Thomas Pfeil

TL;DR: This review addresses the opportunities that deep spiking networks offer and investigates in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware.

TL;DR: This study presents a highly configurable neuromorphic computing substrate and uses it for emulating several types of neural networks, including a mixed-signal chip, which has been explicitly designed as a universal neural network emulator.

TL;DR: The proposition of a good hardware verification practice may rise synergy effects between hardware developers and neuroscientists, and how weight discretization could be considered for other backends dedicated to large-scale simulations is suggested.

TL;DR: A methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems and represents the basis for the maturity of the model-to-hardware mapping software is presented.

TL;DR: This work makes use of neuromorphic hardware—electronic versions of neurons and synapses on a microchip—to implement a neural network inspired by the sensory processing architecture of the nervous system of insects, and demonstrates that this neuromorphic network achieves classification of generic multidimensional data—a widespread problem with many technical applications.