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- W2614698515 abstract "In a spiking neural network (SNN), individual neurons operate autonomously and only communicate with other neurons sparingly and asynchronously via spike signals. These characteristics render a massively parallel hardware implementation of SNN a potentially powerful computer, albeit a non von Neumann one. But can one guarantee that a SNN computer solves some important problems reliably? In this paper, we formulate a mathematical model of one SNN that can be configured for a sparse coding problem for feature extraction. With a moderate but well-defined assumption, we prove that the SNN indeed solves sparse coding. To the best of our knowledge, this is the first rigorous result of this kind." @default.
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- W2614698515 date "2017-05-15" @default.
- W2614698515 modified "2023-09-27" @default.
- W2614698515 title "Sparse Coding by Spiking Neural Networks: Convergence Theory and Computational Results" @default.
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