WEI DA WU
Dietitian and Nutritionist in Providence, RI

License number
Rhode Island FMC18605
Issued Date
Oct 27, 2010
Expiration Date
Oct 27, 2019
Category
Food Safety
Type
Certified Food Safety Mgr
Address
Address
80 Providence Place Mall SUITE 5135, Providence, RI 02903
Phone
(646) 812-0501 (Work)

Professional information

Wei Wu Photo 1

Student At Brown University

Position:
Teaching Assistant at Brown University
Location:
Providence, Rhode Island Area
Industry:
Research
Work:
Brown University since Sep 2010 - Teaching Assistant Theoretical Life Science Center, Fudan University, Shanghai 2007 - 2009 - Research Assistant
Education:
Brown University 2009 - 2014
PhD, Applied Math
Fudan University 2005 - 2009
BSc, Physics


Wei Wu Photo 2

Method And System For Inferring Hand Motion From Multi-Cell Recordings In The Motor Cortex Using A Kalman Filter Or A Bayesian Model

US Patent:
2004007, Apr 15, 2004
Filed:
Jun 4, 2003
Appl. No.:
10/455509
Inventors:
Lucien Bienenstock - Providence RI, US
Michael Black - Providence RI, US
Wei Wu - Providence RI, US
Yun Gao - Providence RI, US
Assignee:
Brown University Research Foundation
International Classification:
G06F017/10
US Classification:
703/002000
Abstract:
A method and system to decode neural activity in the motor cortex to infer at least the position and velocity of a subject's hand from neural spiking activity of some number of nerve cells. In one embodiment the method includes simultaneously recording electrical activity of the nerve cells in the primary motor cortex to obtain neural data; and modeling the encoding and decoding of the neural data using a Kalman filter, where a measurement model assumes a cell firing rate to be a stochastic linear function of at least the position and velocity of the hand, and where the measurement model is learned from training data in conjunction with a system model that encodes a manner in which the hand moves. In another embodiment the method includes using the neural data to generate training data of neural firing activity conditioned on hand kinematics; learning a non-parametric representation of the firing activity using a Bayesian model; inferring an a posterior probability distribution over hand motion, conditioned on the neural training data using Bayesian inference; defining a non-Gaussian likelihood term that is combined with a prior probability for the kinematics based on learned firing models of multiple nerve cells; and using a particle filtering method is to represent, update, and propagate the posterior distribution over time.