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

2010/11/01 by Guy Isely, Christopher J. Hillar, Isely, Guy +2
Engineering · Neuroscience · #FOS: Biological sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1011.0241

openalex publication_date 2010/11/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

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.

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