In　this　paper,　a　novel　variant　of　bio-inspired　planning　algorithms　is　presented　for　robot　collision-free　path　planning　in　dynamic　environments　without　prior　information.　The　first　contribution　of　this　paper　is　that,　with　mild　technical　analysis,　the　traditional　neural　dynamic　model　almost　always　returns　a　sub-optimal　choice　in　some　challenging　scenarios,　such　as　the　boundary　map　and　the　narrow　pathway　map.　Second,　the　proposed　planning　algorithm,　namely　the　padding　mean　neural　dynamic　model,　is　a　topologically　organized　network　with　connections　among　neighbouring　neurons　and　is　good　for　spreading　nerve　impulses　such　as　a　waves　without　coupling　effects.　The　signal　transduction　method　within　a　network　is　based　on　a　dynamic　neural　activity　field,　which　propagates　high　neural　activity　from　the　target　state　to　the　whole　field,　excluding　obstacle　regions.　Third,　simulation　studies　are　conducted　to　compare　the　performance　of　the　proposed　planning　algorithm　and　other　popular　planning　algorithms　in　terms　of　effectiveness　and　efficiency.　As　a　result,　the　proposed　method　can　drive　a　robot　to　find　more　reasonable　paths　in　both　static　maps　and　unknown　dynamic　scenarios　with　moving　obstacles　and　a　moving　target.　Finally,　the　novel　excitatory　input　design　of　the　proposed　algorithm　is　discussed　and　analysed　to　explore　the　neural　stimulus　propagation　mechanism　within　the　network.