  We introduce a new approach for solving forward systems of differential
equations using a combination of splitting methods and physics-informed neural
networks (PINNs). The proposed method, splitting PINN, effectively addresses
the challenge of applying PINNs to forward dynamical systems and demonstrates
improved accuracy through its application to neuron models. Specifically, we
apply operator splitting to decompose the original neuron model into
sub-problems that are then solved using PINNs. Moreover, we develop an $L^1$
scheme for discretizing fractional derivatives in fractional neuron models,
leading to improved accuracy and efficiency. The results of this study
highlight the potential of splitting PINNs in solving both integer- and
fractional-order neuron models, as well as other similar systems in
computational science and engineering.
