Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication [article]

Guy Isely, Christopher J. Hillar, Friedrich T. Sommer
2010 arXiv   pre-print
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
more » ... gorithm can explain how neural populations in the brain that receive subsampled input through fiber bottlenecks are able to form coherent response properties.
arXiv:1011.0241v1 fatcat:jkqbu2yg3zduhjegopuq2pdbhe