BrainScaleS-2 User Documentation

The documentation of the BrainScaleS-2 system comprises two main components:

• Demos & Examples providing an introduction to the system and its usage,

• and API documentation generated from in-code comments

Contents

• Demos & Examples
  • Welcome to the BrainScaleS-2 Tutorial
    • BrainScaleS-2 single neuron experiments
    • Learning with the SuperSpike rule
    • Introduction to matrix multiplication
    • Exploring the analog MAC operation
    • Train DNNs on BrainScaleS-2
    • BrainScaleS-2 on-chip plasticity experiment
    • Structured Neurons
    • How to use Genetic Algorithms to automatically parameterize BrainScaleS-2
    • Training an SNN on BrainScaleS-2 with PyTorch
    • Executing the Notebooks
    • Shared Hardware Resources
    • Final test: Hardware Execution
  • BrainScaleS-2 single neuron experiments
    • A silent neuron
    • Leak over threshold
    • Fixed-pattern variations
    • Summary
    • Outlook: External stimulation
  • Learning with the SuperSpike rule
    • Training spiking networks with surrogate gradients
    • Helper functions
    • Define network in PyNN
    • Construct Poisson input spike trains
    • Generating a taget spike train
    • The SuperSpike learning rule
    • Training the network
  • BrainScaleS-2 on-chip plasticity experiment
    • Controlling the on-chip plasticity processor
    • The plasticity kernel
    • The experiment
  • Structured Neurons
    • Imports and Calibration
    • Constructing a Compartment Chain
    • External Input and Experiment Definition
    • Performing the Experiment
  • How to use Genetic Algorithms to automatically parameterize BrainScaleS-2
    • Constructing the Compartment Chain in PyNN
    • Genetic Algorithm
    • References
  • Training an SNN on BrainScaleS-2 with PyTorch
    • References and further reading
  • Exploring the dynamic range
    • Define network in PyNN
  • Introduction to matrix multiplication
    • Principles
    • Example
  • Exploring the analog MAC operation
    • The hxtorch API
    • Noise and fixed-pattern deviations
    • Linearity of the MAC operation
  • Train DNNs on BrainScaleS-2
    • Hardware in the loop
    • Simulating hardware: The mock mode
    • Example application: the Yin-Yang dataset
  • Introduction to the non-interactive queue runner
    • First Experiment
    • Second Experiment
    • Third Experiment: Fixed-pattern noise
    • Fourth Experiment: External stimulation
  • Wie verarbeitet unser Gehirn Informationen?
    • Das Neuron
    • Das Aktionspotential
  • Unsere neuromorphe Hardware
  • Wie können wir mit den künstlichen Nervenzellen kommunizieren?
    • Einführung in PyNN
  • Experimente mit einer einzelnen Nervenzelle
    • 0. Umgebung vorbereiten
    • 1. Einfluss der Zellparameter
    • 2. Stimulierung einer Nervenzelle
  • Experimente mit mehreren Nervenzellen Teil 1
    • Umgebung vorbereiten
    • Die Feuerkette
  • Experimente mit mehreren Nervenzellen Teil 2
    • Umgebung vorbereiten
    • Sudoku
    • Bonus: Wie hängt der Erfolg mit der Anzahl der vorgegebenen Zahlen zusammen?
  • Maschinelles Lernen
    • Was ist ein neuronales Netz?
    • Umgebung vorbereiten
    • Beispiel: Erkennung von handgeschriebenen Ziffern
  • Neuronenverbindungen - Synapsen
    • 1. Synapsen Position
    • 2. Synaptisches Gewicht
    • 3. Synapsen Art
  • Synapsennetzwerke
    • Die Oder-Operation
    • Die Nicht-Operation
    • Die Und-Operation
  • Welcome to the Advanced Physics Lab for Physicists by the Electronic Vision(s) Group
  • Introduction to PyNN
  • Binary operations using spiking neurons
    • The OR-operation
    • The AND-operation
    • The NOT-operation
    • The XOR-operation
  • Sudoku
• API Reference
  • PyNN API
  • hxtorch API
    • hxtorch from Python
    • hxtorch from C++
  • grenade API
  • haldls API
  • lola API
  • stadls API
  • fisch API
  • hxcomm API
  • calix API

Indices and tables

• Index

• Search Page