Antiferromagnetic artificial neuron modeling of the withdrawal reflex

• Published in: Journal of computational neuroscience Vol. 52; no. 3; pp. 197 - 206
• Main Authors: Bradley, Hannah, Quach, Lily, Louis, Steven, Tyberkevych, Vasyl
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2024; Springer Nature B.V
• Subjects: Antiferromagnetism; Artificial neural networks; Biological effects; Biomedical and Life Sciences; Biomedicine; Effectiveness; Human Genetics; Interneurons; Modelling; Motor neurons; Neural networks; Neurology; Neurons; Neurosciences; Pain perception; Sensory neurons; Sensory stimuli; Spin dynamics; Spinal cord; Stimuli; Theory of Computation; Artificial neuron; Biological system modeling; Neuroanatomy; Antiferromagnets; Artificial neural networks
• ISSN: 0929-5313; 1573-6873; 1573-6873
• DOI: 10.1007/s10827-024-00873-3

Replicating neural responses observed in biological systems using artificial neural networks holds significant promise in the fields of medicine and engineering. In this study, we employ ultra-fast artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators to emulate the biological withdrawal reflex responsible for self-preservation against noxious stimuli, such as pain or temperature. As a result of utilizing the dynamics of AFM neurons, we are able to construct an artificial neural network that can mimic the functionality and organization of 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 and antagonist motor neurons. To showcase the effectiveness of AFM neuron modeling, we conduct simulations of various scenarios that define the withdrawal reflex, including responses to both weak and strong sensory stimuli, as well as voluntary suppression of the reflex.