Resolving the size and nature of neocortical population codes

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Cortex is the source of our most basic and most advanced brain functions, of how we hear, see, and touch; of how we think, plan, and act. All arise from the combined activity of millions or billions of individual neurons. Within these gargantuan numbers, small sets of neurons have specific roles. One set might fire to a high pitched tone; one might fire to the brush of cloth on the tip of an index finger; yet another to start moving your right elbow. Our proposal asks the simple question: to do a task, how many sets, with how many roles, does the brain use? Imagine the part of the brain necessary for doing a particular task is an orchestra playing in a sound proofed room. Our question is the same as asking: how can we work out what score they are playing? And work out the roles of each set of instruments within that score? Up till now, our brain recording technology has been like blindly lowering microphones at random next to one or two players in the orchestra, listening for a few minutes, then trying to reconstruct the entire score. Done this way, we have no idea of which type of individual instruments are involved, let alone how they interact, or group into their wood, string and other ensembles. We don't even know how big the orchestra is. So to solve the problem of reconstructing the brain's score for a task, we need to be able to record the whole orchestra of neurons at once, one microphone per neuron. We can then work out from that cacophony what ensembles and instruments they represent, their roles, and how they combine to create the full score. Recent technological advances means that we now have the right kind of one-microphone-per-neuron data. This has been made possible by the wonderfully neat correspondence between the whiskers on a mouse's face and the way a whisker is represented in the brain. Mice can learn to find which of two spouts contains water by touching a pole with a single whisker. This single whisker is represented in their cortex by a barrel-shaped column of neurons. It is small enough that a lab has now recorded the activity of every neuron in its top half while the mice tried to get their water. As the only representation of that single whisker, it must contain all the information the mice need to solve the task. So we know these data must contain within them the brain's orchestra for this task. Our goal is to use this data to answer our question: how many sets of neurons, with how many roles, does the brain need to solve this task?To do so, we will use so-called "unsupervised" methods, algorithms that can determine for themselves how many different sets of neurons there are in the data, how large they are, and which neurons belong to which sets. They do this by working out which neurons are consistently active at the same time. Having found the sets, we can then find out the their roles by comparing their activity with the mouse's behaviour: for example, we can work out if some sets are active while it moves its whiskers, or while it licks the water.If we answer this question, what do we learn? We will learn about the basic building blocks of how cortex computes. If we can only represent N things in N sets of neurons, then that places an upper limit on our capacity to think. We will learn about the resilience of cortex to damage, whether through accidents or diseases such as dementia. If multiple sets of neurons have the same task, then we may lose some and carry on as normal. But if some sets have a unique role, then damage to them, however small, could be disastrous. Ultimately, we will learn about how these sets combine to produce the full score. Labs and clinics are exploring how we can transmit the activity of small bits of motor cortex to give patients direct control over their artificial limbs. If we knew how to work out the full score for controlling limb movement, the accuracy of this control would improve many times over.