In this study, we present a highly configurable neuromorphic computing substrate and use it
for emulating several types of neural networks. At the heart of this system lies a mixed-signal …

In this article, we present a methodological framework that meets novel requirements
emerging from upcoming types of accelerated and highly configurable neuromorphic …

High-level brain function, such as memory, classification, or reasoning, can be realized by
means of recurrent networks of simplified model neurons. Analog neuromorphic hardware …

Recent developments in neuromorphic hardware engineering make mixed-signal VLSI
neural network models promising candidates for neuroscientific research tools and …

Modeling networks of spiking neurons is a common scientific method that helps to
understand how biological neural systems represent, process and store information. But the …

In this paper we describe our approach towards highly configurable neuromorphic hardware
systems that serve as useful and flexible tools in modeling neuroscience. We utilize a mixed …

In this experiment you will characterize and use the Spikey-hardware platform. It is part of an
emerging field of neuromorphic computing devices and was developed in Heidelberg at the …

Neural networks present a fundamentally different model of computation fromthe
conventional sequential digital model. Dedicated hardware may thus be moresuitable for …

Deciphering the working principles of brain function is of major importance from at least two
perspectives. From the clinical viewpoint, a deeper understanding of our brains will lead to …

When describing increasingly complex systems, the required array of equations equivalently
grows in size and complexity. In many (usually simple) cases, statistical methods can be …