  Neuromorphic computing, commonly understood as a computing approach built
upon neurons, synapses, and their dynamics, as opposed to Boolean gates, is
gaining large mindshare due to its direct application in solving current and
future computing technological problems, such as smart sensing, smart devices,
self-hosted and self-contained devices, artificial intelligence (AI)
applications, etc. In a largely software-defined implementation of neuromorphic
computing, it is possible to throw enormous computational power or optimize
models and networks depending on the specific nature of the computational
tasks. However, a hardware-based approach needs the identification of
well-suited neuronal and synaptic models to obtain high functional and energy
efficiency, which is a prime concern in size, weight, and power (SWaP)
constrained environments. In this work, we perform a study on the
characteristics of hardware neuron models (namely, inference errors,
generalizability and robustness, practical implementability, and memory
capacity) that have been proposed and demonstrated using a plethora of emerging
nano-materials technology-based physical devices, to quantify the performance
of such neurons on certain classes of problems that are of great importance in
real-time signal processing like tasks in the context of reservoir computing.
We find that the answer on which neuron to use for what applications depends on
the particulars of the application requirements and constraints themselves,
i.e., we need not only a hammer but all sorts of tools in our tool chest for
high efficiency and quality neuromorphic computing.
