<p>Here are some notes and ideas, and hopefully an answer to your question:</p>

<ol>
<li><p>New membranes are produced by a process during divisions as you read in genetics; sometime calls embryogenesis.It is just the divisions of cytoplasm. The layer than separates nuclides are suggested to polarize toward nuclides inside a single cytoplasmic they all are. So, as it might be guessed by the definition of the word "cell", normally in final form of a tissue there will be no cell inside a cell (or its cytoplasm). They will be split into two cells, as always in science this is what "usually" happens (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734032/" rel="nofollow">This article provides some backgrounds on how this mechanism works</a>).</p></li>
<li><p>There are however vesicles, simply bags of some chemical stuff (e.g. neurotransmitters). They fuse into the cell membrane at its terminals and some N.t. would be released. They are not living cells though, but their outside layer is similar to the ones in a real cell, made in form of phospholipid bilayer.</p></li>
<li><p>Rather than neurons, there are some more cells in the nervous system, called <em>Glias</em>, even more than neurons. Their main role seems to support neurons and synapses. You may think of them as supporting cells, joint with a neuron or a synapse helps them being activated or deactivated. There are recent studies, investigating the role of Glias in the brain.</p></li>
<li><p>Neurons (for example in cortex, which is the most influential area of the brain interested computer scientists) do not perform activations alone. But they form a network; in fact networks of networks. In cortex, they together form columns, super columns, and in human around 6 layers. They usually study groups, columns or bigger forms of interconnected neurons, except in invasive single neuron imaging techniques. So neurons have been in some sense studied doing computations <em>"on average"</em>, but not alone. Even the activation in neurons (action potentials) are generated at a place called axon hillock, that does not rationalize having two cells can simple generate two independent signals and then merge them together; this can be done easier at synapse level since nature is just a sloth and select best random outcomes.</p></li>
<li><p>Synapses are where information is stored in artificial and natural neural networks. So even a cell within a cell (connected), does not provide further improvements (like new trends of providing computing power necessary for deep nets). By cell-within-cell in ANNs you just add another connection and possibly a new layer. You can do the same by defining new layer as in deep networks. </p></li>
<li><p>Action potentials to send signals of activation forward in cell are performed and developed in places outside the cell body (soma). There are usually occurs in dendrites and mostly in axons. So biologically speaking. A cell body within a cell body does not help or intervenes in computations or for communication. If it happens in real world, there are just mutations, or wrongly or in-lab developed ones (It is possible, right?); similarly other solutions perform same mechanism, for example like a single neuron, or two connected neurons normally through a single synapse.</p></li>
<li><p>Though the idea could be investigated by self-similar systems (fractals), dimension of self-similarity measures, dynamic systems, and in general chaos theory. In neural system this fractal dimension is an integer at cell level (a single cell). You can however study how this self-similarity works for artificial neural nets :-).</p></li>
</ol>

<hr>

<p><em>I've tried to look at you question from biologically-inspired computer science perspective...</em>, and hope it helps :-)</p>

CC BY-SA 3.0
2016-09-12T08:24:40.887Z
@Christiaan The answer is obviously "No, as far as we know." I've tried to demonstrate why, that's all! :-)
Morty
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2016-09-12T08:43:46.680Z
I'd recommend making that [more] explicit in the answer. Great.+1
AliceD
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2016-09-12T12:36:10.210Z
I'll watch how he rebuts the p.5 as he did in my case.
Little Alien
CC BY-SA 3.0
2016-09-12T17:05:27.273Z
This is such a good answer that I wish I could accept both of them. It's more detail than I really need to answer it, but it doesn't hurt to have the additional knowledge. Thanks.
SQLServerSteve
CC BY-SA 3.0
2016-09-12T06:58:00.677Z
2016-09-12T08:14:17.643Z
2016-09-12T08:14:17.643Z
Morty
<p>As the other answers point out, there is no such thing as a neuron inside another neuron, i.e. a literal nested neuron.</p>

<p>However, from an artificial neural network point of view, there is evidence that a single pyramidal neuron can operate as a hierarchical two layer network thanks to its dendritic tree. To quote the <a href="http://iec-lnc.ens.fr/IMG/Files/GNT/Publications/caze-humphries-gutkin2013-ploscb.pdf" rel="nofollow noreferrer">paper's</a> abstract</p>

<blockquote>
  <p>Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results
  in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of
  computing linearly non-separable functions, such as the exclusive OR.</p>
</blockquote>

<p>This means that the neuron behaves as if it had many nested subunits, which are in fact only branches of the dendritic tree, not real neurons.</p>

<p>I am not an expert in that field but you should look at <a href="http://iec-lnc.ens.fr/group-for-neural-theory/members/faculty-in-alphabetical-order/gutkin-boris/?lang=en" rel="nofollow noreferrer">Boris Gutkin's work</a> who is presently at <a href="https://www.hse.ru/en/org/persons/135913473#sci" rel="nofollow noreferrer">Moscow</a>.</p>

CC BY-SA 3.0
2017-01-09T23:10:21.393Z
From a quick glance at Gutkin's CV, it seems some of his research might be applicable. There might not be evidence for nesting of neurons per se, but that doesn't mean that the brain doesn't leverage hierarhical structures, like calculations over various neurotransmitters and these lower-level dendrtiic spikes. Thanks - I would've never found Gutkin's name without this (I'm not up on the biological side of neural nets).
SQLServerSteve
CC BY-SA 3.0
2017-01-09T20:00:56.043Z
2017-01-09T20:00:56.043Z
lucianopaz
<p>As I understand the idea of parallel computing, the idea of massive parallelism is to reduce the (computer) node sizes while maximizing connections between nodes. If you look at the evolution of supercomputers, initially they were build of very powerful superscalar processors. But, as time goes, they are trying to build of more interconnected lighter processors. That is because communication is the bottleneck in the supercomputing. Powerful nodes compute too much information that they need to stand by to receive/send more to/from their (distant) neighbours. It makes no sense to make very powerful nodes, but, instead to spend the machine space to more cabling. </p>

<p>Neural networks are the ultimate parallelism machines. Their compute nodes, neurons, are as simple as possible. The paradigm, known as connectionism, means that computation is achieved through emergence of results when simple nodes communicate with each other. It consists mostly of cables. In NN the computation is made by thouthands of inter-neuron synapses rather than neuron itself. The idea is that you should connect directly with as much other neurons as possible. You can consider a number of connected neurons as one neuron (subnetwork) but why and, for sure, you do not want to isolate any neurons by a physical wrapper to force them to connect only to each other and prevent possible connections the rest of the neurons. It seems unnecessary, difficult to implement and not only wasteful but adverse limitation.</p>

<p>There is hardly a room for other (sub)neural network in the neuron. You may decide yourself, analyzing the function of neuron how can you implement it. As far as I know, biologically plausible spiking neurons are made of simple capacitor+resistor+something firing a spike if capacitor charge exceeds some threshold. Nothing indicates that you either can fit another neural network into the neuron or need it to simulate it.</p>

<p>Why not to hypothesize existence of humans inside the neurons or smaller heads in human head? A head, made of other heads can give more power.</p>

CC BY-SA 3.0
2016-09-11T16:28:03.557Z
1. "The idea is that you should connect directly with as many other neurons as possible" is not correct for all neural network types. HTMs, convolutionals, Echo State Networks and One-Shot Learning are among the neural net types that deliberately limit connectivity. 2. As I said above, there may indeed be benefit in modeling neurons in a nested way, because this requires only 1 table rather than separate neuron and layer tables, which also reduces the number of relational joins. 3. Nothing in this post answers my question: is there any evidence for biological nested neurons?
SQLServerSteve
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2016-09-11T17:16:14.790Z
I am looking at scientific philosophy currently and it says that the only way to answer your question is 'yes'. 'No' is impossible because we never know for sure. Probably, we have just overlooked them. If you do not know, you cannot answer. This makes answer 'no' impossible.
Little Alien
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2016-09-11T17:27:59.250Z
I asked whether neuroscientists currently possess any evidence for biological nested neurons. It's a very simple yes-or-no thing; either professional neuroscientists have such evidence in hand or they don't. Perhaps they exist but we don't yet have evidence for them, but that kind of speculation wouldn't answer my question.
SQLServerSteve
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2016-09-12T10:13:00.600Z
@SQLServerSteve - Perhaps you could make this last comment here explicit in the question. It would help. The computer-modeling/ANN and neurobiology angles tend to fuse in the question. I tried a shot to at your question. Nice one! Not many questions here at CogSci get three answers.
AliceD
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2016-09-12T12:23:20.753Z
@Christiaan Probably this is a nice question but it seems to beg for ignorance, the argument from ignorance. I do not understand why your other arguments were not downvoted. My example reveals that other arguments are received negaively. Moreover, he has rebutted my 'teleological' argument here in the above comment. I argued that you do not need nested neurons from computational pnt of view, saying that whatever you can with nested, you can do with external. He provided evidence for the opposite. Probably, your ignorance 'answer is yes because I do not know' keeps your answer in good standing.
Little Alien
CC BY-SA 3.0
2016-09-12T13:07:24.183Z
Oh they are pretty much universal, I just don't agree it applies on my answer. But then, my knowledge on philosophy is clearly not as good as yours. Perhaps I'm not understanding you correctly. It's just that I don't like to be called ignorant. Maybe I am, but I don't like to hear it. I'll remove my comments here to prevent answer clutter below your answer. Don't understand the downvote btw. I'll give you a +1 for your efforts.
AliceD
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2016-09-12T13:32:19.053Z
@Christiaan I may mistake, but I read that article as 'argument from ignorance says that something is yes/true because I am unaware of the opposite.' You do not need to be a strong philosopher to see a flaw in it. Yet, I see that this is the kind of argument the OP begs for and you give it. If you give an argument from ignorance, you must give the ignorance in the first place. You cannot apply the argument otherwise. You say 'I do not know (about the opposite)' in your answer. That is the ignorance that you build your answer on. Without that ignorance, you could not give the answer.
Little Alien
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2016-09-12T13:56:09.377Z
@LittleAlien - thanks for spelling this out to me. Gotcha.
AliceD
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2016-09-12T17:02:10.503Z
@Christiaaan - I thought it was explicit in the first sentence, the second sentence of the third paragraph and the title, but I'll add that comment in to make it clearer.
SQLServerSteve
CC BY-SA 3.0
2016-09-12T18:47:15.580Z
@SQLServerSteve - I thought I had it clear, until I saw this answer. It's crystal clear now. I'll edit my answer to remove the statement on the question being unclear once the question's edited.
AliceD
CC BY-SA 3.0
2016-09-12T20:02:17.523Z
@Christiaaan No problem, I changed the question already. Your answer was solid + provides additional detail to anyone who comes across this thread and is curious as to *why* nested neurons are implausible. Thanks.
SQLServerSteve
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2016-09-11T11:09:04.003Z
2016-09-11T17:02:20.287Z
2016-09-11T17:02:20.287Z
Little Alien
<p>Disregarding the feasibility of neurons within neurons <em>in silico</em>, the answer to</p>

<blockquote>
  <p>Is there any evidence for neurons existing within other neurons in humans or other organisms?</p>
</blockquote>

<p>from a practical, biological point of view is <em>no</em>.</p>

<p><strong>The literature point-of-view</strong><br>
The strongest argument against the neuron embedded (I'll deliberately not say "nested" as it has a computational and statistical taste to it) in another neuron is: I've never heard of it. I could cite every single neuro textbook I have <em>not</em> mentioning the phenomenon, but I'll wager that doesn't make too much sense, hence this unreferenced argument.   </p>

<p><strong>The physiological view</strong><br>
Neurons need a <a href="https://courses.washington.edu/conj/membpot/membranepot.htm" rel="nofollow noreferrer">membrane potential</a> to operate, <em>e.g.</em>, to fire <a href="http://www.ncbi.nlm.nih.gov/books/NBK10897/" rel="nofollow noreferrer">action potentials</a>, or change their membrane in a graded manner. That membrane potential is, with perhaps an exception or two, always negative. That is, inside negative, outside neutral. A neuron within a neuron would provide inhospitable living conditions for the former. The latter wouldn't be too happy either. The former will not be able to build up the normal <a href="http://www.evolutionnews.org/2015/03/pumping_for_lif094241.html" rel="nofollow noreferrer">gradient</a> of high Na<sup>+</sup> outside the cell, and a high K<sup>+</sup> inside, as the enveloping neuron would try to do exactly that same thing. The inner cell would thus build up a gradient (push Na<sup>+</sup> out) and the enveloping cell would again transport the Na<sup>+</sup> out. For K<sup>+</sup> the reverse would happen (see <a href="https://highered.mheducation.com/sites/9834092339/student_view0/chapter44/sodium-potassium_exchange_pump.html" rel="nofollow noreferrer">Na<sup>+</sup>,K<sup>+</sup>-ATPase</a> for background). In short, the inner cell would not be able to build up a membrane gradient, while the outer cell would have to deal with a pesky parasite messing up its ionic homeostasis. The inner cell would die, as many processes vital to the cell need a gradient (ion transport of nutrients along the membrane, disposal of garbage) and the outer cell would then finally be a happy neuron, barred the debris in its insides.     </p>

<p><strong>The teleological view</strong><br>
Physically embedded neurons don't make sense. Simply, because neurons exist, basically, to convey information from one neuron to the next. The textbook example of a neuron (Fig. 1) has an input part (the <a href="http://www.columbia.edu/cu/psychology/courses/1010/mangels/neuro/neurocells/neurocells.html" rel="nofollow noreferrer">dendritic region</a>), a space-traversing part to cover distance (the <a href="http://www.biology-pages.info/N/Neurons.html" rel="nofollow noreferrer">axon</a>) and an output part (the <a href="http://www.biology-online.org/dictionary/Axon_terminals" rel="nofollow noreferrer">axon terminal</a>). Any processing occurring within the neuron is done primarily by logic operators such as summation, subtraction and integration (through the dendritic tree collecting information) and gain control (inhibitory or excitatory influences in dendritic region, cell body or axonal part). But in the end, a neuron is designed to get information from A to B. Therefore, a neuron within a neuron doesn't make sense from a neurophysiological vantage point.</p>

<p><a href="https://i.sstatic.net/nOlNp.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/nOlNp.jpg" alt="Motoneuron"></a><br>
<sup>Fig. 1. Textbook example of a motor neuron. The neuron receives information from higher motor areas in the dendritic region and stimulates the muscle using action potentials. Rate code is used to regulate the strength of muscle contraction. source: <a href="http://obes.dvrlists.com/motor-neuron-disease-causes/" rel="nofollow noreferrer">Obes DVR Lists</a>.</sup></p>

<p><sub><strong>Reference</strong><br>
<strong>-</strong> <a href="http://www.ncbi.nlm.nih.gov/books/NBK10897/" rel="nofollow noreferrer">Purves <em>et al</em>. (eds.) <em>Neuroscience</em>, 2<sup>nd</sup> ed. Sunderland (MA): Sinauer Associates (2001). The Ionic Basis of Action Potentials</a></sub></p>

CC BY-SA 3.0
2016-09-12T17:09:40.353Z
This answers my question thoroughly, as does Morty's reply; the extra information in both is not strictly necessary, but is certainly helpful as background and food for thought. I wish I could accept both. I might eventually be able to post on why modeling artificial neural nets with nested neurons can be computationally efficient, whereas it's not in a biological sense. I'd need time to do a lot of benchmarking and all that first. This might be a case where ANNs can benefit from departing from biological plausibility. Thanks.
SQLServerSteve
CC BY-SA 3.0
2016-09-12T18:46:00.623Z
@SQLServerSteve - happy this answer helped. I got into quite an argument with others here :) phewww.... I really like the question - food for thought too :-)
AliceD
CC BY-SA 3.0
2016-09-12T08:10:00.037Z
2016-09-16T19:24:14.600Z
2016-09-16T19:24:14.600Z
AliceD
<p>Is there any evidence for neurons existing within other neurons in humans or other organisms? From what little I've learned about physical neuroscience while learning to build neural nets, I don't see how one neural cell could actually contain another, as a nested structure implies; on the other hand, I figured I'd ask, because neuroscientists have discovered such a wide variety of neuron types, exhibiting all kinds of fascinating behaviors and structures. Furthermore, I suppose it's conceivable that the complex firing of neurotransmitters might be interpreted in a hierarchical way, akin to nested neurons.</p>

<p>The reason I'm asking is entirely practical: I've recently discovered that there may be certain advantages to treating neurons as nested structures when performing data and object modeling of neural nets. I'm interested in finding out if such nesting could be biologically plausible and if so, how I could go about finding more information on the topic. I've performed Internet searches and looked through the threads at StackExchange and can find nary a reference to the term "nested neuron."</p>

<p>Edited to Add (from the comments section, by request): </p>

<p>I'm asking whether neuroscientists currently possess any evidence for biological nested neurons. It's a very simple yes-or-no thing; either professional neuroscientists have such evidence in hand or they don't. </p>

CC BY-SA 3.0
2016-09-10T05:11:43.840Z
As in a neuronal cell *within* another? Doesn't exist.
AliceD
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2016-09-10T05:21:00.033Z
@Christiaan That's what I figured. I've never heard of any biological cell existing within another, except in the case of pathogens invading hosts. But I figured I'd check, given that neuroscientists have discovered so many unexpected, amazing things about brain cells...
SQLServerSteve
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2016-09-10T05:27:53.703Z
I even doubt whether any cell could survive within another for that matter.  Of course it has happened in case of mitochondria. And perhaps some pathogens, but it's a rare phenomenon for sure,  if it exists at all.
AliceD
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2016-09-10T16:18:27.857Z
What nested structures do you think imply the existence of these neurons?
Seanny123
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2016-09-10T16:27:20.623Z
I mean, you mention them in your question, but could you be more specific? I feel like you could rephrase this into a state-space perspective instead of a biological-space perspective, but keep using these nested structures. Maybe that's a valid answer to your question? That you shouldn't try to hard to map ANNs to biology in general?
Seanny123
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2016-09-10T16:28:19.057Z
@Seanny123 I don't think any evidence I'm aware of implies their existence; I'm just checking to make sure that there isn't any. As I said in my question, my motivation for asking it comes from neural nets, where I've learned it may be more efficient to model neurons in a nested way. I'm just wondering if there's a biologically plausible counterpart. I doubt there is, but since there are many types of neural cells I'm not aware of, I figured I should ask the experts before jumping to conclusions.
SQLServerSteve
CC BY-SA 3.0
2016-09-10T16:34:54.143Z
@Seanny123 I don't really think I *need* to map ANNs to biology, but if there are similar nested structures in both, I should read up on them. Academic papers on neural nets usually at least mention corresponding biological neurons (such as pyramidal cells in HTMs and convolutionals). Furthermore, if there are nested biological neurons, perhaps knowing something about them would help me implement nested ANN neurons; for example, I might find potential limitations, or related capabilities I didn't think of etc. I'm just trying to cover my bases before plunging into uncharted territory.
SQLServerSteve
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2016-09-10T18:56:00.753Z
Thanks for explaining that to me. Can you elaborate on what you mean by "nested"?
Seanny123
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2016-09-10T19:22:50.517Z
"Nested" in the context of data and object modeling means that an object can have a parent-child or otherwise hierarchical relationship with other objects of the same type, as if one "contained" the other. For example, you might ordinarily model neural nets using objects called Layers that contain objects called Neurons. Separate database tables might be required for both. As long as there's a lot of overlap in functionality and properties between a Layer and a Neuron, however, you might be able to dispense with Layers and use a single NeuronTable, plus a hierarchical relationship.
SQLServerSteve
CC BY-SA 3.0
2016-09-10T19:26:47.313Z
"Nesting" is easy to pull off in data and object modeling, as long as the objects have similar properties and actions. Sometimes it is more computationally efficient as well. Pulling that off in cellular biology might be a lot harder, since it'd probably mean that one cell would have to contain another, or to be made up of other cells. I don't see how that'd be biologically plausible - but who knows, I know just enough about neuroscience to know that I ought to consult the experts first.
SQLServerSteve
CC BY-SA 3.0
2016-09-11T11:20:53.837Z
You can consider a group of connected neurons as a single neuron. What is the problem?
Little Alien
<neuroscience><theoretical-neuroscience><neural-network><neurophysiology>
"Nested Neurons": Are they biologically plausible?
SQLServerSteve
# "Nested Neurons": Are they biologically plausible?

(Asked by: username_0 on 2016-09-10)

Is there any evidence for neurons existing within other neurons in humans or other organisms? From what little I've learned about physical neuroscience while learning to build neural nets, I don't see how one neural cell could actually contain another, as a nested structure implies; on the other hand, I figured I'd ask, because neuroscientists have discovered such a wide variety of neuron types, exhibiting all kinds of fascinating behaviors and structures. Furthermore, I suppose it's conceivable that the complex firing of neurotransmitters might be interpreted in a hierarchical way, akin to nested neurons.

The reason I'm asking is entirely practical: I've recently discovered that there may be certain advantages to treating neurons as nested structures when performing data and object modeling of neural nets. I'm interested in finding out if such nesting could be biologically plausible and if so, how I could go about finding more information on the topic. I've performed Internet searches and looked through the threads at StackExchange and can find nary a reference to the term "nested neuron."

Edited to Add (from the comments section, by request):

I'm asking whether neuroscientists currently possess any evidence for biological nested neurons. It's a very simple yes-or-no thing; either professional neuroscientists have such evidence in hand or they don't.

**Tags:** neuroscience, theoretical-neuroscience, neural-network, neurophysiology

> **Comments:**
> **username_1**: As in a neuronal cell *within* another? Doesn't exist.  
> **username_0**: @Christiaan That's what I figured. I've never heard of any biological cell existing within another, except in the case of pathogens invading hosts. But I figured I'd check, given that neuroscientists have discovered so many unexpected, amazing things about brain cells...  
> **username_1**: I even doubt whether any cell could survive within another for that matter.  Of course it has happened in case of mitochondria. And perhaps some pathogens, but it's a rare phenomenon for sure,  if it exists at all.  
> **username_2**: What nested structures do you think imply the existence of these neurons?  
> **username_2**: I mean, you mention them in your question, but could you be more specific? I feel like you could rephrase this into a state-space perspective instead of a biological-space perspective, but keep using these nested structures. Maybe that's a valid answer to your question? That you shouldn't try to hard to map ANNs to biology in general?  
> **username_0**: @username_2 I don't think any evidence I'm aware of implies their existence; I'm just checking to make sure that there isn't any. As I said in my question, my motivation for asking it comes from neural nets, where I've learned it may be more efficient to model neurons in a nested way. I'm just wondering if there's a biologically plausible counterpart. I doubt there is, but since there are many types of neural cells I'm not aware of, I figured I should ask the experts before jumping to conclusions.  
> **username_0**: @username_2 I don't really think I *need* to map ANNs to biology, but if there are similar nested structures in both, I should read up on them. Academic papers on neural nets usually at least mention corresponding biological neurons (such as pyramidal cells in HTMs and convolutionals). Furthermore, if there are nested biological neurons, perhaps knowing something about them would help me implement nested ANN neurons; for example, I might find potential limitations, or related capabilities I didn't think of etc. I'm just trying to cover my bases before plunging into uncharted territory.  
> **username_2**: Thanks for explaining that to me. Can you elaborate on what you mean by "nested"?  
> **username_0**: "Nested" in the context of data and object modeling means that an object can have a parent-child or otherwise hierarchical relationship with other objects of the same type, as if one "contained" the other. For example, you might ordinarily model neural nets using objects called Layers that contain objects called Neurons. Separate database tables might be required for both. As long as there's a lot of overlap in functionality and properties between a Layer and a Neuron, however, you might be able to dispense with Layers and use a single NeuronTable, plus a hierarchical relationship.  
> **username_0**: "Nesting" is easy to pull off in data and object modeling, as long as the objects have similar properties and actions. Sometimes it is more computationally efficient as well. Pulling that off in cellular biology might be a lot harder, since it'd probably mean that one cell would have to contain another, or to be made up of other cells. I don't see how that'd be biologically plausible - but who knows, I know just enough about neuroscience to know that I ought to consult the experts first.  
> **username_3**: You can consider a group of connected neurons as a single neuron. What is the problem?  

---

## 4 answers

### Answer by username_1 on 2016-09-12. Score: 5 | Accepted answer

Disregarding the feasibility of neurons within neurons *in silico*, the answer to

> Is there any evidence for neurons existing within other neurons in humans or other organisms?

from a practical, biological point of view is *no*.

**The literature point-of-view**  
The strongest argument against the neuron embedded (I'll deliberately not say "nested" as it has a computational and statistical taste to it) in another neuron is: I've never heard of it. I could cite every single neuro textbook I have *not* mentioning the phenomenon, but I'll wager that doesn't make too much sense, hence this unreferenced argument.

**The physiological view**  
Neurons need a [membrane potential](https://courses.washington.edu/conj/membpot/membranepot.htm) to operate, *e.g.*, to fire [action potentials](http://www.ncbi.nlm.nih.gov/books/NBK10897/), or change their membrane in a graded manner. That membrane potential is, with perhaps an exception or two, always negative. That is, inside negative, outside neutral. A neuron within a neuron would provide inhospitable living conditions for the former. The latter wouldn't be too happy either. The former will not be able to build up the normal [gradient](http://www.evolutionnews.org/2015/03/pumping_for_lif094241.html) of high Na+ outside the cell, and a high K+ inside, as the enveloping neuron would try to do exactly that same thing. The inner cell would thus build up a gradient (push Na+ out) and the enveloping cell would again transport the Na+ out. For K+ the reverse would happen (see [Na+,K+-ATPase](https://highered.mheducation.com/sites/9834092339/student_view0/chapter44/sodium-potassium_exchange_pump.html) for background). In short, the inner cell would not be able to build up a membrane gradient, while the outer cell would have to deal with a pesky parasite messing up its ionic homeostasis. The inner cell would die, as many processes vital to the cell need a gradient (ion transport of nutrients along the membrane, disposal of garbage) and the outer cell would then finally be a happy neuron, barred the debris in its insides.

**The teleological view**  
Physically embedded neurons don't make sense. Simply, because neurons exist, basically, to convey information from one neuron to the next. The textbook example of a neuron (Fig. 1) has an input part (the [dendritic region](http://www.columbia.edu/cu/psychology/courses/1010/mangels/neuro/neurocells/neurocells.html)), a space-traversing part to cover distance (the [axon](http://www.biology-pages.info/N/Neurons.html)) and an output part (the [axon terminal](http://www.biology-online.org/dictionary/Axon_terminals)). Any processing occurring within the neuron is done primarily by logic operators such as summation, subtraction and integration (through the dendritic tree collecting information) and gain control (inhibitory or excitatory influences in dendritic region, cell body or axonal part). But in the end, a neuron is designed to get information from A to B. Therefore, a neuron within a neuron doesn't make sense from a neurophysiological vantage point.

[![Motoneuron](https://i.sstatic.net/nOlNp.jpg)](https://i.sstatic.net/nOlNp.jpg)  
Fig. 1. Textbook example of a motor neuron. The neuron receives information from higher motor areas in the dendritic region and stimulates the muscle using action potentials. Rate code is used to regulate the strength of muscle contraction. source: [Obes DVR Lists](http://obes.dvrlists.com/motor-neuron-disease-causes/).

**Reference**  
**-** [Purves *et al*. (eds.) *Neuroscience*, 2nd ed. Sunderland (MA): Sinauer Associates (2001). The Ionic Basis of Action Potentials](http://www.ncbi.nlm.nih.gov/books/NBK10897/)

> **Comments:**
> **username_0**: This answers my question thoroughly, as does username_4's reply; the extra information in both is not strictly necessary, but is certainly helpful as background and food for thought. I wish I could accept both. I might eventually be able to post on why modeling artificial neural nets with nested neurons can be computationally efficient, whereas it's not in a biological sense. I'd need time to do a lot of benchmarking and all that first. This might be a case where ANNs can benefit from departing from biological plausibility. Thanks.  
> **username_1**: @username_0 - happy this answer helped. I got into quite an argument with others here :) phewww.... I really like the question - food for thought too :-)  

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### Answer by username_4 on 2016-09-12. Score: 3

Here are some notes and ideas, and hopefully an answer to your question:

1. New membranes are produced by a process during divisions as you read in genetics; sometime calls embryogenesis.It is just the divisions of cytoplasm. The layer than separates nuclides are suggested to polarize toward nuclides inside a single cytoplasmic they all are. So, as it might be guessed by the definition of the word "cell", normally in final form of a tissue there will be no cell inside a cell (or its cytoplasm). They will be split into two cells, as always in science this is what "usually" happens ([This article provides some backgrounds on how this mechanism works](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734032/)).
2. There are however vesicles, simply bags of some chemical stuff (e.g. neurotransmitters). They fuse into the cell membrane at its terminals and some N.t. would be released. They are not living cells though, but their outside layer is similar to the ones in a real cell, made in form of phospholipid bilayer.
3. Rather than neurons, there are some more cells in the nervous system, called *Glias*, even more than neurons. Their main role seems to support neurons and synapses. You may think of them as supporting cells, joint with a neuron or a synapse helps them being activated or deactivated. There are recent studies, investigating the role of Glias in the brain.
4. Neurons (for example in cortex, which is the most influential area of the brain interested computer scientists) do not perform activations alone. But they form a network; in fact networks of networks. In cortex, they together form columns, super columns, and in human around 6 layers. They usually study groups, columns or bigger forms of interconnected neurons, except in invasive single neuron imaging techniques. So neurons have been in some sense studied doing computations *"on average"*, but not alone. Even the activation in neurons (action potentials) are generated at a place called axon hillock, that does not rationalize having two cells can simple generate two independent signals and then merge them together; this can be done easier at synapse level since nature is just a sloth and select best random outcomes.
5. Synapses are where information is stored in artificial and natural neural networks. So even a cell within a cell (connected), does not provide further improvements (like new trends of providing computing power necessary for deep nets). By cell-within-cell in ANNs you just add another connection and possibly a new layer. You can do the same by defining new layer as in deep networks.
6. Action potentials to send signals of activation forward in cell are performed and developed in places outside the cell body (soma). There are usually occurs in dendrites and mostly in axons. So biologically speaking. A cell body within a cell body does not help or intervenes in computations or for communication. If it happens in real world, there are just mutations, or wrongly or in-lab developed ones (It is possible, right?); similarly other solutions perform same mechanism, for example like a single neuron, or two connected neurons normally through a single synapse.
7. Though the idea could be investigated by self-similar systems (fractals), dimension of self-similarity measures, dynamic systems, and in general chaos theory. In neural system this fractal dimension is an integer at cell level (a single cell). You can however study how this self-similarity works for artificial neural nets :-).

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*I've tried to look at you question from biologically-inspired computer science perspective...*, and hope it helps :-)

> **Comments:**
> **username_4**: @Christiaan The answer is obviously "No, as far as we know." I've tried to demonstrate why, that's all! :-)  
> **username_1**: I'd recommend making that [more] explicit in the answer. Great.+1  
> **username_3**: I'll watch how he rebuts the p.5 as he did in my case.  
> **username_0**: This is such a good answer that I wish I could accept both of them. It's more detail than I really need to answer it, but it doesn't hurt to have the additional knowledge. Thanks.  

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### Answer by username_5 on 2017-01-09. Score: 2

As the other answers point out, there is no such thing as a neuron inside another neuron, i.e. a literal nested neuron.

However, from an artificial neural network point of view, there is evidence that a single pyramidal neuron can operate as a hierarchical two layer network thanks to its dendritic tree. To quote the [paper's](http://iec-lnc.ens.fr/IMG/Files/GNT/Publications/caze-humphries-gutkin2013-ploscb.pdf) abstract

> Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results
> in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of
> computing linearly non-separable functions, such as the exclusive OR.

This means that the neuron behaves as if it had many nested subunits, which are in fact only branches of the dendritic tree, not real neurons.

I am not an expert in that field but you should look at [Boris Gutkin's work](http://iec-lnc.ens.fr/group-for-neural-theory/members/faculty-in-alphabetical-order/gutkin-boris/?lang=en) who is presently at [Moscow](https://www.hse.ru/en/org/persons/135913473#sci).

> **Comments:**
> **username_0**: From a quick glance at Gutkin's CV, it seems some of his research might be applicable. There might not be evidence for nesting of neurons per se, but that doesn't mean that the brain doesn't leverage hierarhical structures, like calculations over various neurotransmitters and these lower-level dendrtiic spikes. Thanks - I would've never found Gutkin's name without this (I'm not up on the biological side of neural nets).  

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### Answer by username_3 on 2016-09-11. Score: 0

As I understand the idea of parallel computing, the idea of massive parallelism is to reduce the (computer) node sizes while maximizing connections between nodes. If you look at the evolution of supercomputers, initially they were build of very powerful superscalar processors. But, as time goes, they are trying to build of more interconnected lighter processors. That is because communication is the bottleneck in the supercomputing. Powerful nodes compute too much information that they need to stand by to receive/send more to/from their (distant) neighbours. It makes no sense to make very powerful nodes, but, instead to spend the machine space to more cabling.

Neural networks are the ultimate parallelism machines. Their compute nodes, neurons, are as simple as possible. The paradigm, known as connectionism, means that computation is achieved through emergence of results when simple nodes communicate with each other. It consists mostly of cables. In NN the computation is made by thouthands of inter-neuron synapses rather than neuron itself. The idea is that you should connect directly with as much other neurons as possible. You can consider a number of connected neurons as one neuron (subnetwork) but why and, for sure, you do not want to isolate any neurons by a physical wrapper to force them to connect only to each other and prevent possible connections the rest of the neurons. It seems unnecessary, difficult to implement and not only wasteful but adverse limitation.

There is hardly a room for other (sub)neural network in the neuron. You may decide yourself, analyzing the function of neuron how can you implement it. As far as I know, biologically plausible spiking neurons are made of simple capacitor+resistor+something firing a spike if capacitor charge exceeds some threshold. Nothing indicates that you either can fit another neural network into the neuron or need it to simulate it.

Why not to hypothesize existence of humans inside the neurons or smaller heads in human head? A head, made of other heads can give more power.

> **Comments:**
> **username_0**: 1. "The idea is that you should connect directly with as many other neurons as possible" is not correct for all neural network types. HTMs, convolutionals, Echo State Networks and One-Shot Learning are among the neural net types that deliberately limit connectivity. 2. As I said above, there may indeed be benefit in modeling neurons in a nested way, because this requires only 1 table rather than separate neuron and layer tables, which also reduces the number of relational joins. 3. Nothing in this post answers my question: is there any evidence for biological nested neurons?  
> **username_3**: I am looking at scientific philosophy currently and it says that the only way to answer your question is 'yes'. 'No' is impossible because we never know for sure. Probably, we have just overlooked them. If you do not know, you cannot answer. This makes answer 'no' impossible.  
> **username_0**: I asked whether neuroscientists currently possess any evidence for biological nested neurons. It's a very simple yes-or-no thing; either professional neuroscientists have such evidence in hand or they don't. Perhaps they exist but we don't yet have evidence for them, but that kind of speculation wouldn't answer my question.  
> **username_1**: @username_0 - Perhaps you could make this last comment here explicit in the question. It would help. The computer-modeling/ANN and neurobiology angles tend to fuse in the question. I tried a shot to at your question. Nice one! Not many questions here at CogSci get three answers.  
> **username_3**: @Christiaan Probably this is a nice question but it seems to beg for ignorance, the argument from ignorance. I do not understand why your other arguments were not downvoted. My example reveals that other arguments are received negaively. Moreover, he has rebutted my 'teleological' argument here in the above comment. I argued that you do not need nested neurons from computational pnt of view, saying that whatever you can with nested, you can do with external. He provided evidence for the opposite. Probably, your ignorance 'answer is yes because I do not know' keeps your answer in good standing.  
> **username_1**: Oh they are pretty much universal, I just don't agree it applies on my answer. But then, my knowledge on philosophy is clearly not as good as yours. Perhaps I'm not understanding you correctly. It's just that I don't like to be called ignorant. Maybe I am, but I don't like to hear it. I'll remove my comments here to prevent answer clutter below your answer. Don't understand the downvote btw. I'll give you a +1 for your efforts.  
> **username_3**: @Christiaan I may mistake, but I read that article as 'argument from ignorance says that something is yes/true because I am unaware of the opposite.' You do not need to be a strong philosopher to see a flaw in it. Yet, I see that this is the kind of argument the OP begs for and you give it. If you give an argument from ignorance, you must give the ignorance in the first place. You cannot apply the argument otherwise. You say 'I do not know (about the opposite)' in your answer. That is the ignorance that you build your answer on. Without that ignorance, you could not give the answer.  
> **username_1**: @LittleAlien - thanks for spelling this out to me. Gotcha.  
> **username_0**: @Christiaaan - I thought it was explicit in the first sentence, the second sentence of the third paragraph and the title, but I'll add that comment in to make it clearer.  
> **username_1**: @username_0 - I thought I had it clear, until I saw this answer. It's crystal clear now. I'll edit my answer to remove the statement on the question being unclear once the question's edited.  
> **username_0**: @Christiaaan No problem, I changed the question already. Your answer was solid + provides additional detail to anyone who comes across this thread and is curious as to *why* nested neurons are implausible. Thanks.  

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