No, it doesn’t stand for Chicken Noodle Soup.
Yes, that’s what some of us call it anyway for fun.

Computation and Neural Systems — CNS for short — is probably one of the most confusing-sounding majors at Caltech. After four years, I think my family finally knows what it stands for. They still have no clue what it means, but hey, we’ve made progress!

I get the question all the time: what even is CNS? And to be fair, I asked it too. When I was picking a major, I wasn’t exactly sure what I wanted. I liked biology, I liked math, I liked computers, I liked big questions. CNS was the only option that didn’t make me choose.

At its core, CNS is about understanding how brains compute and how we can build machines that do the same. It lives in the space between neuroscience, machine learning, physics, engineering, and computer science. It’s the major for people who are curious about intelligence, structure, function, and how all of those blur together when you look closely enough.

The official CNS site puts it like this: we’re trying to understand the relationship between the physical structure of a system (neurons, molecules, circuits, hardware), the dynamics of how it runs, and the computational problems it can solve. Which sounds like a lot — and it is — but the beauty of CNS is that it lets you explore across disciplines. You’re not expected to come in knowing exactly what your thing is. In fact, you’re kind of rewarded for not knowing yet.

The program itself has an amazing origin story. CNS began as a PhD program in the 1980s, when a few Caltech legends — Carver Mead, John Hopfield, and Richard Feynman — collaborated to teach courses on neural networks, neuromorphic circuits, and the physics of computation. They were interested in the brain not just as a biological organ, but as a computing machine. They didn’t just ask how neurons worked, but they asked what kinds of problems those networks were designed to solve, and how you could replicate those processes in silicon.

Basically, before “AI” was the buzzword it is now, they were thinking about this stuff.

In 1986, CNS officially became a graduate program. It wasn’t until a couple of decades later that it opened up as an undergrad major. So yeah, it’s a little under-the-radar. There aren’t many of us. But that’s part of what makes it special. The people who choose CNS are almost always deeply curious, slightly chaotic, and totally okay with living in the grey area between disciplines.

Academically, the major is just as interdisciplinary as it sounds. You build a foundation in math, physics, bio, and computer science, and then branch into more specialised topics: neuroscience, machine learning, vision, signals and systems, neural computation, neuroeconomics, and research methods. Your schedule might bounce from cellular neurobio to signal processing to a CS learning theory class all in one week — and weirdly, it works. CNS is designed for breadth and depth, which is something I didn’t realise I needed until I was in it.

I’ve never regretted picking CNS. It let me explore without feeling behind. It gave me enough structure to stay grounded, but enough freedom to follow what I was genuinely interested in (and figure that out). Most importantly, it taught me how to think across boundaries and to take tools from one field and use them to solve problems in another.

So if you’re someone who’s into brain stuff and circuits, machine learning and biology, theory and application, then CNS might be your kind of major. It’s not one-size-fits-all. It’s not simple to explain at family dinners (trust me). But it’s interesting. It’s flexible. And it makes room for the kind of thinking that doesn’t always fit in a single department.

And yes — your family will still call it some version that fits the letters of the CNS acronym, but they’ll eventually remember!