Noise　permeates　every　level　of　the　nervous　systems,　from　single　neurons　to　the　whole　system.　However,　the　noise　seems　not　to　take　so　much　trouble　to　neural　information　processing　in　contrast　to　the　electronic　system.　The　robustness　of　neural　information　processing　inspired　us　to　study　the　method　of　neuromorphic　hardware　systems　to　resist　electromagnetic　interference.　The　dynamics　of　the　neuron　model　can　reflect　the　inherent　mechanism　of　neural　information　processing.　In　this　work,　we　take　the　Maeda-Makino　(MM)　hardware　neuron　as　an　example　to　study　the　robustness　mechanism　of　biological　neuron　information　encoding　based　on　symbolic　dynamics.　MM　hardware　neuron　is　a　neuromorphic　neuron　circuit　with　biological　plausibility.　The　simulation　results　show　that　MM　hardware　neuron　can　encode　the　order　of　current　stimulus　periods,　even　with　parameter　disturbance　and　noisy　input　signals　which　simulate　the　effects　of　electromagnetic　interference　on　neuronal　cell　membrane　permeability,　sodium　ion　channels,　potassium　ion　channels,　and　action　potentials.　Choosing　a　robust　hardware　neuron　is　the　basis　for　building　a　neuromorphic　hardware　system　that　can　resist　electromagnetic　interference.　This　letter　not　only　verifies　the　robustness　of　using　symbol　sequence　encoding　but　also　provides　an　optional　hardware　neuron　solution　for　neuromorphic　hardware　designed　for　anti-electromagnetic　interference.　©　2019　IEEE.