The Dropout Curriculum

How do you plan what to learn, to solve the problems you care about?

How do you figure out what you should learn, while you are a college student?

Or if you’ve dropped out of (or avoided) college, and are teaching yourself (the situation that prompted the title of this essay), what knowledge should you seek, and what skills should you practice?

Some people learn a skill early in life and then practice that skill throughout their career. This can be quite fulfilling if one loves the day-to-day act of performing that skill. But one is then often limited to solving problems that are well-addressed by that skillset, meaning that other problems might appear to be out of reach. Conversely, some people are obsessed with a specific problem and are excited about solving it over extended periods of time, perhaps even over a lifespan. But if such a person lacks the skills needed to solve that problem, the path could become frustrating, or even futile.

A hybrid model that may be useful for many people is to consider having a “skill” phase of one’s life, where one learns the fundamental knowledge appropriate to address a problem space of interest, practicing relevant skills as needed, followed by an “impact” phase of one’s life, where one applies the knowledge and skills learned earlier, to that problem space. How should one choose what to learn in the “skill” phase to best solve the problems of the “impact” phase?

Of course, you can jump right into a problem space and learn what’s going on: what the needs are, as perceived by current practitioners within that problem space, and what skills they currently possess to meet those needs. This is a common strategy for reasonably mature fields, such as math or computer science, and often works well. But if the problem space is from a less mature field, such as biology or bioengineering, and thus full of fundamental risk and ambiguity - the kind of problem space that many ambitious and well-intentioned people might seek to enter, and even transform - then there is no guarantee that practitioners currently in the space have the complete set of knowledge and skills needed to fully address the problems of their space. There is also no guarantee that current practitioners are perceiving the needs of their problem space in the most effectively solvable way. In such a situation, one might want to have studied something radically different before entering the problem space, to bring a fresh perspective to the table. In many scientific fields, big revolutions were initiated by outsiders. Is it possible to learn the right kind of knowledge in order to become that outsider, deliberately?

If one wants to bring a radically different perspective to a problem space, then one might be without clear mentors to copy or curricula to follow. In such a case, how does one figure out what to do? One possibility is to look at the problem space, which often relates to a complex system and all the ways it can go wrong, and examine the fundamental building blocks of the system and how they interact. Then, one could focus the “skill” phase of one’s learning on studying knowledge and skills related to those building blocks, and their interactions, so that later, in the “impact” phase, one could apply such knowledge and skills to the higher-level problems at hand.

Alternatively, one could try to break down the building blocks and interactions, as currently perceived by the field, into even finer-scale building blocks and interactions, aiming to reveal unexploited avenues for innovation. For example, suppose one wants to be a biologist or bioengineer, and solve intractable diseases such as those of the brain or aging. If you study such diseases at a phenomenological level, and ignore the relevant building blocks (e.g., biomolecules) and their interactions (e.g., chemistry), you could be missing out on critical mechanisms underlying the disease, or potential ingredients for new tools to confront the disease.

In contrast, if one thinks of the human body, or any biological system, as made of fundamental building blocks, biomolecules, which interact through chemistry according to physical laws, then one might be able to use that lower-level knowledge to support the design of radically novel, highly effective technologies. Think of magnetic resonance imaging, or super-resolution microscopy, or genome sequencing - in each case, a deep understanding of physics or chemistry, applied to biological building blocks considered at the molecular level, was critical for envisioning, realizing, and applying the technology. (The quote, attributed to Einstein but probably apocryphal, that “problems cannot be solved at the level of understanding that created them,” comes to mind.)

One advantage of studying the science of building blocks and their interactions, in the skill phase of your life, is that you are probably studying a science that is more mature than the science you work on in the impact phase. To continue the example from the previous paragraph, physics and chemistry are more mature sciences than biology and bioengineering. The good news is - by studying these more mature sciences, you will be learning things that will likely still be true, and even current, decades later. In contrast, a less-mature, less-fundamental field may have evolved, with new discoveries and inventions, so as to become perhaps unrecognizable relative to its initial state. (This doesn’t mean to avoid majoring in biology or bioengineering, by the way - but one should try to dive deeply into the mechanisms underlying all that one learns, and not always be content with the abstraction layers presented by an expert or class.)

Suppose you are a student in a university - what major should you pick, and which classes should you take? Suppose you are not in a university, or dropped out - what knowledge, online or otherwise, should you learn? In both cases, it can be dangerous to follow a cookbook-style path. For example, simply pursuing the requirements of a major in a university setting may not help you achieve your specific career goals. If you are self-taught or seeking out mentors, then there still remains the challenge of choosing what to learn or who to guide you. The arguments above suggest that it could be helpful if you (or a mentor) could, given a problem space, identify the building blocks (and interactions) to study, and then consider studying the science of those building blocks and interactions. Below, each of the three of us includes some thoughts on our personal path:

One of us (Ed) studied physics, and electrical engineering and computer science, and also quite a bit of chemistry. Now, I lead a neurotechnology group at MIT, aiming to deconstruct the brain into computational processes, that run on top of chemistry, according to the laws of physics - with one goal being to create biologically accurate computer simulations of the brain, and another being to understand consciousness. Understanding biomolecules and their interactions involves physical principles that operate over certain ranges of energy scale, size scale, and temporal scale. That means that knowing general relativity (which helps with understanding black holes) or plasma physics (which helps with understanding the sun) may not be as immediately useful as, say, thermodynamics, or quantum mechanics. During the “skill” phase of my career, I had the luxury (having been an undergrad for 6 years) to learn several fundamental sciences, perhaps to overcompletion. Not all my classes are equally useful to me now, although I enjoyed most of them very much. In the interest of efficiency, at the end of this essay, you can see my suggested “dropout curriculum” to learn how to be a neuroengineer, written up for someone who knows basic physics, math, and chemistry, and wants to know the essentials for broadly operating in the field of neuroengineering. It is a list of classes (or online resources associated with classes) that could be helpful to the quest, stripped to the minimum.

Then I had to pick a problem space to work on, for the “impact” phase of my career. I cofounded MIT’s autonomous submarine team, and a couple months later, we won the first world championship. That was, scientifically speaking, too easy a problem. Another project I worked on at the time, was building a quantum computer. Early quantum computing designs did not scale well; this problem was too hard, scientifically speaking, at the time. My next project was a neuroscience one - and this one was “just right.” The brain may appear complex and messy at first glance, but because of my skillset, it appeared to me to be a computer system running on chemistry according to physics. One core problem was the heterogeneity of the building blocks - unlike physics and chemistry, which involve a small number of building blocks (elementary particles and atoms, respectively), the number of kinds of building block in the brain (biomolecules) is perhaps in the millions. So I set out to build tools to see and control those things. (More on this in a later series of posts.)

Claire, another author, chose to study electrical engineering and computer science to build fundamental skills in physics, math, and the generally applicable field of computer science, rather than taking more special topics classes in neuroscience. I instead learn to apply these skills through hands-on work and research. For example, at E11 Bio, I have been able to understand the intricacies of the problems that connectomics poses. And, through working at various labs at the McGovern Institute, I have learned many detailed microscopy, circuit design, and wet lab techniques that build strong intuition when designing future experiments and goals. However, it is not just neuroscience research; by also working with AI and algorithm researchers, I have been able to apply tools from other fields to novel research problems.

Finally, Nina, another contributor, studies computational neuroscience, with the goal of deeply understanding the fundamentals of math, physics, computation, and their relevance to pursuits within chemistry and biology. I spend most class slots I have focusing on more mature sciences, with occasional neuroscience electives to inspire excitement and learn currently-relevant techniques. I take part in many extracurriculars to better expand my breadth and depth of relevant skills, with occasional impact-focused projects to dip my toes into the problem spaces I’m passionate about (e.g. Alzheimer’s and related neurodegenerative conditions). During my time working with the Tsai Lab in the Picower Institute for Learning and Memory at MIT (they focus on network-level approaches to studying neurological disorders) on cognitive resilience in Alzheimer’s, I have learned both relevant skills (math, bioinformatics, experimental design) and have been able to contribute knowledge to the field as a whole.

Of course, when one pivots from the comfort of the “skill” phase to the ambiguity of the “impact” phase - that can feel difficult, provide enormous stress and frustration, and make one feel truly stupid - even if one is well prepared. Difficult problems are, well, difficult. And pretending a problem is simple, does not make it simple. We will have many essays on how to tackle the art of problem solving, in the future. The “impact” phase often involves breaking down big problems into smaller ones, strategies such as systematic ideation and constructive failure, deep questioning and strategic collaboration, and the engineering of serendipity (which we think is a learnable, teachable thing). There may not be a roadmap for where you are going. But you can learn how to make one of your own. By beginning with skill and then progressing to impact, you will be well equipped with toolboxes to not only plan out your path but to adapt to inevitable surprises and tantalizing opportunities along the way.

Here are classes of the “Dropout Curriculum” for neuroengineering, labeled by MIT class number (as evaluated in 1995-1999, with links to current, closest-match, MIT Open Courseware sites):

Core Classes¹

• 5.11 Principles of Chemical Science (posted as 5.111SC)

• 5.12 Organic Chemistry I

• 5.60 Thermodynamics and Kinetics

• 7.012 Introductory Biology

• 8.01 Physics I (posted as 8.01SC)

• 8.02 Physics II

• 8.03 Physics III (posted as 8.03SC)

• 8.04 Quantum Physics I

• 8.05 Quantum Physics II

• 18.01 Single Variable Calculus (posted as 18.01SC)

• 18.02 Multivariable Calculus (posted as 18.02SC)

• 18.03 Differential Equations (posted as 18.03SC)

• 18.06 Linear Algebra (posted as 18.06SC)

• 6.003 Signals and Systems

• 6.042 Math for Computer Science

• 8.044 Statistical Mechanics I

• 6.011 Communication, Control, and Signal Processing

• 9.00 Introduction to Psychology (posted as 9.00SC)

• 9.641J Introduction to Neural Networks

1. Some of these classes have multiple OCW links; we chose the best link (in our opinion, and chosen at the time of our writing of this essay) to learn from.