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• W2047219060 abstract "Large cortical models based on differential equations may require significant computations to converge, in addition tothe computations required to simulate learning. Fortunately, sensitivity analysis for such models can be done using theimplicit function theorem (IFT), as shown by McFadden in 1993 for a model with virtual lateral (VLI) inwhich inhibition is based on competition for activation, rather than on direct reduction of activation levels. The currentwork reviews recent neurobiological work on the nature of inhibition, and also reports new results on numerical issuesthat arise in the analysis of VLI models of cortical networks. The IFT technique is at least an order of magnitude fasterthan numerical ODE solvers. A new explanation for inhibition based on energy resource sharing is proposed." @default.
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• W2047219060 date "2007-04-09" @default.
• W2047219060 modified "2023-09-23" @default.
• W2047219060 title "Implicit differential analysis for cortical models" @default.
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• W2047219060 doi "https://doi.org/10.1117/12.725209" @default.
• W2047219060 hasPublicationYear "2007" @default.
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