BY DR SCOTT RICH
One of the most important lessons I’ve learnt in research is a simple one: terminology matters. It’s very difficult to convince someone of the validity of your hypothesis if you’re operating from a different set of first principles, amongst which the very meaning of language is perhaps most paramount. Even the simplest of terms can carry different meanings, particularly to those working in different environments or (most critically for me) different disciplines.
Given this conundrum, before we can begin to have the important conversation I aspire for with this series of blog posts I first have to define my terms. More specifically, what exactly do I mean by interdisciplinary research?
On the surface the answer may seem straightforward: interdisciplinary research is research that crosses disciplines. But here we find more non-trivial terminology that needs defining. What are these disciplines? In my mind, they’re the “traditional” academic disciplines as you’d see them defined by university departments. Mathematics is a discipline. Biology and chemistry, while both falling under the broad banner of “science”, are also unique disciplines. In the social sciences, philosophy, history, and literature are all their own disciplines, with many important sub-disciplines further complicating matters.
So, what then is interdisciplinary research? For me, it’s any research that utilizes tools, techniques, and resources from multiple “traditionally defined” academic disciplines. In my own work, I use tools from mathematics, applied physics, and computer science and apply them to questions posed from neuroscience. That would span at least four traditional “departments” in an institute of higher learning. The possible combinations are myriad, leading to an exponential increase in the types of research carried out in the modern age. For example, tools from mathematics can be applied to almost any scientific discipline (not just neuroscience), ranging from chemistry to sociology. But the potential for interdisciplinary research is not limited to the traditionally “hard” sciences. For instance, the burgeoning field of “neuroethics” raises important ethical questions about our ever-improving understanding of the brain, spanning not only the disciplines of philosophy and neuroscience, but the sometimes-larger divides between the “hard” sciences, “social” sciences, and the arts.
Inherent in these definitions is a final all-important term: traditional. The way that interdisciplinary research aims to break down the “traditional” barriers between academic fields is what makes this research so exciting, and yet so challenging. It’s the proverbial double-edged sword.
On one hand, there are a wide range of existing research problems that can be well served by an interdisciplinary perspective. To give a personal example, by creating simulated models of a neural system on a computer (commonly termed in silico experiments), I’m able to perform experimental manipulations that would be impossible or intractable in the brain of a live subject (termed an in vivo experiment) or in surgically removed brain tissue (an example of an in vitro experiment). While, for instance, existing drugs affecting neural activity may also bring about a variety of side effects, in a mathematical model I can more directly manipulate individual aspects of a neuron’s behaviour without these secondary effects. Importantly, we must never overlook the reality that no mathematical model can realistically encompass the entirety of a biological system like a neuron, but the insights provided by these simulated experiments are invaluable nonetheless. My experimental colleagues can take the results of my research to generate novel experimental hypothesis that have some “a priori” support, and examine whether my findings from a simplified model are indeed illustrative of the biological system. Following the previous example, by comparing my simulation of a drug’s primary effect with experimental results, my experimental collaborators are better suited to uncover which aspects of the pharmacological treatment are due to the compound’s stated purpose, and which are likely side effects.
The other side of the coin involves a constant battle with academic “tradition”. During my graduate studies I felt constantly pressured to justify the validity and importance of my interdisciplinary research to friends and colleagues who were more traditional, “pure” mathematicians. While they were studying centuries old mathematical theorems, I was focused on understanding the dynamics of a neuron. It was isolating and often left me feeling like I was without a true academic “home”. Given that I felt this way in a program that was designed to be “interdisciplinary”, I can only imagine the challenges others have felt! Even now, although I’ve found a home at an institution that actively supports and encourages interdisciplinary research, I still face additional challenges when applying for fellowships and publishing. Indeed, a traditional neuroscientist reviewing my work might find themselves skeptical of research that presents no biological experiments, putting the onus on me to justify the validity of my research program more than someone presenting more traditional neuroscientific research.
Despite these challenges, I couldn’t imagine myself in any other field. I maintain that those who embrace interdisciplinary research, and the associated challenges, will be better prepared for the future of academia, in which the divisions between “traditional” disciplines hold less and less weight. In these blog posts, I hope to convince you all of that important point. Every field of research can benefit from an interdisciplinary perspective, even if you only draw one tool, one citation, or one perspective from that secondary discipline. It may take jumping through a few extra hoops, but the results, and the satisfaction, are well worth the challenge.
In part two of this series, I’ll dive deeper into the winding road that led me to become a computational neuroscientist, including advice for aspiring interdisciplinary scientists at all stages of their careers looking to make a similar transition.
Dr Scott Rich is a Postdoctoral Research Fellow at the Krembil Research Institute in Toronto, Ontario, Canada. He received his B.S. in Mathematics from Duke University in 2012 and his Ph.D in Applied and Interdisciplinary Mathematics from the University of Michigan in 2018. Scott is an advocate for interdisciplinary research, with his current work as a Computational Neuroscientist epitomizing this endeavor. By applying mathematical and computational tools to a neuroscientific problem, Scott seeks to better understand and mechanistically explain the sometimes counterintuitive ways in which the brain transitions into seizure. You can find him here http://scottrich.mystrikingly.com/ and on Twitter @RichCompNeuro.