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Found 6 papers, 1 papers with code
Bio-inspired learnable divisive normalization for ANNs
In this work we introduce DivNormEI, a novel bio-inspired convolutional network that performs divisive normalization, a canonical cortical computation, along with lateral inhibition and excitation that is tailored for integration into modern Artificial Neural Networks (ANNs).
Learning compact generalizable neural representations supporting perceptual grouping
Work at the intersection of vision science and deep learning is starting to explore the efficacy of deep convolutional networks (DCNs) and recurrent networks in solving perceptual grouping problems that underlie primate visual recognition and segmentation.
V1Net: A computational model of cortical horizontal connections
The primate visual system builds robust, multi-purpose representations of the external world in order to support several diverse downstream cortical processes.
Learning long-range spatial dependencies with horizontal gated recurrent units
Progress in deep learning has spawned great successes in many engineering applications.
Learning long-range spatial dependencies with horizontal gated-recurrent units
As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks.
