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Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
Guy D Isely · Christopher J Hillar · Fritz Sommer

Wed Dec 08 05:45 PM -- 05:50 PM (PST) @ Regency Ballroom

A new algorithm is proposed for a) unsupervised learning of sparse representations from subsampled measurements and b) estimating the parameters required for linearly reconstructing signals from the sparse codes. We verify that the new algorithm performs efficient data compression on par with the recent method of compressive sampling. Further, we demonstrate that the algorithm performs robustly when stacked in several stages or when applied in undercomplete or overcomplete situations. The new algorithm can explain how neural populations in the brain that receive subsampled input through fiber bottlenecks are able to form coherent response properties.

Author Information

Guy D Isely (UC Berkeley)
Christopher J Hillar (Redwood Center for Theoretical Neuroscience)
Fritz Sommer (UC Berkeley)

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