Brain-machine interfaces in rat motor cortex: implications of adaptive decoding algorithms

2003 ◽  
Author(s):  
K.J. Otto ◽  
R.J. Vetter ◽  
T.C. Marzullo ◽  
D.R. Kipke
Keyword(s):  
Motor Cortex ◽  
Brain Machine Interfaces ◽  
Decoding Algorithms ◽  
Adaptive Decoding

scholarly journals Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Movement Planning ◽  
Neural Population ◽  
Great Effort ◽  
Single Neurons ◽  
Latent Factors ◽  
Brain Machine Interfaces ◽  
Population Activity ◽  
Systems Perspective ◽  
Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

Adaptive Decoding for Turbo Codes over CDMA2000 Mobile System

2011 ◽  
Vol 135-136 ◽  
pp. 171-178
Author(s):  
Shuai Yuan ◽  
Min Dan Bai ◽  
Hua Qing Zhang
Keyword(s):  
Turbo Codes ◽  
Error Control ◽  
Wireless Systems ◽  
New Method ◽  
Channel Fading ◽  
Mobile System ◽  
Error Control Coding ◽  
Mobile Wireless ◽  
Decoding Algorithms ◽  
Adaptive Decoding

As a powerful branch of error-control coding, Turbo codes have been paid a great attention since they are proposed. Due to the powerful error correcting capability, Turbo codes are very attractive for mobile wireless systems to combat channel fading. However, there are many kinds of decoding algorithms for Turbo codes, any of them have merits and demerits. In this paper, we present a new method to decode Turbo codes over cdma2000 mobile system, that is introducing a controller to achieve the purposer of adaptive decoding. And this method make the system more reliable.

Decoding Algorithms for Brain–Machine Interfaces

2012 ◽  
pp. 223-257 ◽  
Author(s):  
Austin J. Brockmeier ◽  
José C. Príncipe
Keyword(s):  
Brain Machine Interfaces ◽  
Decoding Algorithms

scholarly journals Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates

2011 ◽  
Vol 23 (12) ◽  
pp. 3162-3204 ◽  
Author(s):  
Zheng Li ◽  
Joseph E. O'Doherty ◽  
Mikhail A. Lebedev ◽  
Miguel A. L. Nicolelis
Keyword(s):  
Linear Regression ◽  
Closed Loop ◽  
Unscented Kalman Filter ◽  
Bayesian Regression ◽  
Brain Machine Interfaces ◽  
Neuronal Populations ◽  
Long Time ◽  
Bayesian Linear Regression ◽  
Adaptive Decoding ◽  
Control Accuracy

Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel paradigm uses the decoder's output to periodically update its neuronal tuning model in a Bayesian linear regression. We use two previously known statistical formulations of Bayesian linear regression: a joint formulation, which allows fast and exact inference, and a factorized formulation, which allows the addition and temporary omission of neurons from updates but requires approximate variational inference. To evaluate these methods, we performed offline reconstructions and closed-loop experiments with rhesus monkeys implanted cortically with microwire electrodes. Offline reconstructions used data recorded in areas M1, S1, PMd, SMA, and PP of three monkeys while they controlled a cursor using a handheld joystick. The Bayesian regression self-training updates significantly improved the accuracy of offline reconstructions compared to the same decoder without updates. We performed 11 sessions of real-time, closed-loop experiments with a monkey implanted in areas M1 and S1. These sessions spanned 29 days. The monkey controlled the cursor using the decoder with and without updates. The updates maintained control accuracy and did not require information about monkey hand movements, assumptions about desired movements, or knowledge of the intended movement goals as training signals. These results indicate that Bayesian regression self-training can maintain BMI control accuracy over long periods, making clinical neuroprosthetics more viable.

scholarly journals Latent factors and dynamics in motor cortex and their application to brain-machine interfaces

2018 ◽  
Author(s):  
Chethan Pandarinath ◽  
K. Cora Ames ◽  
Abigail A Russo ◽  
Ali Farshchian ◽  
Lee E Miller ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Movement Planning ◽  
Neural Population ◽  
Great Effort ◽  
Single Neurons ◽  
Latent Factors ◽  
Brain Machine Interfaces ◽  
Population Activity ◽  
Systems Perspective ◽  
Behaving Monkeys

In the fifty years since Evarts first recorded single neurons in motor cortex of behaving monkeys, great effort has been devoted to understanding their relation to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study network-level phenomena is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective, and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the “latent factors” underlying observed neural population activity. Finally, we discuss efforts to leverage these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

scholarly journals Analysing correlated noise on the surface code using adaptive decoding algorithms

Quantum ◽  
2019 ◽  
Vol 3 ◽  
pp. 131 ◽  
Author(s):  
Naomi H. Nickerson ◽  
Benjamin J. Brown
Keyword(s):  
Error Correcting Codes ◽  
Error Rates ◽  
Noise Model ◽  
Complex Nature ◽  
Correlated Noise ◽  
Correlated Errors ◽  
Decoding Algorithms ◽  
Spatially Correlated ◽  
Surface Code ◽  
Adaptive Decoding

Laboratory hardware is rapidly progressing towards a state where quantum error-correcting codes can be realised. As such, we must learn how to deal with the complex nature of the noise that may occur in real physical systems. Single qubit Pauli errors are commonly used to study the behaviour of error-correcting codes, but in general we might expect the environment to introduce correlated errors to a system. Given some knowledge of structures that errors commonly take, it may be possible to adapt the error-correction procedure to compensate for this noise, but performing full state tomography on a physical system to analyse this structure quickly becomes impossible as the size increases beyond a few qubits. Here we develop and test new methods to analyse blue a particular class of spatially correlated errors by making use of parametrised families of decoding algorithms. We demonstrate our method numerically using a diffusive noise model. We show that information can be learnt about the parameters of the noise model, and additionally that the logical error rates can be improved. We conclude by discussing how our method could be utilised in a practical setting blue and propose extensions of our work to study more general error models.

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