LAUSR

Spintronic neurons which emit sharp voltage spikes are required for the realization of hardware neural networks enabling fast data processing with low-power consumption. In many neuroscience and computer science models, neurons are abstracted as non-linear oscillators. Magnetic nano-oscillators called spin-torque nano-oscillators are interesting candidates for imitating neurons at nanoscale. These oscillators, however, emit sinusoidal waveforms without spiking while biological neurons are relaxation oscillators that emit sharp voltage spikes. Here we propose a simple way to imitate neuron spiking in high-magnetoresistance nanoscale spin valves where both magnetic layers are free and thin enough to be switched by spin torque. Our numerical-simulation results show that the windmill motion induced by spin torque in the proposed spintronic neurons gives rise to spikes whose shape and frequency, set by the charging and discharging times, can be tuned through the amplitude of injected dc current. We also found that these devices can exhibit chaotic oscillations. Chaotic-like neuron dynamics has been observed in the brain, and it is desirable in some neuromorphic computing applications whereas it should be avoided in others. We demonstrate that the degree of chaos can be tuned in a wide range by engineering the magnetic stack and anisotropies and by changing the dc current. The proposed spintronic neuron is a promising building block for hardware neuromorphic chips leveraging non-linear dynamics for computing.