Title :
Adaptive compressed sensing — A new class of self-organizing coding models for neuroscience
Author :
Coulter, William K. ; Hillar, Christopher J. ; Isley, Guy ; Sommer, Friedrich T.
Author_Institution :
Univ. of California, Berkeley, CA, USA
Abstract :
Sparse coding networks, which utilize unsupervised learning to maximize coding efficiency, have successfully reproduced response properties found in primary visual cortex [1]. However, conventional sparse coding models require that the coding circuit can fully sample the sensory data in a one-to-one fashion, a requirement not supported by experimental data from the thalamo-cortical projection. To relieve these strict wiring requirements, we propose a sparse coding network constructed by introducing synaptic learning in the framework of compressed sensing. We demonstrate a new model that evolves biologically realistic, spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning must rely on an impoverished and distorted account of the original visual data. Further, we demonstrate that the model could form a general scheme of cortical communication: it can form meaningful representations in a secondary sensory area, which receives input from the primary sensory area through a “compressing” cortico-cortical projection. Finally, we prove that our model belongs to a new class of sparse coding algorithms in which recurrent connections are essential in forming the spatial receptive fields.
Keywords :
brain; data compression; eye; image coding; medical image processing; neurophysiology; physiological models; unsupervised learning; adaptive compressed sensing; feedforward connectivity; neuroscience; primary visual cortex; self-organizing coding models; sparse coding networks; spatially smooth receptive fields; synaptic learning; thalamo-cortical projection; unsupervised learning; Biological system modeling; Brain modeling; Compressed sensing; Gaussian processes; Image coding; Matching pursuit algorithms; Mathematical model; Neurons; Neuroscience; Wiring; adaptive coding; biological system modeling; image coding; nonlinear circuits; random codes;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5495209