A　novel　neural　network　model　S-LINN　was　proposed　based　on　the　neurons　connection　and　lateral　inhibition　mechanism　in　the　cortex.　Considering　the　multilayer　and　span　connection　of　cortex　neurons,　the　model　was　used　to　simulate　the　cerebral　cortex　structure.　The　approximation　ability　was　verified　throuth　the　universial　approximate　analysis.　Meanwhile,　a　supervised　algorithm　based　on　the　error　back-propagation　and　gradient　descent　theory　was　developed　to　train　the　network　parameters.　Simulation　results　for　the　abalone　age　prediction　demonstrate　that　the　proposed　model　can　achieve　higher　accuracy　of　approximation　and　generalization　with　a　comparable　compact　network　structure.