A wafer-scale neuromorphic hardware system for large-scale neural modeling

TLDR

Neural tissue modeling is a key tool for studying biological networks, traditionally performed with numerical methods, and recent efforts emphasize the usability of neuromorphic systems for neuroscience. The FACETS project aims to complement computational modeling with diverse neuromorphic systems. An integrated software/hardware framework using the PyNN language enables transparent execution of models on either neuromorphic hardware or numerical simulators, and a large analog neuromorphic system within FACETS supports complex models and realistic network topologies. The system can realize over 10,000 synapses per neuron, enabling direct execution of models that were previously only numerically simulatable.

Abstract

Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal an integrated software/hardware framework has been developed which is centered around a unified neural system description language, called PyNN, that allows the scientist to describe a model and execute it in a transparent fashion on either a neuromorphic hardware system or a numerical simulator. A very large analog neuromorphic hardware system developed within FACETS is able to use complex neural models as well as realistic network topologies, i.e. it can realize more than 10000 synapses per neuron, to allow the direct execution of models which previously could have been simulated numerically only.