Abstract: The disclosures relates to systems and methods for using a trained alignment neural network along with a trained latent representation model to achieve accurate alignment between complex neural signals arising from co-variation across neuron populations over time and their intended motor control that can be invariant for a much longer period without supervised recalibrations. In one implementation, the method may include receiving neural data for a period of time from one or more sensors. The method may further include transforming the neural data to generate aligned variables using a trained alignment network. The method may also include processing the aligned variables through a trained latent model to determine a latent spatiotemporal representation of one or more brain state variables for the period of time and decoding the latent spatiotemporal representation into a brain state for the period of time.

Abstract: Methods and systems are provided to prevent pathological overfitting in training autoencoder networks, by forcing the network to only model structure that is shared between different data variables and to enable an automatic search of hyperparameters in training autoencoder networks, resulting in automated discovery of optimally-trained models. The method may include training a neural network. The training may include applying a first binary mask to the set of training data to determine the training input data. The training may include processing the training input data by the neural network to produce network output data. The training may include determining one or more updates of the parameters based on a comparison of at least a portion of the network output data and a corresponding portion of the training data. The portion of the network output data and the corresponding portion of the training input data being inverts.

Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.

Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.

Abstract: This disclosure provides a retinal prosthetic method and device that mimics the responses of the retina to a broad range of stimuli, including natural stimuli. Ganglion cell firing patterns are generated in response to a stimulus using a set of encoders, interfaces, and transducers, where each transducer targets a single cell or a small number of cells. The conversion occurs on the same time scale as that carried out by the normal retina. In addition, aspects of the invention may be used with robotic or other mechanical devices, where processing of visual information is required. The encoders may be adjusted over time with aging or the progression of a disease.

Abstract: A brain machine interface (BMI) for restoring performance of poorly performing decoders is provided. The BMI has a decoder for decoding neural signals for controlling the brain machine interface. The decoder separates in part neural signals associated with a direction of movement and neural signals associated with a speed of movement of the brain machine interface. The decoder assigns relatively greater weight to the neural signals associated with a direction of movement.

Abstract: This disclosure provides a retinal prosthetic method and device that mimics the responses of the retina to a broad range of stimuli, including natural stimuli. Ganglion cell firing patterns are generated in response to a stimulus using a set of encoders, interfaces, and transducers, where each transducer targets a single cell or a small number of cells. The conversion occurs on the same time scale as that carried out by the normal retina. In addition, aspects of the invention may be used with robotic or other mechanical devices, where processing of visual information is required. The encoders may be adjusted over time with aging or the progression of a disease.