Jia, H., Rochefort, N., Chen, X., & Konnerth, A. (2010). Dendritic organization of sensory input to cortical neurons in vivo Nature, 464 (7293), 1307-1312 DOI: 10.1038/nature08947
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.”