Neuromorphic engineering (NE) encompasses a diverse range of approaches to information
processing that are inspired by neurobiological systems, and this feature distinguishes …

This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

Despite vast numbers of studies of stained cells in the mouse brain, no current brain atlas
provides region-by-region neuron counts. In fact, neuron numbers are only available for …

This book sets out to build bridges between the domains of photonic device physics and
neural networks, providing a comprehensive overview of the emerging field of" …

This paper introduces a spiking hierarchical model for object recognition which utilizes the
precise timing information inherently present in the output of biologically inspired …

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation
in a way that is inspired by concepts learned about the human brain. SNNs are artificial …

Personalization and adaptivity can promote motivated usage, increased user acceptance,
and user identification in serious games. This applies to heterogeneous user groups in …

We present results from a new approach to learning and plasticity in neuromorphic
hardware systems: to enable flexibility in implementable learning mechanisms while …

Ever since the dawn of antiquity, people have strived to improve their cognitive abilities.
From the advent of the wheel to the development of artificial intelligence, technology has …

Understanding the human brain is a “Grand Challenge” for 21st century research.
Computational approaches enable large and complex datasets to be addressed efficiently …