The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

In the last years, the idea to dynamically interface biological neurons with artificial ones has
become more and more urgent. The reason is essentially due to the design of innovative …

This paper presents an efficient approach for implementing spike-timing-dependent
plasticity (STDP) on the SpiNNaker neuromorphic hardware. The event-address map** …

Closed-loop experiments involving biological and artificial neural networks would improve
the understanding of neural cells functioning principles and lead to the development of new …

This paper describes how an emerging standard neural network modelling language can be
used to configure a general-purpose neural multi-chip system by describing the process of …

Background: The human brain is the most complex system in the known universe, it is
therefore one of the greatest mysteries. It provides human beings with extraordinary abilities …

Spatio-and spectro-temporal data (SSTD) are the most common types of data collected in
many domain areas, including engineering, bioinformatics, neuroinformatics, ecology …

With both knowing more and more details about how neurons and complex neural networks
work and having serious demand for making performable huge artificial networks, more and …

We have developed an open software platform called Neurokernel for collaborative
development of comprehensive models of the brain of the fruit fly Drosophila melanogaster …

Artificial neural networks increasingly involve spiking dynamics to permit greater
computational efficiency. This becomes especially attractive for on-chip implementation …