Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data CompressionSimilar Papers 제목 키워드 기반
Reconstruction-free Cascaded Adaptive Compressive Sensing
Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-s…
Compressive SensingImage ReconstructionCompressive adaptive computational ghost imaging
Compressive sensing is considered a huge breakthrough in signal acquisition. It allows recording an image consisting of $N^2$ pixels using much fewer than $N^2$ measurements if it can be transformed to a basis where most…
Compressive SensingSparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data
Radar-based perception has gained increasing attention in autonomous driving, yet the inherent sparsity of radars poses challenges. Radar raw data often contains excessive noise, whereas radar point clouds retain only li…
Autonomous Drivingobject-detectionObject DetectionAdaptive and Cascaded Compressive Sensing
Scene-dependent adaptive compressive sensing (CS) has been a long pursuing goal which has huge potential in significantly improving the performance of CS. However, without accessing to the ground truth image, how to desi…
Compressive SensingCompression, Restoration, Re-sampling, Compressive Sensing: Fast Transforms in Digital Imaging
Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, i…
Compressive SensingImage CompressionImage ReconstructionImage Restoration