Explorables

Interactive versions of material from my teaching: small simulations that run in your browser, with sliders you can move while they run. They grew out of Jupyter notebooks for Theoretical Neuroscience at Goethe University and a webinar for the German Neuroscience Olympiad, and are inspired by Dirk Brockmann’s Complexity Explorables.

The leaky integrate-and-fire neuron

A spiking neuron model with live membrane trace, noise, and an f-I curve that builds up as you explore.

Data analysis: PCA and Fourier

Principal component analysis on the iris data, and sampling, aliasing and reconstruction with the discrete Fourier transform.

Recurrent networks: from kernel to pattern

A difference-of-Gaussians kernel, its spectrum, and a ring network that turns noise into stripes.

Hebbian learning in the weight plane

Plain Hebbian learning, saturation bounds and Oja’s rule, animated as trajectories in the space of two synaptic weights.