Simulator-like exploration of cortical network architectures with a mixed-signal VLSI system

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-signal VLSI model that implements a massively accelerated network of spiking neurons, and we describe a novel methodological framework that allows to exploit both the speed and the programmability of this device for the systematic and simulator-like exploration of cortical network architectures. We present a variety of experimental results that illustrate the functionality of our modeling platform, and we verify all hardware measurements with reference software simulations. Especially on the network level these comparison studies are unique in terms of the quantitative correspondence between the data. The presented hardware experiments include high-conductance states in hardware neurons and the application of synaptic depression and facilitation for self-adjusting network architectures.