Hey Nav, so in my experiment, the first step to understanding brain activity (as far as we do 🙂 ) is to record electrical signals from as many neurons as possible. When neurons are active, they generate small electric shocks (called action potentials, or spikes for short), which then lead to them sending messenger molecules to neighbouring neurons. Depending on the type of neuron you are looking at, this either activates or silences those neighbouring neurons. By putting a long row of extremely small electrodes in the brain, we can measure spikes from a lot of the surrounding neurons. Say, about 50 neurons. Still very little compared to the thousands of neurons around, but enough to be getting on with. That’s step one.
The next challenge then is to find ways of analyzing and describing the activity patterns of those 50 neurons. Does neuron 37 always spike when neuron 4 spikes? Are there rhythms, neuron groups etc.? You can imagine that if 50 neurons are all active over a long time, it gets pretty chaotic. So you need smart mathematical methods of highlighting the interesting structures in the mayhem.
Finally, you need to relate the patterns to what the mouse is doing at the time. Is he going for the right visual object? Is he running fast or slow? Is he paying attention, changing his mind? To find that out we obviously also need good ways of recording the exact behaviour of the mouse. Which is why the computer game is really handy because we have a record of all the movement decisions the mouse makes! If we can then find neuron patterns that predict what the mouse will do next (for example, if it will make a mistake or not), we’ve made a big step towards understanding brain signals.