Leland Stanford Junior University

Columbia University

University of Cambridge

Realius, Inc.

John Patrick Cunningham

Krishna V Shenoy

Paul Nuyujukian

Mark M Churchland

Jonathan C Kao

Sean C Perkins

Byron M Yu

Vikash Gilja

Cynthia A Chestek

Stephen I Ryu

Chuck Teller

Tim Murphy

Karen Schroeder

Mechanisms including: receiving a first set of observed spike counts (FSoOSCs) for the spiking cells; determining a set of probabilities (SoPs) by: retrieving the SoPs from stored information (SI); or calculating the SopS based on the SI, wherein the SI regards possible biological states (BSs) of a subject, wherein each of the possible BSs belongs to at least one of a plurality of time sequences (PoTSs) of BSs, wherein each of the PoTSs of BSs corresponds to a possible action of the subject, and wherein each probability in the set of probabilities indicates a likelihood of observing a possible spike count for one of the plurality of spiking cells; identifying using a hardware processor a first identified BS of the subject from the possible BSs based on the FSoOSCs and the set of probabilities; and determining an action to be performed based on the first identified BS.

Systems, methods, and media for decoding observed spike counts for spiking cells

A brain-machine interface is provided that incorporates a neural dynamical structure in the control of a prosthetic device to restore motor function and is able to significantly enhance the control performance compared to existing technologies. In one example, a neural dynamical state is inferred from neural observations, which are obtained from a neural implant. In another example, the neural dynamical state can be inferred from both the obtained neural observations and from the kinematics. A controller interfaced with the prosthetic device uses the inferred neural dynamical state as input to the controller to control kinematic variables of the prosthetic device.

Brain machine interfaces incorporating neural population dynamics

Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

Brain machine interface

Games and devices for playing games based on market simulations are described. The games may use real market data or may simulate markets using real data. Other games may use guesses from a large number of other players to simulate the market. One embodiment is described where real estate markets are the basis for a game. The value of the real estate may be determined by actual sales prices or valuations, by automated valuation models of the sales or valuation of similar items, or from a 'wisdom of the crowd' algorithm. In one embodiment, the wisdom of the crowd algorithm uses a segmented authority model to make the pricing algorithm more robust.

Market-based simulation game and system

Probability densities are calculated. According to an example embodiment, a Gaussian probability density is computed for a set of data representing a multivariate Gaussian distribution (Gaussian) by matching moments of truncated distributions. A distribution is projected onto dimensions of the set of data to generate cavity-local Gaussians. For the set of data, site-local univariate Gaussians are selected so that the moments match cavity-local univariate Gaussians (the moments of the product of the cavity-local Gaussians and the selected site-local univariate Gaussians, match the moments of the product of the cavity-local Gaussians and the cavity-local truncations). The selected site-local univariate Gaussians are aggregated globally to form a new global Gaussian.

System and method for computing cumulative densities of data represented by gaussian distributions

Growing community of inventors

Saratoga, CA, United States of America

John Patrick Cunningham