One question I keep coming back to in olfaction is deceptively simple: before odors reach the downstream classifier-like layers of the brain, what should the early circuit do to the raw receptor responses?

Olfactory receptor neurons (ORNs) already transform chemical mixtures into neural activity. But their responses can be redundant, correlated, and strongly shaped by nuisance variables such as total concentration. A good preprocessing circuit should keep the useful relationships between odors while making the representation more efficient for downstream computation.

This is where similarity matching becomes a useful lens.

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I’ve been playing tennis since middle school, and I’ve been doing science seriously for about five years. For most of that time, I thought of them as separate pursuits — one for the mind, one for the body. But the longer I do both, the more I realize how deeply intertwined they are.

Here are the lessons the tennis court has taught me about research — and vice versa.

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When I tell people I study “computational neuroscience,” I usually get one of two reactions: a puzzled look followed by “so… you study computers?” or an excited “oh, like brain-computer interfaces?” Both responses miss the mark, but they’re understandable — it’s a genuinely strange and wonderful field that sits at the intersection of biology, physics, mathematics, and computer science.

This post is my attempt to explain what computational neuroscience actually is, why I fell in love with it, and what the first years of a PhD in this field have taught me.

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Hello and welcome! I’m Minxing Liu, a PhD student in Computational Neuroscience. This site is where I’ll be sharing my adventures at the intersection of two very different worlds: the laboratory and the tennis court.

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