Antiferromagnetic artificial neuron modeling of biological neural networks

Replicating neural responses observed in biological systems using artificial neural networks has applications in both medicine and engineering. Antiferromagnetic (AFM) artificial neurons based on spin Hall oscillators have many features that closely resemble biological neurons [1], making them a good candidate for biological neural network modeling. Here we use the AFM neuron model to simulate the biological neural network responsible for the withdrawal reflex, which protects the self from harmful stimuli. The withdrawal reflex is a polysynaptic spinal reflex that responds to sensory stimuli by flexing and relaxing the opposing muscles in the same limb [2]. We build an artificial neural network from AFM neurons that simulates the biological neural network responsible for this reflex. The unique features of AFM neurons, such as inhibition that stems from an effective AFM inertia, allow for the creation of biologically realistic neural network components, like the interneurons in the spinal cord. The effectiveness of AFM neuron modeling is proven by simulating various scenarios that define the withdrawal reflex.





[1] H. Bradley, et al, AIP Advances 13, 015206 (2023)



[2] S. Schmidt, Human Anatomy & Physiology, p. 470.