Hardware Spiking Artificial Neurons, Their Response Function, and Noises

In this chapter, overviewed are hardware-based spiking artificial neurons that code neuronal information by means of action potential, viz. spike, in hardware artificial neural networks (ANNs). Ongoing attempts to realize neuronal behaviours on Si ‘to a limited extent’ are addressed in comparison with biological neurons. Note that ‘to a limited extent’ in this context implicitly means ‘sufficiently’ for realizing key features of neurons as information processors. This ambiguous definition is perhaps open to a question as to what neuronal behaviours the key features encompass. The key features are delimited within the framework of neuromorphic engineering, and thus, they approximately are (i) integrate-and-fire; (ii) neuronal response function, i.e. spike-firing rate change upon synaptic current; and (iii) noise in neuronal response function. Hardware-based spiking artificial neurons are aimed to achieve these goals that are ambitious albeit challenging. Overviewing a number of attempts having made up to now illustrates approximately two seemingly different approaches to the goal: a mainstream approach with conventional active circuit elements, e.g. complementary metal-oxide-semiconductor (CMOS), and an emerging one with monostable resistive switching devices, i.e. threshold switches. This chapter will cover these approaches with particular emphasis on the latter. For instance, available types of threshold switches, which are classified upon underlying physics will be dealt with in detail.