“…Biological brains can perform computational tasks at an ∼100,000× efficiency compared to the digital computers. − A typical biological neuron has a surface area of ∼10 μm 2 , spends ∼10 pJ energy to generate each spike, and operates at a frequency of ∼100 Hz, which translates to a power cost of ∼1 nW for biological systems. ,, The first set of efforts in emulating biological neurons dates back to 1960s following the FN model using voltage controlled NDR devices , paired with inductors to produce relaxation oscillations similar to neuronal spiking behavior. − The inductor element is the main scaling bottleneck of this circuit implementation of spiking neuron, as coil-based passive inductors are difficult to fabricate at nanoscale with the required inductance values. The emergence of current-controlled NDR devices featuring metal–insulator phase transition materials has enabled generation of relaxation oscillations using capacitors, leading to considerable progress in artificial spiking neurons. − There have been other approaches to producing NDR, such as band to band tunneling, resonant tunneling, Gunn effect, real space electron transfer in III–V heterostructures, body biasing of MOSFET, exploiting graphene’s unique dispersion relationship near its Dirac point, using trap-based recombination processes, redox behavior of molecular junctions, and multiple circuits. ,,− Recently, we have shown that a graphene–silicon photodetector can show voltage-dependent NDR behavior under optical illumination while operating in the photovoltaic regime . This photosensor coupled with an inductive circuit element generates optically driven voltage oscillations similar to those of ganglion cells in the retina, following the FN model of spiking neurons.…”