What is the goal of sensory coding?
Neural Computation
The Problem of Sparse Image Coding
Journal of Mathematical Imaging and Vision
Natural Computing: an international journal
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Learning Overcomplete Representations
Neural Computation
Sparse approximation of images inspired from the functional architecture of the primary visual areas
EURASIP Journal on Applied Signal Processing
A sparse generative model of v1 simple cells with intrinsic plasticity
Neural Computation
Robust Coding Over Noisy Overcomplete Channels
IEEE Transactions on Image Processing
Coding static natural images using spiking event times: do neurons Cooperate?
IEEE Transactions on Neural Networks
Motion-based prediction is sufficient to solve the aperture problem
Neural Computation
Neural associative memories and sparse coding
Neural Networks
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Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components.