Lam, Stanley Y.M.Shi, Bertram E.Boahen, Kwabena A2023-05-222023-05-222005-05-232009-07-13https://repository.upenn.edu/handle/20.500.14332/2804We describe an algorithm for self-organizing connections from a source array to a target array of neurons that is inspired by neural growth cone guidance. Each source neuron projects a Gaussian pattern of connections to the target layer. Learning modifies the pattern center location. The small number of parameters required to specify connectivity has enabled this algorithm's implementation in a neuromorphic silicon system. We demonstrate that this algorithm can lead to topographic feature maps similar to those observed in the visual cortex, and characterize its operation as function maximization, which connects this approach with other models of cortical map formation.Gaussian distributionneural netsself-organising feature mapsfunction maximizationguiding connectionslearning modified pattern center locationneural growth cone guidanceneuromorphic silicon systemsself-organized cortical map formationsource neuron Gaussian connection patternsource/target array self-organizing connectionstopographic feature mapsvisual cortexPresentation