Yes. There is an abundance of literature to support differential information, and function. The details of the information depends on the type of neuron.

Thanks, Larry!

Are there any overviews or summaries of this?

In addition to the lead below, I would recommend looking at reviews in dendritic mRNA content and signling, reviews in axonal mRNA content and signaling, and electrical properties of axons and dendrites. These studies cross multiple sub-fields within neuroscience.

You might also try looking up the following reference in SCOPUS, 40 publications cite this paper, also read who they have cited. Good luck.

Fritschy J.-M., Panzanelli P., Kralic J.E., Vogt K.E., Sassoe-Pognetto M.

Differential dependence of axo-dendritic and axo-somatic GABAergic synapses on GABAA receptors containing the α1 subunit in Purkinje cells.

(2006) Journal of Neuroscience, 26 (12) , pp. 3245-3255.

Hi Lev,

You may also be interested to check out the work on target-cell dependent plasticity by Chris McBain and colleagues. In several studies they have shown that hippocampal mossy fiber axon terminals from a single dentate gyrus granule cell can exhibit unique properties (for both short and long term plasticity) depending on the target cell type that it forms synaptic connections with (either interneurons or principle neurons). This series of studies is among the best evidence I have seen for this topic. Here is a good review on the topic that will cite all of the primary papers:

Pelkey KA & McBain CJ (2007). Differential regulation at

functionally divergent release sites along a common axon.

Curr Opin Neurobiol17, 366–373.

Jonathan, thank you very much! It is interesting.

But can you tell me, why does the language used in the paper (and most other neurobiological and related) papers has to be so obscure? ;--)

I know what you mean...it is just a specialized topic and a lot of jargon to get around. I guess we get used to it after a long time. But I am sure you can appreciate it is the same way I feel about reading about computational neurosciences!

I agree, but still I don't believe that a *fundamental* progress in both these fields can be made with such a narrow focus. The 'mental' cannot be properly approached in such a way.

Larry and others,

"I would recommend looking at reviews in dendritic mRNA content and signling, reviews in axonal mRNA content and signaling, and electrical properties of axons and dendrites. "

May I ask you to refer to at least one or two latest reviews in the areas you mentioned above and related to the question?

If not, may be you know some names.

These papers should guide you in your quest. Search forward and backward from them and you'll get more than you were looking for. Best wishes, Larry

Localization and translation of mRNA in dentrites and axons

Christy Job1 & James Eberwine1

Nature Reviews Neuroscience 2, 889-898 (December 2001) | doi:10.1038/35104069

Dendrites differ from axons in patterns of microtubule stability and

polymerization during development

Katherine M Kollins†3, Robert L Bell†1, Matthew Butts1 and

Ginger S Withers*1,2

Neural Dev. 2009; 4: 26.

Projection domains of MAP2 and tau determine spacings between microtubules in dendrites and axons

J. Chen*, Y. Kanai, N. J. Cowan† & N. Hirokawa*‡

Nature 360, 674 - 677 (17 December 1992); doi:10.1038/360674a0

Branching out: mechanisms of dendritic arborization

Yuh-Nung Jan & Lily Yeh Jan

Nature Reviews Neuroscience 11, 316-328 (May 2010)

Microtubule assembly, organization and dynamics in axons and dendrites

Cecilia Conde1 & Alfredo Cáceres1

Nature Reviews Neuroscience 10, 319-332 (May 2009) | doi:10.1038/nrn2631

Thank you Larry!

Different dendrites on the same neuron would carry different qualitative information if their synaptic inputs were from axons that are stimulated by different qualitative sources. Different axons on the same neuron might carry different qualitative information if the different axons were influenced by different qualitative sources.

Thank you Arnold, but can you clarify, please, about: "axons that are stimulated by different qualitative sources."

You're welcome.

With respect to your question, "why does the language used in the paper (and most other neurobiological and related) papers has to be so obscure?"

As a biologist working in engineering, applying engineering back to biology, I would say that the apparent obscurity is an effort to achieve precise terms/descriptions that are meaningful for the discipline to achieve and communicate the dept of the field. Unfortunately, it will appear obscure to outsiders. For example, Young's modulus vs stiffness, the former is quantitative but obscure to the generalist, especially when more precise or contextual specifications are defined (e.g. tensile modulus, elastic modulus, tangent modulus, bulk modulus, shear modulus). The same thing happens in biology.

What becomes complicated in biology, is when obscure terms are made that appear to make no sense (Bride of sevenless (a.k.a. Boss)). Herein, you need a decoder ring to decipher the jargon: http://www.curioustaxonomy.net/gene/fly.html

Lev, when we talk about qualitative information in ordinary discourse we have to acknowledge that we are dealing with a very fuzzy concept. When you ask about qualitative information carried in axons, I don't think that you can separate this question from the question of the kind of neuronal *system* in which the axon serves as a component.

So here is the way I understand information:

My definition: _Information is any property of any object, event, or situation that can be detected, classified, measured, or described in any way.

1. The existence of information implies the existence of a complex
 physical system consisting of (a) a source with some kind of structured
content (S), (b) a mechanism that systematically encodes the structure of
 S, (c) a channel that selectively directs the encoding of S, (d) a
 mechanism that selectively receives and decodes the encoding of S.

2. A distinction should be drawn between latent information and what 
might be called kinetic information. All structured physical objects
 contain latent information. This is as true for undetected distant
 galaxies as it is for the magnetic pattern on a hard disc or the ink
marks on the page of a book. Without an effective encoder, channel, and
 decoder, latent information never becomes kinetic information. Kinetic 
information is important because it enables systematic responses with 
respect to the source (S) or to what S signifies. None of this implies
 consciousness.

3. A distinction should be drawn between kinetic information and
 manifest information. Manifest information is what is contained in our
 phenomenal experience. It is conceivable that some state-of-the-art 
photo—>digital translation system could output equivalent kinetic 
information on reading English and Russian versions of War and Peace, 
but a Russian printing of the book provides me no manifest information
 about the story, while an English version of the book allows me to
 experience the story. The "explanatory gap" is in the causal connection
 between kinetic information and manifest information._

On the basis of this definition, we are talking about kinetic information of a qualitative kind within a biological system. In such a system, a qualitative source must be an event of a kind that can change the response/output of the system with respect to the source. In the case of an axonal branch, for example, if the system's response depended on patterns of spike frequency and there were a different ionic environment/stimulus on a particular branch of a single neuron, this axonal branch, because of its distinct ionic environment, would differentially influence the system's response. If the ionic change on the axonal branch were systematically related to a sensory (qualitative) source then we could say that the branch in question is carrying qualitative information that is different than the other branches of the neuron.

Arnold, may I suggest what I meant by "qualitatively different" in the original question?

I meant something very specific. Let's take an extremely simplified example where we have only three dendrites coming to a neuron.

In the original question, I asked if it is true that each of these three dendrites is 'configured' in a specific manner which makes it similar to some other dendrites in other neurons.

In other words, is it true that each dendrite in the brain can be 'colored' in a particular color reflecting the kind of 'information' it carries? Thus, out of three dendrites we might have two of a particular 'color', or we might have all three of different 'colors'.

Lev: "In other words, is it true that each dendrite in the brain can be 'colored' in a particular color reflecting the kind of 'information' it carries?"

The answer is yes. However, you should be aware that when we consider N dendritic branches in a single neuron, each branch might have a different "color" but the whole cell is a leaky integrator that mixes all of its dendritic information to provide its own output "color". This justifies our modeling multi-branched dendrites as a single dendritic shaft.

It might help to take a look at The Cognitive Brain, Ch. 2, "Neuronal Properties" and Ch. 3, "Learning, Imagery, Tokens, and Types: The Synaptic Matrix". You can read these chapters here:

http://people.umass.edu/trehub/thecognitivebrain/chapter2.pdf

http://people.umass.edu/trehub/thecognitivebrain/chapter3.pdf

"The answer is yes. However, you should be aware that when we consider N dendritic branches in a single neuron, each branch might have a different "color" but the whole cell is a leaky integrator that mixes all of its dendritic information to provide its own output "color". This justifies our modeling multi-branched dendrites as a single dendritic shaft."

How strong is the justification for this "modeling multi-branched dendrites as a single dendritic shaft"?

This is an issue for the scientific community to decide. In my opinion the justification is very strong. The synaptic matrix model for learning and imagery treats N dendritic branches as a single shaft and performs successfully in computer simulation tests. See for example "Self-Directed Learning in a Complex Environment", and "Sparse Coding of Faces in a Neuronal Model ..." in these links:

http://www.people.umass.edu/trehub/thecognitivebrain/chapter12.pdf

http://people.umass.edu/trehub/sparscodtre.pdf