Deep　neural　networks　(DNNs)　have　proved　to　be　remarkably　successful　in　various　domains,　in　particular　for　implementing　complex　functions　and　performing　sophisticated　tasks.　However,　their　vulnerability　to　adversarial　noise　undermines　their　reliability　for　safety-critical　tasks.　Despite　attempts　to　improve　the　robustness　using　algorithmic　approaches,　an　effective　hardware　implementation　is　still　lacking.　Here　an　artificial　probabilistic　neuron　device　is　proposed　based　on　arrays　of　coupled　nanomagnets,　referred　to　as　artificial　spin　ices,　which　return　a　nonlinear　function　with　built-in　stochasticity　in　response　to　an　ultrafast　laser-induced　excitation.　By　exploiting　solid-state　ionic　gating,　the　magnetic　coupling　is　electrically　modulated,　as　a　result　of　the　magnetic　anisotropy-mediated　competition　of　the　symmetric　exchange　interaction　and　Dzyaloshinskii-Moriya　interaction,　and　hence　tune　the　stochastic　property　of　the　neuron　device　at　run-time.　Stochastic　DNNs　are　then　constructed　with　an　output　layer　comprising　several　of　probabilistic　neuron　devices.　Compared　to　conventional　DNNs,　the　stochastic　DNNs　exhibit　an　order　of　magnitude　greater　resistance　to　adversarial　noise,　providing　a　significant　improvement　in　robustness.　This　approach　opens　the　way　to　more　secure　and　reliable　DNNs,　enabling　broader　uses　in　real-world　applications.　©　2024　Wiley-VCH　GmbH.