worked the numbers it would result in a vanishingly small electromagnetic field, certainly much too small to influence the gate potential of an adjacent cell membrane in any meaningful way. We need to see millivolts to have any effect and the electromagnetic field with these starting conditions should work out to picovolts.

To make things worse, as soon as the activation wave starts to move along the cell dendrites and axons the net effect through the cell body would have to consider that the same thing is happening at both sides of the cell body, but in opposite signs, canceling on the lateral axis through the cell. This leaves the mechanism of field generation along the longitudinal aspect of the cell under consideration. In an unmyelinated fiber, we are looking at a propagation rate of about 0.5-2.0 m/s. Plugging these factors into the formula for the generation of electromagnetic potential yields a very tiny figure.

Restating the key issue: Gates trigger thresholds are in the range of 5o millivolts and the voltages due to electromagnetic mechanisms (both generation and reception) are several orders of magnitude less than this.

You are welcome to demonstrate the calculations for both the field formation and receptive antenna geometry to show me where I am missing the electromagnetic transmission mechanism but I am not able to see how it could work.

@dpatirniche 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.
I would apply the same considerations to these areas of inquiry. This stuff is not magic - it takes a certain amount of energy to make things work and the available electromagnetic energy is vastly too small to have any meaningful influence. I would add that at a distance - the fields from the individual cells would decohere and all you would have is an averaged “brain-wave” which is what is measured in electroencephalography. With this method, even with direct contact with the skin, you can only read somewhere in the range of 10 µV to 100 µV. As with all energy, this drops off with cube power law distribution (filling space) so even at arm’s length, the signal is too small to be detected by any physical mechanism I am aware of.

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Yes, I have something similar to a radio-antenna in mind. But it is funny that you think that detecting electromagnetic signals is a limitation. What other signals are there?

Of the four fundamental interactions (i.e. strong and weak nuclear force, gravitation and electromagnetism), electromagnetism and gravity are the only relevant ones. Chemical specificity and proper electromagnetic waves in matter or vacuum, are mainly driven by the permanently-present electromagnetic field (that exists due to the fact that charged matter in relative motion to each other exist at all times). Mechanical waves are also driven in part by electromagnetism and in part by the action of the masses as they are displaced through space.

Combining fluid mechanics with electromagnetism and solving these fully-coupled equations on 3D biological volumes would definitely be the way to go, but I’m afraid that current numerical solvers will still require significant development until such gargantuan task can be tackled. Not to talk about massive quantum electrodynamics. This is a reason why you do not find too many references when you search for biological antennae.

Even pure electromagnetism in matter in its fully-coupled 3D version is still a very difficult task to solve. There are some studies that show very promising results on (incorrectly-bounded) 3D biological structures in this direction. Check out:

Regarding the wiki article that discusses biological antennae, I don’t think that the name of this organ is a misnomer, but that they are actually electromagnetic organs with a fractal spectrum.

Electro-chemical, which is how I understand the mechanism of the synapse and action potential to operate.
I am familiar with myelination which is why I used the phrase “unmyelinated fiber” as this is what is happening in the dendrites. This is why this part of the brain is typically described as Gray matter.


Electro-chemical signals are electromagnetic signals. Chemistry, at this level has to tackled with the same instruments. Molecules are atoms, which are moving charges, that maintain a certain spatial relation to each other. Since the only particularization that is allowed is a geometric one (as the fundamental physical constituents are not variables), electrochemical signals can be well described with an electromagnetic formalism.

Perhaps, but not as antennas or as propagating fields.

As far as what happens at the ion channels, the interactions are not so much a free field but a very structured lock & key mechanism that requires a specific molecule to activate the gate. It is a huge stretch to try and cram that mechanism into faraday’s laws; the wrong tool for the job at hand.

I do work with corrosion mechanisms as part of my job and this is pure electrochemistry. Applying relativistic or electromagnetic mechanisms is just plain inappropriate to the task at hand.The contribution of these elements are surely there but so small as to be lost in the thermal noise energy of the constituent parts.


Yes, classical electromagnetism isn’t up to the task of resolving molecular constituents, but surely quantum electrodynamics will. Classical electromagnetism is just a limiting theory to more refined set of laws. These more refined theories, if applied to biological architecture will refine antenna perspective, but any static description will not even in principle be able to account for the biological

It might be true that you can do well without magnetism in a homogeneous bath with large electrodes. However, biological structures are more much intricate and much closer together than one might naively think. For example the typical depth of the extracellular space is approx 60nm; i.e. the extracellular volume that separates two cells is typically 60nm in any direction, and approx 15nm in the synaptic cleft.

A 100mV pulse propagating with 10^6 dendritic-spine-head-diameters per second (a dendritic spine head diameter is approx 1um), passing not at an arm’s length, but at 15nm, will surely have a noticeable effect. Especially since all cellular spaces, and in particular dendritic spines (see the picture below, which is a segmentation that I performed from electron tomograms of cerebellar dendritic spines) are laced with a dense macromolecular mesh, that, due to the static charges that all hydrophilic molecular surfaces posses, condensates ions, thus altering the conductivity of the intracellular fluid.

The highly intricate conducting substrates force highly elaborate circulation patterns to develop within these regions.

You will see as much as you willing, or able, to integrate. If you want to get anywhere close to a real estimate then you need to start with a very small animal and integrate over a small, yet still extremely vast, and richly patterned volume.

Since it is well known that the drift velocity of electron, or ions, is not the same as the propagation of an electromagnetic field in a conductor, right questions to ask, I think go somewhat like this:
How can you propagate an electric pulse within a conductive medium with a velocity of 1m/s? What conditions need to apply in order for such an event to occur? Electrical currents find the path of least resistance quasi-instantaneously, and energy will be transported along this path from thereon. How can electrical energy be slowed so many orders of magnitude?

I await your definitive paper(s) on the impact of this line of thought on neural-computing theory and practice.

What parts of the neural-computing theory do you think will be most affected by the insights you offer?
The configuration of components?
Some other aspect?