COMPRESSED MATRIX REPRESENTATIONS OF NEURAL NETWORK ARCHITECTURES BASED ON SYNAPTIC CONNECTIVITY

    公开(公告)号:US20230004791A1

    公开(公告)日:2023-01-05

    申请号:US17364444

    申请日:2021-06-30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks using compressed matrix representations. One of the methods includes obtaining a network input; and processing the network input using a neural network to generate a network output, comprising: processing the network input using an input subnetwork of the neural network to generate an embedding of the network input; and processing the embedding of the network input using a brain emulation subnetwork of the neural network, wherein the brain emulation subnetwork has a brain emulation neural network architecture that represents synaptic connectivity between a plurality of biological neurons in a brain of a biological organism, the processing comprising: obtaining a compressed matrix representation of a sparse matrix of brain emulation parameters; and applying the compressed matrix representation to the embedding of the network input to generate a brain emulation subnetwork output.

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