Goal-directed　navigation　is　a　field　receiving　much　research　attention.　One　of　the　research　branches　involves　bottom-up　construction　of　navigation　ability　based　on　artificial　neural　network　inspired　by　neurophysiology.　Examples　include　prefrontal　cortical　network,　which　organizes　information　from　place　cells,　motor　cells　and　other　interneurons,　providing　reward　calculation　and　direct　action　readout　in　a　single　framework.　Although　this　kind　of　models　works　fine　in　small-scale　environment,　it　fails　in　large-scale　environment.　In　order　to　solve　this　problem,　we　introduced　wavefronts　propagation　method　in　the　cortical　network,　which　is　a　multi-scale　planning　method　with　low　time　complexity.　The　main　parts　of　our　model　are　reward　cells　generating　non-decaying　wavefronts,　and　interneurons　recruiting　Spike　Timing-Dependent　Plasticity　(STDP)　for　storing　preferred　direction.　Tolman　maze　tests　showed　that　robot　recruiting　this　model　has　spatial　cognition　similar　to　that　of　rats　in　Tolman＇s　experiments,　providing　a　possible　cognitive　map　formation　mechanism　Tolman　proposed.　Moreover,　our　model　is　both　scale-free　and　robust　to　neuron　noise.　©　2019　Association　for　Computing　Machinery.