Monday, 14 June 2010

How the brain controls the non-body

ResearchBlogging.orgWhen asked about my research area by people in the pub (this happens probably more than it should, most likely due to the disproportionate amount of time I spend there) I usually reply that I work on motor control, or ‘how the brain controls the body’. Today’s paper by Ganguly and colleagues looks at how the brain can control things without a body. There are some very cool results here.

The field of neuroprosthetics, or the control of prosthetic devices by brain activity, is a rapidly emerging one. It’s been previously shown that monkeys and humans can learn to control an on-screen cursor through the power of the mind alone, as their brain activity is either directly or indirectly measured and fed through a decoder that transforms the activity into cursor movement. One question we don’t yet know the answer to is: what is the nature of the neural activity that is decoded?

Ganguly et al. set out to answer this question. They trained two monkeys on a reach-to-targets task while recording the activity of a number of neurons. They found that a small group of these neurons were very stable across several days to weeks – that is, each individual neuron fired in the same way on day 19 as it did on day 1, for example. The researchers then used these stable neurons as inputs to their decoder. The figure below (Figure 2 in the paper) shows how the monkeys improved with time:

Improvement over time

It’s quite a complicated figure, but pretty straightforward to work out. Panel A shows the error rate (top) and time to reach the target (bottom) for one of the monkeys (the other is shown in the red inset). You can see that they get better and faster at hitting the target. Panel B shows that the monkeys get better as they make more reaches in a session on days 2 and 4, but by day 6 their performance is basically at a plateau. Panel C shows performance in the first 5 minutes of the task – note that after a few days their error rate drops significantly. Panel D shows the actual cursor movements they’re making, and how those movements get more correlated (i.e. more similar to one another) as time goes on.

The researchers also note that stable task performance is associated with the stabilization of the neural tuning properties – as the preferred directions of the neurons (the directions in which they fire the most) get more similar across days, performance increases.

So this is pretty interesting – a stable subset of neurons can be used to control an onscreen cursor. Where I think it gets cooler are some of the other questions they ask about this stable neural population. What happens if you remove certain neurons from the subset being fed into the decoder – that is, is the whole group being used to control the cursor, or just a subset of the subset? Here’s your answer (Figure 5 in the paper):

Performance relative to number of neurons dropped

Yup, performance drops significantly as you remove neurons. But, crucially, it isn’t destroyed with the loss of one or two, meaning that if a neuron dies, for example, it’s not going to radically change your performance. And since the brain is always learning and changing, other neurons are likely recruited to fill the gap.

There’s more in here, but one of the most interesting things is the ability to learn new decoders alongside old ones, and recall them rapidly when necessary, and also that the stable activity patterns emerge very early in each trial. This finding is very awesome because it parallels something I’m interested in for my own research: the idea of internal models in the brain that are used for certain tasks and can be switched between when necessary. So the same set of neurons can theoretically be used to perform different tasks, as long as the internal model ‘knows’ how to interpret their firing for each task.

As I said, incredibly cool stuff, and it means that we are another step closer to understanding how brain activity controls prosthetic limbs – and, of course, our real, natural limbs as well.

By the way, the paper is open access so you can read it yourself (if you don’t have an institutional subscription) via the link at the bottom.


Ganguly, K., & Carmena, J. (2009). Emergence of a Stable Cortical Map for Neuroprosthetic Control PLoS Biology, 7 (7) DOI: 10.1371/journal.pbio.1000153

Images copyright © 2009 Ganguly & Carmena


  1. Ha, now this one I could understand all of. I too, found it to be cool info, thanks, Carl!

  2. Glad you liked it, Puck. It's hard to judge how to present things sometimes, because of course all this stuff requires a vast amount of specialist knowledge. I do my best to try to explain it in terms that someone who hasn't been stuck in a room with robots for five years will be able to take something away from.

  3. two questions from someone who lacks the vast amount of specialist knowledge:

    1. how were single neurons "eliminated" in the found structure?
    2. if the same set of neurons can cause alternating tasks, "who" does the differenciation? or do I have to think of something like an internal type of "grammar"?

    Thanks for a short reply

  4. Good questions, Herbert, thanks.

    1. I wasn't clear about this in the description, sorry. Basically the researchers used a decoder to read the neuronal activity. To 'eliminate' one of the neurons from the decoder, they just didn't include its input. So the neuron was still there, merrily firing away, but it wasn't being used to control the cursor.

    2. This is kind of the core of the paper for me. What we're seeing here is only the output of the neurons in primary motor cortex (M1) - these neurons themselves are connected to many others. So for example a different task would cause frontal cortex neurons to activate, which would then send a sweeping series of signals to activate other cortical areas, like the cerebellum. This activity then modulates the M1 neurons and causes them to have different activity for different tasks.

    That's the way I think about it anyway. Hope that made sense!

  5. Good post! Good blog as well by the looks of your first posts - keep it up :)

  6. Thanks, I appreciate it! I'm enjoying the writing and reading. Hopefully I won't slack off too much when my experiment finally starts working and I can do some testing...