California Institute of Technology

University of California

University of Louisville Research Foundation, Inc.

Neurorecovery Technologies, Inc.

Thomas Anthony Desautels

Joel W Burdick

Yu-Chong Tai

Victor Reggie Edgerton

Yury P Gerasimenko

Susan Jill Harkema

Roland R Roy

Claudia A Angeli

Mandheerej S Nandra

Jonathan Hodes

John F Naber

Robert S Keynton

Nicholas A Terrafranca

Douglas J Jackson

Steven L Upchurch

Yangsheng Chen

Yangshen Chen

Andreas Krause

Jr Nicholas A Terrafranca

Methods of enabling locomotor control, postural control, voluntary control of body movements (e.g., in non-weight bearing conditions), and/or autonomic functions in a human subject having a spinal cord injury, a brain injury, or a neurological neuromotor disease are described.

High density epidural stimulation for facilitation of locomotion, posture, voluntary movement, and recovery of autonomic, sexual, vasomotor, and cognitive function after neurological injury

Methods comprising applying electrical stimulation to patients in conjunction with physical training are described.

Neurostimulator devices are described. An example neurostimulator device includes a stimulation assembly connectable to a plurality of electrodes, wherein the plurality of electrodes are configured to stimulate a spinal cord. The neurostimulator device also includes an interface and at least one processor configured to modify at least one complex stimulation pattern deliverable by the plurality of electrodes by integrating data from the interface and performing a machine learning algorithm on the at least one complex stimulation pattern.

Neurostimulator

Neurostimulator devices are described comprising: a stimulation assembly connectable to a plurality of electrodes, wherein the plurality of electrodes are configured to stimulate a spinal cord; one or more sensors; and at least one processor configured to modify at least one complex stimulation pattern deliverable by the plurality of electrodes by integrating data from the one or more sensors and performing a machine learning method implementing a Gaussian Process Optimization on the at least one complex stimulation pattern. Methods of use are also described.

Neurostimulator devices using a machine learning method implementing a gaussian process optimization

Methods of enabling locomotor control, postural control, voluntary control of body movements (e.g., in non-weight bearing conditions), and/or autonomic functions in a human subject having spinal cord injury, brain injury, or neurological neuromotor disease are described.

Described are devices including stimulation assemblies connectable to a plurality of electrodes. The plurality of electrodes can be configured to connect to a spinal cord at a location below a lesion of the spinal cord. The stimulation assembly can be configured to deliver stimulation to selected ones of the plurality of electrodes when the stimulation assembly is connected to the plurality of electrodes when located below the lesion of the spinal cord. Methods of using the devices are also described.

Spinal stimulator systems for restoration of function

A method and system for selecting a batch of input data from available input data for parallel evaluation by a function is disclosed. The function is modeled as drawn from a Gaussian process. Observations are used to determine a mean and a variance of the modeled function. An upper confidence bound is determined from the determined mean and variance. A decision rule is applied to select input data from the available input data to add to the batch of input data. The selection of the input data is based on a domain-specific time varying parameter. Intermediate observations are hallucinated within the batch. The hallucinated observations are used with the decision rule to select subsequent input data from the available input data for the batch of input data. The input data of the batch is evaluated in parallel with the function. The resulting determined data outputs are stored.

Method and system for parallel batch processing of data sets using Gaussian process with batch upper confidence bound

Methods of enabling locomotor control, postural control, voluntary control of body movements (e.g., in non-weight bearing conditions), and/or autonomic functions in a human subject having spinal cord injury, brain injury, or neurological neuromotor disease. In certain embodiments, the methods involve stimulating the spinal cord of the subject using an epidurally placed electrode array, subjecting the subject to physical training thereby generating proprioceptive and/or supraspinal signals, and optionally administering pharmacological agents to the subject. The combination of stimulation, physical training, and optional pharmacological agents modulate in real time electrophysiological properties of spinal circuits in the subject so they are activated by supraspinal information and/or proprioceptive information derived from the region of the subject where locomotor activity is to be facilitated.

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Pasadena, CA, United States of America

Thomas Anthony Desautels