Neural networks and the bias/variance dilemma
Neural Computation
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models
Neural Information Processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Spiking neural networks for cortical neuronal spike train decoding
Neural Computation
Spiking neural networks for cortical neuronal spike train decoding
Neural Computation
A new look at state-space models for neural data
Journal of Computational Neuroscience
Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control
Journal of Computational Neuroscience
Switching Manipulator Control for Motion on Constrained Surfaces
Journal of Intelligent and Robotic Systems
Stochastic optimal control as a theory of brain-machine interface operation
Neural Computation
Information transmission using non-poisson regular firing
Neural Computation
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian. In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results. The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.