Neural Spiking Dynamics in Asynchronous Digital Circuits

We implement a digital neuron in silicon using delay-insensitive asynchronous circuits. Our design numerically solves the Izhikevich equations with a fixed-point number representation, resulting in a compact and energy-efficient neuron with a variety of dynamical characteristics. A digital implementation results in stable, reliable and highly programmable circuits, while an asynchronous design style leads to energy-efficient clockless neurons and their networks that mimic the event-driven nature of biological nervous systems. In 65 nm CMOS technology at 1 V operating voltage and a 16-bit word length, our neuron can update its state 11,600 times per millisecond while consuming 0.5 nJ per update. The design occupies 29,500 mu m(2) and can be used to construct dense neuromorphic systems. Our neuron exhibits the full repertoire of spiking features seen in biological neurons, resulting in a range of computational properties that can be used in artificial systems running neural-inspired algorithms, in neural prosthetic devices, and in accelerated brain simulations.