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Meaningful representations emerge from Sparse Deep Predictive Coding ↗
Sparse coding strategies have been successfully applied by neuroscientists to model some properties of the visual cortex [ 9 , 10 ] . Recent advances have shown that these models are related to a wide range of mathematical methods to extract sparse representations [ 11 ] , yet most often with a sing…
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Sparse coding strategies have been successfully applied by neuroscientists to model some properties of the visual cortex [ 9 , 10 ] . Recent advances have shown that these models are related to a wide range of mathematical methods to extract sparse representations [ 11 ] , yet most often with a single processing layer. Interestingly, Predictive Coding (PC), a theory formalized by Rao & Ballard [ 12 ] to model contextual effects in a population of V1 neurons, gives a framework to combine sparse coding with a hierarchical structure [ 13 ] . Indeed, PC suggests that the brain uses Bayesian inference to consistently update an internal model of the world that describes the possible causes of a given sensory input [ 14 ] . Whereas the vast majority of CNN are mainly feed-forward, PC introduces recurrent connections combining top-down and bottom-up processes.