Predoctoral Training in Quantitative Neuroscience

This proposal will extend a program for Predoctoral Training in Quantitative Neurosciences at Caltech. The field of neuroscience is currently experiencing explosive growth, driven by a plethora of new technologies for observing and manipulating the brain. To apply these methods fruitfully and interpret the resulting flood of data, modern neuroscientists need a sophistication in mathematical and computational approaches that traditional neuroscience training programs cannot provide. At the same time we are seeing a convergence of understanding at different levels of brain organization, ranging from the molecules that specify neural connections to the dynamic function of large circuits in the human brain. Thus, there is an urgent need to train young neuroscientists with an integrated perspective that spans many levels – from molecules to behavior to computation – which have traditionally been taught in separate programs. Caltech is in an ideal position to answer these demands. It is an institution almost entirely dedicated to scientific research, routinely ranked among the top few universities in the world. Caltech is well known for its rigorous training in physics, mathematics, and engineering, but it also has a distinguished history in biological research, and in the neurosciences in particular. The small size supports a climate in which scientific collaboration across disciplines is effortless. In 2019, we created an exciting environment for training in quantitative neuroscience. The training program is focused on PhD students in years 1 and 2. The strategy begins with selective recruiting of candidates from a broad range of undergraduate majors, ranging from biochemistry to psychology to computer science. First-year students get introduced to faculty research during three rotations of 3 months each, after which they choose a primary mentor to supervise their PhD research. Trainees also complete a rigorous 2-year course curriculum that ensures core competency in the following six areas: (i) molecular and cellular neuroscience; (ii) systems and circuit neuroscience; (iii) human neuroscience and brain disorders; (iv) tools and technology for neuroscience; (v) applied mathematics and statistics; (vi) scientific programming and data analysis. Trainees get writing experience through a mentored process of fellowship applications. They also give numerous oral presentations in forums on and off campus. After a candidacy examination at the end of year 2, trainees focus on PhD research, with the aim of authoring one or more major publications, and graduating by year 6 or earlier. Each trainee’s trajectory through the program is accompanied by individualized advising, adjusting coursework to ensure broad competency while also challenging the student in a domain of special expertise. In summary, the proposed program in quantitative neurosciences responds in a timely manner to an urgent demand for a new type of graduate training in this rapidly evolving discipline.