summary:
Alain Destexhe. Dendrites Do It in Sequences (24 September 2010)
Science 329 (5999), 1611.

article:

Tiago Branco, Beverley A. Clark, and Michael Häusser. Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons (24 September 2010)
Science 329 (5999), 1671.

Consider a a cortical neuron in V1, layer 2/3, whose output shows sharp orientation tuning. What are the orientation tunings of the most important inputs to that neuron? What is the spatial distribution of these inputs in the neuron’s dendritic tree?

Here’s three possibilities. (1) You might expect the neuron to collect inputs which are broadly tuned for that same orientation (the “weak-bias model”). (2) Or, you might expect that the neuron as a whole collects inputs with various tunings, but that each dendritic branches would tend to collect inputs with a certain orientation. (3) Or, neither of these could be the case; maybe the inputs just take all sorts of orientations, randomly distributed among the dendritic tree. Here a picture of these possibilities from the News and Views:

Jia, Rochefort, Chen, and Konnerth analyzed the orientation tuning of such neurons as well as the orientation tuning of the calcium dynamics within the neuron’s dendritic tree. Their results support the third option (inputs with heterogenous tuning, spatially mixed).

While hyperpolarizing the cell, they found “calcium hotspots” in the dendritic tree, that is, places where there was a noticeable, localized calcium signal in response to stimulation. They then analyzed the orientation tuning of these hotspots. Figure 3b shows three hotspots and their calcium response to various drifting gratings (oriented visual stimuli):

Figure 3c shows what the orientation tuning was for all of the hotspots in one neuron:

The main results are that the orientation tuning of the hotspots is heterogeneous (all sorts of different tunings are found), and that there is no discernible spatial pattern to where the differently tuned hotspots are located within the dendritic tree.

Furthermore, they compared the histogram of the orientation tuning of hotspots between sharply tuned neurons and broadly tuned neurons, and found that they were similar, supporting the hypothesis that whatever it is that makes some neurons have sharper orientation than others tuning in their output, the cause is something other than having sharper orientation tuning in their inputs. Fig. 4d (OSI stands for “orientation selectivity index”):

Here’s an excerpt from the Nature editor’s summary: “Whether…. tuning is already encoded in a neuron’s dendritic inputs or whether the neuron itself computes its selective response has been unclear….They discover that, while all neurons receive distributed input signals coding for multiple stimulus orientations, each neuron makes its own ‘decision’ as to the orientation preference of its firing output.”

Some cautionary notes: (A} the News and Views makes it sound as if this study established linear dendritic summation. As far as I can tell, the study didn’t test that directly. (B) above, I said that possiblity 3 is that the inputs are “randomly distributed”; in the study, however, although the distribution SEEMED random, it’s possible that it is just organized in some complicated way that made it look random. (C) I could be wrong about this, but as far as I can tell, there’s no guarantee that the calcium hotspots are the “most important” synaptic inputs; they might be ones which just happen to have a high density of calcium channels (D) they are only looking in about four planes of focus and getting about 13 hotspots per neuron, so this is only a small proportion of all of the synapses (E) even if the set of strong synapses showed heterogeneous tuning, there could be many weak synapses that all have tuning that matches the output tuning. (F) I defined the hotspots as “noticeable, localized calcium signal in response to stimulation”, but this is pretty subjective. The article does not exactly specify an algorithm which was used to pick out the hotspots from within their imaging data. All the methods has to say about it is, “Transient changes in Ca2+ fluorescence (?f/f) were systematically examined by an adaptive algorithm, which involved small regions of interest (ROIs) of 3?×?4?µm, noise filtering and pattern matching.”

A very cool article on a new open source, online system to crowd source the assemblage of data in neuroscience from the Voice of San Diego.  From the article:

Traditionally, the study of the brain was organized somewhat like an archipelago. Neuroscientists would inhabit their own island or peninsula of the brain, and see little reason to venture elsewhere.

Molecular neuroscientists, who study how DNA and RNA function in the brain, didn’t share their work with cognitive specialists who study how psychological and cognitive functions are produced by the brain, for example.

But there has been an awakening to the idea that brains of humans and mammals should be studied like the complex, and interrelated systems that they are. Neuroscientists realized that they had to start collaborating across disciplines and sharing their data if they wanted to make advances in their own field.

[...]

Ellisman and his UCSD colleagues have devised a solution: crowdsource a brain. And this week they unveiled their years-long project — the Whole Brain Catalog — at the annual convention of the Society for Neuroscience, the largest gathering of brain experts in the world.

You can also see an impressive  artists rendition of the Whole Brain Catalog on YouTube.

UPDATE 10/27: Looks like Voice of San Diego scooped the New York Times, who just posted on this topic in today’s bits blog.

Full disclosure: I am intimately involved with this project.

Watch it online at the TED website.

Two approaches

Broadly, there are two approaches to synaptic tagging: 1) mRNA is distributed widely and translated locally at tagged locations; 2) protein products are distributed widely in the bodies of dendrites but only contact/off-load at tagged synaptic specializations. This News & Views gives a nice overview of the two papers, which find approach 1) in Aplysia cultures with sensorin mRNA and approach 2) in rat hippocampal neurons with Vesl-1S/Homer-1a protein. It amazes me that both were found pretty much simultaneously, but that might have more to do with the use of the photoconvertible Dendra2 protein than anything else.

With both approaches, we still don’t know why mRNA/protein is directed to a certain location. That is, we can visualize synaptic tagging but we don’t know what is the tag, its ontogeny, or the mechanism of tagging. But that might not be so important to understanding more about neural function. These new tools might allow us to image plasticity at many synapses at once, perhaps even in vivo. But before that, more work is needed… does the optical signal (from the Dendra fusion protein) correlate with degree of potentiation? Can we detect plasticity in the opposite direction, ie. synaptic depression, through another tag?  (As a sidenote to approach 1), the use of 5′ and 3′ UTRs as a sort of molecular zipcode is also intriguing.)

The subcellular organization of neocortical excitatory connections : Article : Nature.

They used ChR2 (with TTX and 4-AP to block action potentials) to find where on the dendritic tree particular inputs synapsed onto L3 and L5 cells and to measure the strength of those inputs. ChR2 depolarizes the input axon locally (60um spot diameter) at points of (potential) axodendritic contact. If you’ve heard the term “potential synapse” before, then think of this technique as a way of checking potential synapses and seeing if there really is an actual synapse there.

The technique allowed them to map on a L3 barrel cortex pyramidal cell where different thalamic inputs (VPm, POm) and cortical inputs (M1, barrel L2/3, barrel L4):

sCRACM stands for subcellular ChR2-assisted circuit mapping.

The paper can be found Here.

Very interesting work. Modulation of the somatic potential seems to influence the EPSP, as measured by paired patch recordings of two layer 5 cells in cortical slice. Somatic depolarization from resting potential to near threshold results in an increase in evoked EPSPs.

In synaptic physiology, we often make a point of distinguishing intrinsic changes (eg. membrane potential) from synaptic conductance changes. Now it looks like the line between those might be a bit blurry!

Here’s a N&V by Eve Marder too.