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Found 3 papers, 1 papers with code
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.
Revealing computational mechanisms of retinal prediction via model reduction
Thus overall, this work not only yields insights into the computational mechanisms underlying the striking predictive capabilities of the retina, but also places the framework of deep networks as neuroscientific models on firmer theoretical foundations, by providing a new roadmap to go beyond comparing neural representations to extracting and understand computational mechanisms.
Recurrent Segmentation for Variable Computational Budgets
Importantly, the RNN may be deployed across a range of computational budgets by merely running the model for a variable number of iterations.
