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Software engineering + neuroscience

Learn to program by understanding the brain

A free, hands-on path from your first line of Python to building brain-computer interfaces and models of neurons. Real neural data from day one. No programming or neuroscience background required.

Start the path Read why this matters

New to all this? How a brain works, how a language model works, and where they meet

A beginner’s guide with animated figures: a neuron next to an artificial unit, a patch of cortex next to a language model predicting the next word. No code, no equations, fifteen minutes.

Why this exists

Modern AI was born from ideas about the brain, yet most machine learning engineers have never looked at a spike train, and most neuroscientists cannot write the code to analyse their own data. This site sits in that gap. It teaches neuroscience through code and code through neuroscience, and it starts from zero.

The path

Seven tracks, each building on the last. Work through them in order, or jump in where your background puts you. The tiles show what is published so far; the rest is being written in order.

flowchart TB

F[1. Foundations] --> S[2. Signals]

S --> D[3. Data]

D --> M[4. Models]

S --> B[5. Decoding and BCI]

M --> B

M --> N[6. Neuro meets AI]

B --> P[7. Precision medicine]

N --> P
The seven tracks and how they feed each other. Start at 1; each arrow means the later track uses what the earlier one taught.
1FoundationsPython for people who have never programmed, taught through real neural data: a neuron, a response, a decoder, a population.4 lessons2SignalsWhat electrodes, EEG and scanners actually measure: from raw voltage to spikes, rhythms and a signal you can control a cursor with.3 lessons
3DataOpen datasets and file standards, and how to load, clean and share neural data reproducibly.coming soon
4ModelsFrom one leaky neuron to Hodgkin-Huxley to spiking networks, each built from scratch.coming soon
5Decoding and BCIClassify intention from neural signals, ending in a working brain-computer interface loop.1 article6Neuro meets AIDeep networks as models of cortex, learning without backpropagation, and what language models do and do not tell us about brains.5 articles7Precision medicineNeural biomarkers, clinical machine learning, and the many ways a good model can fail in a hospital.1 projectPrimersShort introductions to methods the lessons lean on: GLMs, ICA, Bayesian inference. Each one on real data.3 primersInteractive figuresArticles with live, draggable figures: a neuron you can drive, a seizure detector you can tune, a brain next to a language model.4 articles

Lessons use real recordings from published experiments, downloaded with one link, and every lesson is a script you build line by line with its actual output shown. If you are not sure where to begin, read why every neuroscientist should learn to code and then lesson 1.

About the author

I am Santosh Srinivasaiah, a Senior Lead Scientist working on precision medicine, brain-computer interfaces and semantic search. I spend my days translating between the people who study brains and the people who build software, and this site is the guide I wish had existed when I started. More about me and this site.

Latest writing

Notes at the intersection of software engineering and neuroscience.