  Spiking Neural Networks (SNNs) are gaining widespread momentum in the field
of neuromorphic computing. These network systems integrated with neurons and
synapses provide computational efficiency by mimicking the human brain. It is
desired to incorporate the biological neuronal dynamics, including complex
spiking patterns which represent diverse brain activities within the neural
networks. Earlier hardware realization of neurons was (1) area intensive
because of large capacitors in the circuit design, (2) neuronal spiking
patterns were demonstrated with clocked neurons at the device level. To achieve
more realistic biological neuron spiking behavior, emerging memristive devices
are considered promising alternatives. In this paper, we propose, PrMnO3(PMO)
-RRAM device-based neuron. The voltage-controlled electrothermal timescales of
the compact PMO RRAM device replace the electrical timescales of charging a
large capacitor. The electrothermal timescale is used to implement an
integration block with multiple voltage-controlled timescales coupled with a
refractory block to generate biological neuronal dynamics. Here, first, a
Verilog-A implementation of the thermal device model is demonstrated, which
captures the current-temperature dynamics of the PMO device. Second, a driving
circuitry is designed to mimic different spiking patterns of cortical neurons,
including Intrinsic bursting (IB) and Chattering (CH). Third, a neuron circuit
model is simulated, which includes the PMO RRAM device model and the driving
circuitry to demonstrate the asynchronous neuron behavior. Finally, a
hardware-software hybrid analysis is done in which the PMO RRAM device is
experimentally characterized to mimic neuron spiking dynamics. The work
presents a realizable and more biologically comparable hardware-efficient
solution for large-scale SNNs.
