On　the　basis　of　the　connective　mode　and　information　transmit　mechanism　of　cerebral　cortex,　a　novel　lateral　inhibition　neural　network　model　with　span　connection(S-LINN)　is　proposed.　Combining　the　liminar　organization　of　cerebral　cortex,　and　fully　considering　the　lateral　inhibition　connection　between　interneurons　and　the　pyramidal　neurons,　the　proposed　S-LINN　transforms　information　to　other　neurons　in　different　layers,　which　is　used　to　enhance　response　contrast　and　advance　the　network　representation,　respectively.　The　effectiveness　and　superiority　of　the　proposed　network　is　compared　with　other　popular　approaches　on　two　benchmark　problems　in　the　areas　of　real-world　regression　and　classification　problems.　Simulation　results　show　that　the　proposed　S-LINN　can　achieve　the　higher　accuracy　of　approximation　and　generalization　with　the　comparably　compact　network　structure.