Sunday, August 06, 2017

Systems Neuroscience Highlights: June and July 2017

There were lots of great articles the last couple of months, in particular a series of articles on the fly's representation of it's position in space that seem to be coming together nicely.

Sensory Coding

Singla et al.. A cerebellum-like circuit in the auditory system cancels responses to self-generated sounds -- Nature Neuroscience [Pubmed] It is well-known that the brain is able to factor out which sensory inputs are generated by an animal's own behavior, and which are generated by events in the world. How brains do this is still an extremely active research are.  In this paper, the authors report a class of cells in the dorsal cochlear nucleus (DCN) of the brain stem that respond robustly to externally generated auditory cues, but not to self-generated auditory cues in mice (in particular, the sounds generated when they lick). Amazingly, when researchers artificially pipe in sounds in response to licking behavior, these DCN neurons eventually supress responses to such sounds, as if the brain were starting to treat them as being generated by the animal. 
      The authors seem to have found a beautiful model for sensory cancellation effects, one that is very much like that seen in the mormyrid electric fish (as discussed by Abbott's group recently here). It will be interesting to see how similar the principles are in these different systems as folks dig under the hood.

Green et al.. A neural circuit architecture for angular integration in Drosophila -- Nature [Pubmed]. This is actually one of three that papers came out recently, building on a landmark paper from 2015, showing that the fruit fly contains a circular structure (the ellipsoid body, or EB), that acts as an internal compass, and contains a set of neurons that lights up at a different position on the ellipse depending on the angular position of the fly in space. In this paper, they test a potential circuit mechanism about how such an internal compass might be implemented. Namely, there is a second structure, the protocerebral bridge (PB), that is reciprocally connected to the compass, but slightly shifted around the circular axis, and which is preferentially activated by turning behavior. So, for instanced, when the fly turns right, the PB neurons (labeled P-EN in the video) will project to the compass neurons (labeled E-PG in the video), but to a region just a little bit ahead of the presently active spot on the EB. Excitatory interactions will drag the spot around to the appropriate location. This paper is nice because they have tests of sufficiency (activating PB glomeruli causes a shift in the compass location), and necessity (inactivating PB disrupts the compass).
    There is a lot more to the paper: the anatomy actually gets fairly complicated, and I have purposely suppressed tons of details and nomenclature (e.g., if you start to get lost in the anatomy, see this paper, or this one).
     So far, many of the fly-compass researchers seem to be doing relatively coarse-grained calcium imaging (e.g., this glomerulus lights up, and that one doesn't). The work is excellent, as they are pulling information from particular cell types to extract specific hypotheses about circuit mechanisms. Ultimately, though, you still end up with black-boxology (though with fine-grained boxes). The real power will come when they start triangulating their ridiculously powerful genetic toolkit with finger-grained electrophysiology and anatomy to really crack the circuits mechanisms at single-cell resolution. My guess is this is their aim.
    One question I have after reading this and other papers is what is the point of this compass? It seems to not effect the animal very much if you perturb it using optogenetics (see Turner-Evans paper below). In this paper they talk about "occasional" changes in behavior when they disrupt things by stimulating PB, but don't explore or quantify this effect. I am not sure what happens, to spatial navigation, if you ablate EB. Is it like the hippocampus, in that it is more involved in memory than online spatial navigation, even though there is a beautiful spatial representation contained there?

    Note there were two other papers on the fly's internal compass in the past cycle, that I'll just mention briefly:
  • Turner-Evans et al.. Angular velocity integration in a fly heading circuit -- Elife [Pubmed]. Testing the same phase-dragging model as Green et al., with similar results and some nice patch-clamp data from PB. Video is from this paper.
  • Kim et al.. Ring attractor dynamics in the Drosophila central brain -- Science  [Pubmed] Looking at the compass in animals in flight, instead of just on the floating  ball.


Motor Control
Park et al.. Moving slowly is hard for humans: limitations of dynamic primitives -- J. Neurophysiology [Pubmed]. While you will often hear of the speed-accuracy tradeoff (that is, the faster you try to do something, the more likely you are to make a mistake), does this mean when you move really slowly you get really accurate? People don't' typically study the lower extremes of the speed-accuracy tradeoff. In this study they did just that. They had human subjects move their hands back and forth at different speeds, sometimes extremely slowly, so slowly that they could no longer maintain a smooth oscillatory behavior, but started to halt and stop and start again, as if they were shifting from a continuous to a discrete behavioral strategy. 
     While this paper doesn't have any neuronal data, it is significant and fun because of its attempt to infer underlying mechanisms of motor control strategies from a clever and creative extension of simple behavioral techniques. A colleague of mine pointed out that it would be interesting to see how much improvement we would see with training on this task. My reaction is that, even if subjects could ultimately move smoothly with 500 hours of training (dear God please don't do that to your poor undergrads), it would still be significant if without such training, we naturally switched from a continuous to an intermittent control strategy in low-velocity regimes.