Coming back to yesterday's post - does the muscle activation --> limb movement mapping really change in regular life? I mentioned it because it certainly does in these classic force field reaching experiments - but when do you ever encounter a force field in real life? By and large I think this mapping stays relatively constant. Except maybe when you are lifting weights. I haven't been able to think of another situation where it doesn't.

So my current outlook is that the motor system has hardwired into it the forward mapping from muscle activation to limb movement. And the reversed mapping as well, possibly (likely, but maybe not necessary?). It's key job then is to figure out what limb movements to make. Given some goal, infer what sequence of movements is needed to achieve it. As I mentioned in the previous post, this inference will depend on a lot of factors about your environment. A given limb movement will have very different consequences in different environments. More on this another time.

Recently I've been thinking about computation in the nullspace. There is this big idea going around in the motor cortex literature since around 2010ish sprung by the excellent work of Krishna Shenoy and Mark Churchland. The idea is that we should think about motor cortex as a dynamical system, whereby different initial conditions lead to different trajectories in phase space that translate to different limb movements. The prediction then being that in a delayed reaching task, the preparatory activity (in dorsal premotor cortex) that occurs during the delay period (a fixed length time interval between target onset and reaching movement onset at a go cue) serves to put the system in the right initial conditions to generate the appropriate reach. But this raises a suddenly obvious question: how does this dorsal premotor cortex preparatory activity not generate movements? Their answer: it lives in the nullspace of motor cortex activity, meaning that it doesn't affect motor cortex activity. E.g. if a motor cortical neuron has 2 presynaptic inputs with weights +1 and -1, all activity patterns in the nullspace are such that these two neurons are equally active (so the post-synaptic neuron is silent). Or something along these lines - we don't really have a mechanistic circuit model to explain these phenomena yet....

Could nullspace activity be useful for other tasks computations? One recent study up on bioarxiv by Juan Gallego and colleages shows that in a force field task exactly like that I described in the previous post, the only difference in preparatory activity between early (not learned, so bad reaching performance) and late (learned, with stable good reaching) force field trials within a session was activity in the nullspace. Makes sense from the above point of view of using the nullspace to set up the right initial conditions - presumably you need different initial conditions when there is a force field. But nullspace activity is happening all the time, concurrent with potent space activity (the opposite of the nullspace - activity that does affect downstream areas). How can we use this for efficient computation? What are other situations where it could be useful?