Meta-learning Hybrid Spiking networks as physics-based nonlinear solvers for physical simulations

Abstract In the present study, a modified Leaky-Integrate and Fire (LIF) neuron model termed a Hybrid Spiking Neuron (HSN) is proposed and introduced as a physics-based meta-learning solver for applications in engineering mechanics. Unlike LIF neurons, HSNs produce a real-valued spiking signal. In each time step, the activation function determines whether the neuron is active and outputs its real-valued state, or inactive and outputs zero. On neuromorphic hardware such as Loihi 2, these neurons