In this course we will study the computational properties of spiking neural networks implemented using analog "neuromorphic" electronic circuits. We will present network architectures and computational primitives that can use the dynamics of these circuits to exhibit intelligent behaviors. We will characterize these networks and validate them using full custom chips in laboratory experiments.
Lernziel
The objective of this course is to introduce students to the field of “neuromorphic intelligence” with lectures on spiking neural network architectures implemented using mixed-signal silicon neuron and synapse circuits, and with laboratory sessions using neuromorphic chips to measure the computational properties of different spiking neural network architectures. Class projects will be proposed to validate the models presented in the lectures and carry out real-time signal processing and pattern recognition tasks on real-world sensory data.
Inhalt
Students will learn about the dynamical properties of adaptive integrate and fire neurons connected with each other via dynamic synapses. They will explore different neural circuits configured to implement computational primitives such as normalization, winner-take-all computation, selective amplification, and pattern discrimination. The experiments will consist of measuring the properties of real silicon neurons using full-custom neuromorphic processors, and configuring them to create neural architectures that can robustly process sensory signals and perform pattern discrimination despite, or thanks to, the limited resolution and large variability of their individual processing element
Voraussetzungen / Besonderes
Accessible to NSC Master students. It is recommended (but not mandatory) to have taken the Introduction to Neuroinformatics course (INI-401/227-1037-00).
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs