Naïve Coadaptive Cortical Control Gregory J Gage, Kip A Ludwig, Kevin J Otto, Edward L Ionides and Daryl R Kipke. Journal of Neural Engineering 2 (2005)

Slides:

Advertisements

Similar presentations

FMRI Methods Lecture 10 – Using natural stimuli. Reductionism Reducing complex things into simpler components Explaining the whole as a sum of its parts.

Advertisements

CNTRICS April 2010 Center-surround: Adaptation to context in perception Robert Shapley Center for Neural Science New York University.

Informatics and Mathematical Modelling / Intelligent Signal Processing 1 Morten Mørup Decomposing event related EEG using Parallel Factor Morten Mørup.

Biological Modeling of Neural Networks: Week 9 – Coding and Decoding Wulfram Gerstner EPFL, Lausanne, Switzerland 9.1 What is a good neuron model? - Models.

RESEARCHERS ARE DEVELOPING NEW METHODS OF TESTING THE OPERABILITY OF PROSTHETICS VIA THE BRAIN. Controlling a Computer With Thought.

CHAPTER 3 CHAPTER 3 R ECURSIVE E STIMATION FOR L INEAR M ODELS Organization of chapter in ISSO –Linear models Relationship between least-squares and mean-square.

1 1 MPI for Biological Cybernetics 2 Stanford University 3 Werner Reichardt Centre for Integrative Neuroscience Eberhard Karls University Tuebingen Epidural.

A Practical Guide to Troubleshooting LMS Filter Adaptation Prepared by Charles H. Sobey, Chief Scientist ChannelScience.com June 30, 2000.

Particle Swarm Optimization (PSO)  Kennedy, J., Eberhart, R. C. (1995). Particle swarm optimization. Proc. IEEE International Conference.

Patch-based Image Deconvolution via Joint Modeling of Sparse Priors Chao Jia and Brian L. Evans The University of Texas at Austin 12 Sep

B.Macukow 1 Lecture 3 Neural Networks. B.Macukow 2 Principles to which the nervous system works.

Robot Localization Using Bayesian Methods

Introduction: Neurons and the Problem of Neural Coding Laboratory of Computational Neuroscience, LCN, CH 1015 Lausanne Swiss Federal Institute of Technology.

20 10 School of Electrical Engineering &Telecommunications UNSW UNSW 2. Laguerre Parameterised Hawkes Process To model spike train data,

Romain Brette Computational neuroscience of sensory systems Dynamics of neural excitability.

Inferring Hand Motion from Multi-Cell Recordings in Motor Cortex using a Kalman Filter Wei Wu*, Michael Black †, Yun Gao*, Elie Bienenstock* §, Mijail.

1Neural Networks B 2009 Neural Networks B Lecture 1 Wolfgang Maass

ICTON 2007, Rome, Italy The Performance of PPM using Neural Network and Symbol Decoding for Diffused Indoor Optical Wireless Links 1 S. Rajbhandari, Z.

Temporal Video Denoising Based on Multihypothesis Motion Compensation Liwei Guo; Au, O.C.; Mengyao Ma; Zhiqin Liang; Hong Kong Univ. of Sci. & Technol.,

Pontifical Catholic University of the Rio Grande do Sul Brazil Applying Artificial Neural Networks to Energy Quality Measurement Fernando Soares dos Reis.

Neural Networks Part 4 Dan Simon Cleveland State University 1.

1 High Transfer Rate, Real-time Brain-Computer Interface Machine-based learning techniques towards a practical spelling device for the completely paralyzed.

To Understand, Survey and Implement Neurodynamic Models By Farhan Tauheed Asif Tasleem.

A brain-machine interface instructed by direct intracortical microstimulation Joseph E. O’Doherty, Mikhail A. Lebedev, Timothy L. Hanson, Nathan A. Fitzsimmons.

EE491D Special Topics in Communications Adaptive Signal Processing Spring 2005 Prof. Anthony Kuh POST 205E Dept. of Elec. Eng. University of Hawaii Phone:

Biological motor control Andrew Richardson McGovern Institute for Brain Research March 14, 2006.

Adaptive Signal Processing

Neural Decoding: Model and Algorithm for Evidence Accumulator Inference Thomas Desautels University College London Gatsby Computational Neuroscience Group.

Zicong Zhang Authors Wendy A. Suzuki Professor of Neural Science and Psychology, New York University Research interest: Organization of memory.

Algorithm Taxonomy Thus far we have focused on:

Introduction to Adaptive Digital Filters Algorithms

NW Computational Intelligence Laboratory Implementing DHP in Software: Taking Control of the Pole-Cart System Lars Holmstrom.

Michael P. Kilgard Sensory Experience and Cortical Plasticity University of Texas at Dallas.

Brain Machine Interfaces: Modeling Strategies for Neural Signal Processing Jose C. Principe, Ph.D. Distinguished Professor ECE, BME Computational NeuroEngineering.

Chapter 3 Neural Network Xiu-jun GONG (Ph. D) School of Computer Science and Technology, Tianjin University

STUDY, MODEL & INTERFACE WITH MOTOR CORTEX Presented by - Waseem Khatri.

NEURAL NETWORKS FOR DATA MINING

Probabilistic Robotics Bayes Filter Implementations.

CHAPTER 4 Adaptive Tapped-delay-line Filters Using the Least Squares Adaptive Filtering.

Operant Conditioning of Cortical Activity E Fetz, 1969.

Encoding/Decoding of Arm Kinematics from Simultaneously Recorded MI Neurons Y. Gao, E. Bienenstock, M. Black, S.Shoham, M.Serruya, J. Donoghue Brown Univ.,

What is Neural Engineering Tae-Seong Kim, Ph.D.. Neural Engineering Neural engineering also known as Ne uroengineering is a discipline that uses engineering.

Statistical learning and optimal control: A framework for biological learning and motor control Lecture 4: Stochastic optimal control Reza Shadmehr Johns.

1 The BUMP model of response planning: Intermittent predictive control accounts for 10 Hz physiological tremor Robin T. Bye* and Peter D. Neilson Neuroengineering.

Theoretical Neuroscience Physics 405, Copenhagen University Block 4, Spring 2007 John Hertz (Nordita) Office: rm Kc10, NBI Blegdamsvej Tel (office)

Evolutionary Path to Biological Kernel Machines Magnus Jändel Swedish Defence Research Agency.

1 Lecture 6 Neural Network Training. 2 Neural Network Training Network training is basic to establishing the functional relationship between the inputs.

Restoring neural function through neural prosthetic “bypass” Ziv Williams, MD Associate Professor in Neurosurgery Faculty, Harvard-MIT Health Sciences.

CHAPTER 10 Widrow-Hoff Learning Ming-Feng Yeh.

Lecture 5 Neural Control

Professors: Eng. Diego Barral Eng. Mariano Llamedo Soria Julian Bruno

Neural Networks Presented by M. Abbasi Course lecturer: Dr.Tohidkhah.

4. For each unit one lag, one polynomial, and one axis were selected based on the quality of fit of the posterior. Thus, the firing rate of each unit is.

Neural Networks 2nd Edition Simon Haykin 柯博昌 Chap 3. Single-Layer Perceptrons.

Brain-Machine Interface (BMI) System Identification Siddharth Dangi and Suraj Gowda BMIs decode neural activity into control signals for prosthetic limbs.

Artificial Intelligence Methods Neural Networks Lecture 3 Rakesh K. Bissoondeeal Rakesh K. Bissoondeeal.

Probabilistic Robotics Introduction. SA-1 2 Introduction  Robotics is the science of perceiving and manipulating the physical world through computer-controlled.

Presentation on BRAIN GATE Sree Vidyanikethan Engineering College A.Rangampeta, Tirupati. By P.Bhaskar (II MCA) M.Jayaprakash (II MCA)

A recurring neurological disorder characterized by random firing of nerve cells in the brain which cause a temporary shutdown of normal brain function.

Neural Codes. Neuronal codes Spiking models: Hodgkin Huxley Model (brief repetition) Reduction of the HH-Model to two dimensions (general) FitzHugh-Nagumo.

Lecture 2 Introduction to Neural Networks and Fuzzy Logic President UniversityErwin SitompulNNFL 2/1 Dr.-Ing. Erwin Sitompul President University

1 Neural Codes. 2 Neuronal Codes – Action potentials as the elementary units voltage clamp from a brain cell of a fly.

before and after rehabilitation

Matt Gormley Lecture 11 October 5, 2016

References and Related Work

Motor cortex Organization of motor cortex Motor cortical map

Sensing with the Motor Cortex

لجنة الهندسة الكهربائية

Brain-computer interfaces.

Decomposing the motor system

Presentation transcript:

Naïve Coadaptive Cortical Control Gregory J Gage, Kip A Ludwig, Kevin J Otto, Edward L Ionides and Daryl R Kipke. Journal of Neural Engineering 2 (2005)

Outline Naïve coadaptive control: what & why Research context –Neurophysiology –Mathematical models Kalman filter Experimental paradigm –Particulars of their coadaptive algorithm Results Discussion & implications of work

Naïve coadaptive cortical control It is hoped brain-machine interfaces (BMIs) will allow reliable & safe cortical control of prosthetics. Past BMI studies used supervised learning, which requires a training signal – something that paraplegics cannot provide! Plus, many devices do not have inherent correlates to physical motor control, i.e. wheelchairs; thus need a naive, adaptive algorithm. supervision Visual feedback planner/controller

Research context Olds 1965 (Proc. XXIII Int. congress Physiol. Sci), Fetz 1969 (Science 163 pp ) demostrate that the signle unit responses in the motor cortex can be operantly conditioned. Shoham et al 2001 (Nature vol 413 p. 793) demostrate that SCI patients can modulate activity in M1 paralyzed individual paralyzed mean normal

Research context: Supervised BMIs Who/whenrefanimalmodeldofunits Chapin 1999Nature Neuroscience v.2 no ratPCA->ANN, 20ms bin< Wessberg 2000Nature v owl monkeyWiener, ANN, 100ms bin<1, Taylor 2002Science V rhesus macaquescoadaptive, 90ms normalized bin, adhoc/gradient descent <3, 3 bits64 recorded, used Serruya 2002Nature rhesus macaqueswiener27-13 Carmena 2003PLoS Biology V 1(2) rhesus macaqueswiener, 100ms bin, 10 lag, block train Paninski 2003J Neurophysiology rhesus macaquesBayes, conditional probabilities modeled w gaussains, wiener prediction 25-18; mean 11 Musallam 2004Science monkeysHarr wavelet decomposition- >Bayes rule via histogram data base - adaptive 2-3 bits8-16 Olson 2005IEEE Trns. Neural Sys. Rehabilitation 13(1) ratsblock-update SVM1 bit8-10

Wiener filter In general, each study used an implementation of an adaptive filter to map neuronal firing patterns to cursor/prosthetic control. The simplest assumption is that the firing rate is linearly related to {position, velocity, force}: or: position/velocity/force dc term weightserror binned neuronal firing Wiener solution: autocorrelation crosscorrelation The wiener filter is block-update, but the same optimal linear solution can be found iteratively by LMS (least mean squares) or RLS (recursive least squares)

Limitations of Wiener/ optimal linear filters While you can predict postion, velocity, and force independently, you cannot predict them in a self consistent manner solution: give the ‘plant’ memory: dependence on past states (wiener = linear dependence on past/present neural firing) This is the Kalman filter!!!