How can universities best prepare students for a career in neuroscience? Ask Professor Mark Goldman, Department of Neurobiology, Physiology and Behavior and the Center for Neuroscience, and he’ll tell you it’s time to rethink the traditional biology curriculum. To unravel complex systems like the brain, students need advanced training in quantitative and computational techniques. In a paper published in Current Opinion in Neurobiology, Goldman asked neuroscientists for their views on undergraduate and graduate biology education. The survey’s results highlight a critical need for expanding computational neuroscience training at all levels of higher education. “One can no longer put biological sciences or neuroscience in one box and computer science and mathematics in a separate box,” Goldman said. “They are fundamentally intertwined, intermixed, intermingled.”

Bridging the computational divide Goldman and co-author Michale Fee, of the Massachusetts Institute of Technology McGovern Institute and Department of Brain and Cognitive Sciences, sent an informal survey to 107 neuroscientists. Overwhelmingly, respondents noted life sciences students tend to lack sufficient training in quantitative research methods, including computer programming, biological modeling and algorithmic thinking. The results, according to Goldman, highlight a traditional cultural divide between the life sciences and the computer science fields. Goldman and Fee call for higher education leaders to tear down the silos separating these fields and to create biology curriculums promoting interdisciplinary research. A physicist by training, Goldman transitioned to the life sciences during his Ph.D. Later, as a faculty member, he was surprised by the small roles mathematics and computer science played in many biology curriculums. Despite formal instruction in mathematics, students learn relatively little about how mathematical and statistical models are used side-by-side with empirical work to understand how biological systems function, Goldman said.