Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a reservoir computing neural network. In one aspect there is provided a reservoir computing neural network comprising: (i) a brain emulation sub-network, and (ii) a prediction sub-network. The brain emulation sub-network is configured to process the network input in accordance with values of a plurality of brain emulation sub-network parameters to generate an alternative representation of the network input. The prediction sub-network is configured to process the alternative representation of the network input in accordance with values of a plurality of prediction sub-network parameters to generate the network output. The values of the brain emulation sub-network parameters are determined before the reservoir computing neural network is trained and are not adjusting during training of the reservoir computing neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an artificial neural network architecture based on a synaptic connectivity graph. According to one aspect, there is provided a method comprising: obtaining a synaptic resolution image of at least a portion of a brain of a biological organism; processing the image to identify: (i) a plurality of neurons in the brain, and (ii) a plurality of synaptic connections between pairs of neurons in the brain; generating data defining a graph representing synaptic connectivity between the neurons in the brain; determining an artificial neural network architecture corresponding to the graph representing the synaptic connectivity between the neurons in the brain; and processing a network input using an artificial neural network having the artificial neural network architecture to generate a network output.

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 an output for the training input; processing the student neural network output using a discriminative neural network to generate a discriminative score for the student neural network output, wherein the discriminative score characterizes a prediction for whether the network input was generated using: (i) the student neural network, or (ii) a brain emulation neural network; and adjusting current values of the student neural network parameters using gradients of an objective function that depends on the discriminative score for the student neural network output.

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.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a neural network architecture for performing a machine learning task. In one aspect, a method comprises: obtaining data defining a synaptic connectivity graph representing synaptic connectivity between neurons in a brain of a biological organism; generating data defining a plurality of candidate graphs based on the synaptic connectivity graph; determining, for each candidate graph, a performance measure on a machine learning task of a neural network having a neural network architecture that is specified by the candidate graph; and selecting a final neural network architecture for performing the machine learning task based on the performance measures.

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.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. Information associated with two or more options is presented to a user. EEG signals from a sensor coupled to the user are received contemporaneously to the user receiving information associated with the two or more options. The EEG signals are processed in real time to determine which one of the options was selected by the user. In response to determining which one of the options was selected by the user, an action from one or more possible actions associated with the information presented to the user is selected. An output associated with the selected action is then generated.

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.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. EEG signals are received from a sensor coupled to a user. Contextual information from one or both of the user and the user's environment is also received. The EEG signals are processed in real time using a machine learning model to predict an action of the user, which is associated with the contextual information. Output associated with the predicted action is then generated.

Abstract: Closed-loop neurostimulation of large-scale brain networks includes a neurostimulation headset having at least two ultrasound transducer modules configured to generate within a first time period, a first focused ultrasound wave at a region within a portion of a subject's brain, one or more sensors configured to measure, within the first time period, a response from the portion of the subject's brain in response to the first focused ultrasound wave, and an electronic controller in communication with the at least two emitters and the one or more sensors configured to dynamically adjust, based on the measured response from the portion of the subject's brain, a power level of one or more of the at least two ultrasound transducer modules to generate a second focused ultrasound wave at the region within the portion of the subject's brain.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for synchronizing neuroelectric measurements with diagnostic content presentation by performing actions that include causing a presentation system to present diagnostic content a user, where the diagnostic content includes: a first content frame prompting the user to perform a physical task, a second content frame including electroencephalogram (EEG) synchronization content, the second content frame sequentially following the first content frame, and a third content frame indicating an outcome of the physical task, the third content frame sequentially following the second content frame. The actions include obtaining, from a brainwave sensor, EEG signals of the user during presentation of the diagnostic content. The actions include providing the EEG signals as input features to a machine learning model that is trained to predict a psychological state of the user based on the EEG signals.

Abstract: Methods, systems, and computer programs encoded on a computer storage medium, for improving EEG measurements by identifying artifacts present in EEG measurements and providing a real-time indication to a user of likely artifacts in EEG measurements are described. EEG measurements of a patient can be obtained by placing a wearable device or EEG cap on a patient's head. Sensors in the cap provide EEG data to a computing device that processes the data to identify one or more artifacts in the EEG data. The artifacts can be identified by conducting one or more operations of determining the signal to noise ratio of the line noise, calculating mutual information between sensor pairs, and applying the p-welch method. Based on the types of artifacts identified, the computing device can output an indicator that provides feedback to the technician performing an EEG test to make adjustments to the test setup.

Abstract: A method for obtaining an electroencephalogram (EEG) of a user is disclosed. A reference sensor is attached to the user by connecting a first component of the reference sensor to a second component of the reference sensor, at least a portion of the first component being sub-dermally implanted on or adjacent to a mastoid process of the user. At least one active sensor is attached to the user. A first signal is detected from the reference sensor simultaneously as a second signal is detected from the at least one active sensor. The EEG is obtained based on the first signal and the second signal.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. Information associated with two or more options is presented to a user. EEG signals from a sensor coupled to the user are received contemporaneously to the user receiving information associated with the two or more options. The EEG signals are processed in real time to determine which one of the options was selected by the user. In response to determining which one of the options was selected by the user, an action from one or more possible actions associated with the information presented to the user is selected. An output associated with the selected action is then generated.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for causing a stimulus presentation system to present first content to a patient. Obtaining, from a brainwave sensor, electroencephalography (EEG) signals of the patient while the first content is being presented to the patient. Identifying, from within the EEG signals of the patient, first brainwave signals associated with a first brain system of the patient, the first brainwave signals representing a response by the patient to the first content. Determining, based on providing the first brainwave signals as input features to a machine learning model, a likelihood that the patient will experience a type of depression within a period of time. Providing, for display on a user computing device, data indicating the likelihood that the patient will experience the type of depression within the period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining feature sets for a first number of diagnostic trials performed with a patient for diagnostic testing, wherein each feature set includes one or more features of electroencephalogram (EEG) signals measured from the patient while the patient is presented with trial content known to stimulate one or more desired human brain systems. Iteratively providing different combinations of the feature sets as input data to a diagnostic machine learning model to obtain model outputs, each model output corresponding to a particular one of the combinations. Determining, based on the model outputs, a consistency metric, the consistency metric indicating whether a quantity of feature sets in the combinations is sufficient to produce accurate output from the diagnostic machine learning model. Selectively ending the diagnostic testing with the patient based on a value of the consistency metric.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for causing a stimulus presentation system to present content to a patient. Obtaining, from a brainwave sensor, electroencephalography (EEG) signals of the patient while the content is being presented to the patient. Identifying, from within the EEG signals of the patient, brainwave signals associated with a brain system of the patient, the brainwave signals representing a response by the patient to the content. Determining, based on providing the brainwave signals input features to a machine learning model, a likelihood that the patient will experience symptoms of anxiety within a period of time. Providing, for display on a user computing device, data indicating the likelihood that the patient will experience the symptoms of anxiety within the period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting a human participant with information known to stimulate a person's neural reward system. Receiving an EEG signal from a sensor coupled to the human participant in response to presenting the participant with the information, the EEG signal being associated with the participant's neural reward system. Contemporaneously with receiving the EEG signal, receiving contextual information related to the information presented to the human participant. Processing the EEG signal and the contextual information in real time using a machine learning model trained to associate depression in the person with EEG signals associated with the person's neural reward system and the presented information. Diagnosing whether the participant is experiencing depression based on an output of the machine learning model.

Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. Information associated with two or more options is presented to a user. EEG signals from a sensor coupled to the user are received contemporaneously to the user receiving information associated with the two or more options. The EEG signals are processed in real time to determine which one of the options was selected by the user. In response to determining which one of the options was selected by the user, an action from one or more possible actions associated with the information presented to the user is selected. An output associated with the selected action is then generated.