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.