Significant　advancements　in　artificial　neural　networks　(ANNs)　have　driven　the　rapid　progress　of　artificial　intelligence　and　machine　learning.　While　current　feedforward　neural　networks　primarily　handle　static　data,　recurrent　neural　networks　(RNNs)　are　designed　for　dynamical　systems.　However,　RNNs　demand　extensive　training　on　specific　tasks,　limiting　their　scalability　and　affordability　for　edge　computing.　Physical　reservoir　computing　(RC)　offers　an　alternative　approach　by　mapping　inputs　into　high-dimensional　states,　allowing　for　pattern　analysis　within　a　fixed　reservoir.　Unlike　RNNs,　RC　is　well-suited　for　temporal　and　sequential　data　processing　with　rapid　speed　and　low　training　costs.　This　makes　RC　suitable　for　hardware　implementation　across　various　research　domains.　Nonetheless,　existing　demonstrations　of　RC　remain　constrained　to　small-scale　device　arrays.　As　electronic　synapse　arrays　aim　to　approach　very　large-scale　and　highly　complex　hardware　as　in　the　human　brain,　managing　heat　dissipation　becomes　a　formidable　challenge.　In　this　work,　we　successfully　developed　the　neuristors　based　on　textured　h-BN　films,　prepared　using　a　CMOS-compatible　technique,　and　constructed　a　physical　RC　system　based　on　as-fabricated　devices.　Our　approach　leverages　vertically　aligned　BN　to　provide　aligned　diffusion　paths　for　the　reproducible　migration　process　of　metal　ions　from　the　electrodes　and　offers　a　potential　solution　for　thermal　management　in　electronic　devices.　This　achievement　highlights　the　promising　potential　of　our　neuristors　for　future　high-density　and　energy-efficient　neuromorphic　computing.　©　2024　Elsevier　B.V.