Systems Neuroscience Highlights: May 2017

It was a great month for systems neuroscience, and the following articles stood out as pushing things forward in unexpected (to me) and interesting ways.

Sensory Coding
Tien et al -- Homeostatic Plasticity Shapes Cell-Type-Specific Wiring in the Retina -- Neuron  [Pubmed] This is an amazing paper. 
    They generated a line of mice that was lacking a certain type of retinal bipolar cell (the B6 cell). The B6 cell is typically the main input to the ONα retinal ganglion cell. Instead of being completely wrecked in this line of  mice, the ONα RGCs actually maintained the same response profiles seen in wild type animals. This was because other types of bipolar cells compensated for the loss of the B6 cell in the circuit. Hence, it seems that compensatory plasticity mechanisms at play in the retina served to rewire the inputs to this class of RGC to maintain the same type of output to the brain. 
     I always thought homeostatic plasticity research was very cool, but more about neurons maintaining firing rates by changing concentrations/distributions of ion channels and other relatively vanilla properties confined to single units. If there are homeostatic mechanisms at play in systems, with homeostatic sculpting at the circuit level? This seems to be taking things in an entirely new direction.
 
Motor Control
Makino et al -- Transformation of Cortex-wide Emergent Properties during Motor Learning -- Neuron [Pubmed] 
     The authors looked at calcium dynamics in neurons across supragranular layers of cortex as mice learned a motor task (a simple lever-pressing task). The sequence of activation among different motor areas became more compressed in time as they learned the task, and response variability decreased as well. Interestingly, area M2, an infrequently studied motor region in rodents, became a key hub in the motor control network once animals learned the task: the movement-predicting signal in M2 started earlier as they learned, better predicted the activity of other motor areas, and inactivating M2 significantly impaired performance in the task.
     The reason I like this paper is that it isn't just another "Look at all the calcium imaging we did!" paper. It has substantive new results that seems to push our picture of motor control in cool new directions. Also, it is an interesting complement to the recent result from Kawai et al (from Ölveczky's lab) showing that performing a simple overlearned motor sequence does not require M1/M2 (Motor cortex is required for learning but not for executing a motor skill).  While Makino et al do not discuss the Kawai paper, it would be interesting to hear their thoughts on it.

Update added 6/7/17:  I got a helpful comment from an author of the Makino et al. paper who pointed out that in Kawai et al, they didn't just remove M1, but M1 and M2. I missed this in my first reading of Kawai et al, and updated the present post accordingly. Further, he suggested that the task in the current paper requires finer-grained control of the fingers, while Kawai's task used more coarse-grained forelimb movement that are likely controlled subcortically. It is fairly well-known that dexterous digit control in rodents requires the cortex, as acknowledged by Kawai et al.. Finally, these are issues we will be hearing more about from Komiyama's group, so stay tuned!