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• W2160498638 abstract "Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2–4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm “Iterative Soft Thresholding” (IST) by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG." @default.
• W2160498638 created "2016-06-24" @default.
• W2160498638 creator A5079993203 @default.
• W2160498638 creator A5088598187 @default.
• W2160498638 date "2015-01-30" @default.
• W2160498638 modified "2023-09-28" @default.
• W2160498638 title "Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms" @default.
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• W2160498638 doi "https://doi.org/10.1371/journal.pone.0117023" @default.
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