Spiking networks, from the neuron up

The course

Spiking networks, from the neuron up

After a short opening on when spiking networks are worth using, fourteen chapters go from one neuron to a network that drives a car from events. Each starts with the idea, builds the math, gives you a live model to change in your browser, and ends with the sparx code that runs the same model.

Where to start

• New to neural networksRead in order from chapter 1. The math uses sums and exponentials, and each chapter builds what it needs.Start with chapter 1
• You train deep networksRead chapter 0 for when spiking networks pay off. Skim 1 to 3, then read 4 to 8, where the training differs from what you know.Start with chapter 0
• You study or simulate brainsSkim 1 to 3 for this course's notation, then read 9 to 11 for circuits in physical units and how sparx checks itself against NEST and Brian2.Start with chapter 9

Before you start

1. 0Why spikes?Where spiking networks beat conventional ones today, where they lose, and how to choose.

Neurons

1. 1What a neuron doesSynapses, a membrane that keeps charge, and the all-or-none spike.

Learning

1. 5Surrogate gradientsWhy a spike has no gradient, and the stand-in that lets gradient descent through.
2. 8DelaysSpikes take time to travel. Learning how long turns sequences into coincidences.

Circuits

1. 9Networks and dynamicsBalanced excitation and inhibition, irregular firing, and chaos.
2. 11Simulators and fidelityHow a simulator steps time, and how to tell whether two of them agree.