From biological vision to unsupervised hierarchical sparse coding

The formation of connections between neural cells is essentially emerging from an unsupervised learning process. During the development of primary visual cortex (V1) of mammals, for example, one may observe the emergence of cells selective to localized and oriented features. This leads to the development of a rough contour-based representation of the retinal image in area V1. We modeled the formation of this representation along the thalamo-cortical pathway using a sparse unsupervised learning algorithm in a hierarchical network. This algorithm alternates (i) a coding phase to encode the information and (ii) a learning phase to find the proper encoder (also called dictionary). We replicated and adapted the Multi-Layer Convolutional Sparse Coding (ML-CSC) model from Michael Elad's group~\cite{Sulam2017}. As an application, we have trained our implementation on a database containing images from faces. The extracted features show similarities with some of the neuron's receptive field found in V1 and beyond. Furthermore, our results demonstrate the potential application of such a strategy to the fast classification of images, for example in hierarchical and dynamical architectures.


All material (c) L. Perrinet. Please check the copyright notice.

This work was supported by the Doc2Amu project which received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713750. Projet cofinancé par le Conseil Régional Provence-Alpes-Côte d’Azur.Projet cofinancé par le Conseil Régional Provence-Alpes-Côte d’Azur, la commission européenne et les Investissements d'Avenir.

This work was supported by the Ph.D. program in Integrative and Clinical Neuroscience which received funding by the Aix-Marseille Université through the prestigious status of "Excellence Initiative" (A*MIDEX) awarded by the French Government. Ph.D. program in Integrative and Clinical Neuroscience.

TagYear18 TagPublicationsProceedings TagDoc2Amu TagSparse

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