What is the goal of neuroscience?

I’ve just completed the neuroscience bootcamp that precedes the start of classes at Columbia. The bootcamp consists of some lectures, some lab tours, and some more hands-on lab demonstrations. Through it, I have been exposed to a vast variety of methods and studies. This includes everything from genetic sequencing to extracellular physiology and behavior. As a a result, I couldn’t help but think: what is the goal of neuroscience? Conveniently, that question makes for a pretty nice first blog post topic.

So, what is the purpose of this field that I and and my fellow first years are subscribing to for at least the next 5-6 years and presumably far beyond? Webster’s dictionary defines… no I’m not going to go there. But there is a general consensus that the study of neuroscience is the pursuit of “understanding the brain.” But what would that really entail? What do we have to achieve to say that we’ve understood the brain? Many labs are working on the seemingly simple problem of learning the basics of neuroarchitecture, what connects to what. Other labs are looking at what proteins are associated with certain neuro-degenerative diseases. There are people investigating topics such as dendritic spines, neural correlates of visual attention, the dynamics of brain blood flow, and computational models of olfactory processing, to name a few. But even if we somehow got to the point of ‘understanding’ one of these topics, would we say that we understand the brain? What if we understood all of them? Are these seemingly disparate lines of research working towards one cohesive theory of the brain?

I’m inclined to say that at present they are not, and for a variety of reasons. Two main ones being a difference of level and a difference of purpose. By difference of level I am referring to Marr’s levels as he describes in his book, Vision. While Marr was referring specifically to the study of visual information processing, I think his distinctions can be applied to the brain’s processes at large. To Marr, information processing is done on three levels: computational, algorithmic, and physical. This ordering basically represents a funneling of how information can be processed from the very broad description of the goal of the information processing, to the specific approach used to tackle that goal, and finally to the implementation of that approach in the real world. To borrow an example from computer science: Let’s say you have a list of unordered numbers. At the computational level, the goal is to put the numbers in order. Algorithmically, a mergesort approach may used to put the elements in order. That mergesort algorithm can be implemented physically by writing and executing a program in Python on your Mac book. The distinction between the levels is clear when you consider how a different sorting algorithm could’ve just as well been executed on the same computer, or that the mergesort algorithm could have been implemented on your dad’s old Hewlett-Packard desktop from the 90s. Since much the same distinctions can be made about the brain’s information processing, it’s unsurprising that we’ve ended up studying it in different ways on different levels. Large-scale imagers and psychologists may be more interested in what the goal of specific area is, whereas theorists and systems researchers are more likely to care about the algorithms being implemented, and cellular and molecular people are focused on the physical details. The level of study determines the method used, the questions asked, and the value of findings. Of course the distinctions aren’t completely clear, as many forms of research can span levels, or perhaps belong to their own “half-level” somewhere in between. But in any case, to say that we are all working toward the same understanding of the brain, when the brain itself is operating on these different levels that each need to be understood, seems incorrect. We are all working towards an understanding of the brain, but not necessarily the same one.

The other problem, the difference of purpose, is related to how we define success. The way in which we frame the motivation for our research informs how we know when progress is made. In a lab studying Parkinson’s, any experiment that successfully slows or reverses the progression of symptoms would certainly be considered successful, and rightly so. Equivalently, a lab working on prosthetic limb development is happy to find any recording method or decoding algorithm that allows for more accurate control by the user. But do these advances mean an increase in our understanding of the brain? Not necessarily. For instance, treatments of depression (both pharmaceutical and extreme methods such as deep brain stimulation) have been black boxes in terms of how and why they are effective. Certainly these mysteriously effective treatments have led some to investigate their methods, which can produce knowledge about the brain. But in many translational labs the purpose of the research is only tangentially related to understanding the brain. A deep understanding of the brain is only helpful insofar as it leads to new treatments, but treatments without an understanding can be just as beneficial. I wouldn’t go so far as to say this kind of research isn’t neuroscience, but it does demonstrate the diversity of the field, and why it can be so hard to define.

However, I do feel that the varied, irregular and disjointed terrain of this field is merely a product of our present (very limited) knowledge of the brain. We don’t have enough knowledge to see how a cohesive theory of the brain could arise from all our disparate branches of research. Of course if we did reach a full understanding of how the brain works, it would cover all possible levels and serve any purpose. Our knowledge of the computational, algorithmic, and physical workings of Alzheimer’s and the brain areas involved with it, for instance, would make the production of treatments straight-forward. The field will be united. But for now, we are all working on separate chunks of a puzzle who’s end picture none of us knows for certain. The best we can do is try to add one more piece onto our chunk in the hopes that they”ll all come together some day. However, for now, I think the goal of neuroscience will continue to vary from lab to lab, from researcher to researcher, and maybe even from day to day.  Until we’ve all worked hard enough to realize that we’re working on the same thing.