TRAINING ARTIFICIAL NEURAL NETWORKS BASED ON SYNAPTIC CONNECTIVITY GRAPHS

    公开(公告)号:US20210201131A1

    公开(公告)日:2021-07-01

    申请号:US16731186

    申请日:2019-12-31

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a student neural network. In one aspect, there is provided a method comprising: processing a training input using the student neural network to generate a student neural network output comprising a respective score for each of a plurality of classes; processing the training input using a brain emulation neural network to generate a brain emulation neural network output comprising a respective score for each of the plurality of classes; and adjusting current values of the student neural network parameters using gradients of an objective function that characterizes a similarity between: (i) the student neural network output for the training input, and (ii) the brain emulation neural network output for the training input.

    PREDICTING NEURON TYPES BASED ON SYNAPTIC CONNECTIVITY GRAPHS

    公开(公告)号:US20210201111A1

    公开(公告)日:2021-07-01

    申请号:US16776579

    申请日:2020-01-30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining an artificial neural network architecture corresponding to a sub-graph of a synaptic connectivity graph. In one aspect, there is provided a method comprising: obtaining data defining a graph representing synaptic connectivity between neurons in a brain of a biological organism; determining, for each node in the graph, a respective set of one or more node features characterizing a structure of the graph relative to the node; identifying a sub-graph of the graph, comprising selecting a proper subset of the nodes in the graph for inclusion in the sub-graph based on the node features of the nodes in the graph; and determining an artificial neural network architecture corresponding to the sub-graph of the graph.

    Electroencephalogram bioamplifier
    15.
    发明授权

    公开(公告)号:US10952680B2

    公开(公告)日:2021-03-23

    申请号:US15855870

    申请日:2017-12-27

    Abstract: A bioamplifier for analyzing electroencephalogram (EEG) signals is disclosed. The bioamplifier includes an input terminal for receiving an EEG signal from a plurality of sensors coupled to a user. The bioamplifier also includes an analogue-to-digital converter arranged to receive the EEG signal from the input terminal and convert the EEG signal to a digital EEG signal. A data processing apparatus within the bioamplifier is arranged to receive the digital EEG signal from the analogue-to-digital converter and programmed to process, in real time the digital EEG signal using a first machine learning model to generate a cleaned EEG signal having a higher signal-to-noise ratio than the digital EEG signal. The bioamplifier further includes a power source to provide electrical power to the analogue-to-digital converter and the data processing apparatus. The bioamplifier includes a housing that contains the analogue-to-digital converter, the data processing apparatus, the power source, and the sensor input.

    Multi-view display with viewer detection

    公开(公告)号:US10558264B1

    公开(公告)日:2020-02-11

    申请号:US15386691

    申请日:2016-12-21

    Abstract: Methods, systems, and apparatus for generating images that blend an appearance of a display with an environment of the display. In some aspects, output is provided from a display that occludes an object. A gaze direction is determined for an observer located within an environment of the display. An image is generated based on the determined gaze direction of the observer. The generated image is configured to blend an appearance of the display with the environment of the display. The generated image is displayed on the display directed to the observer.

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