Implementing compact, low-power artificial neural processing systems with real-time on-line learning abilities is still an open challenge.In this paper we present a full-custom mixed-signal VLSI device with neuromorphic learning circuits that emulate the biophysics of real spiking neurons and dynamic synapses for exploring the properties of computational neuroscience models and for building brain-inspired computing systems.The proposed architecture allows the on-chip configuration of a wide range of network Mens Hats connectivities, including recurrent and deep networks with short-term and long-term plasticity.The device comprises 128 K analog synapse and 256 neuron circuits with biologically plausible dynamics and bi-stable spike-based plasticity mechanisms that endow it with on-line learning abilities.In addition to the analog circuits, the device comprises also asynchronous Bike Parts - Brakes - Parts digital logic circuits for setting different synapse and neuron properties as well as different network configurations.
This prototype device, fabricated using a 180 nm 1P6M CMOS process, occupies an area of 51.4 mm 2 , and consumes approximately 4 mW for typical experiments, for example involving attractor networks.Here we describe the details of the overall architecture and of the individual circuits and present experimental results that showcase its potential.By supporting a wide range of cortical-like computational modules comprising plasticity mechanisms, this device will enable the realization of intelligent autonomous systems with on-line learning capabilities.