Adaptive compressed sensing - A new class of self-organizing coding models for neuroscience

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 that the new model evolves biologically realistic spatially smooth receptive fields despite the fact that the feedforward connectivity subsamples the input and thus the learning has to 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...

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: William K. Coulter, Cristopher J. Hillar, Guy Isley, Friedrich T. Sommer
• Keywords: ICASSP 2010, Primary Visual Cortex, Signal Processing, Sparse Coding, Sparse Coding Networks