Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

Abstract

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

An Improved Unscented Kalman Filter Based Decoder for Cortical Brain-Machine Interfaces

“…In multiple previous studies (Paninski et al, 2004b; Truccolo et al, 2005; Lawhern et al, 2010; Saleh et al, 2010, 2012; Truccolo et al, 2010; Park et al, 2014; Xu et al, 2014) the past spiking of a neuron as well as the past spiking of other neurons in the population have been used to better model the probability of spiking in a point process framework, sometimes leading to very accurate models (Truccolo et al, 2010). The past neural activity of all neurons in the population may capture correlations in firing due to functional connectivity or common inputs.…”

Monte Carlo point process estimation of electromyographic envelopes from motor cortical spikes for brain–machine interfaces

“…Moreover, our recursive decoding framework may lead to a straightforward way to implement the EMG prediction from cortical neuron spiking in real time. The computational complexity of the proposed method is linearly proportional to the number of the particles that shape the posterior density, times the number of the neural channels [42]. Due to the assumption that all neurons are considered conditionally independent, the particles to estimate the posterior density in a Monte Carlo simulation can be implemented in a parallel way.…”

Decoding methods for neural prostheses: where have we reached?

“…While point process filters (for a review, see Koyama et al, 2010) offer a more realistic noise model, their use in decoding is still relatively rare (Shanechi et al, 2013; Velliste et al, 2014; Xu et al, 2014), due in part to their heavier computational burden. Recently, Citi et al (2013) extend point process methods to model refractory periods of neurons and allow for coarser time discretization by a factor of 10, which may ease this burden.…”

“…The Kalman filter’s Gaussian noise model clearly does not match the data (spike counts), yet due to its accuracy and execution speed the method has remained popular since its first use by Wu et al ( 2003 ) (Aggarwal et al, 2013 ; Chen et al, 2013 ; Dangi et al, 2013a , c ; Homer et al, 2013 ; Ifft et al, 2013 ; Jarosiewicz et al, 2013 ; Kao et al, 2013 ; Merel et al, 2013 ; Wong et al, 2013 ; Zhang and Chase, 2013 ; Fan et al, 2014 ; Golub et al, 2014 ; Gowda et al, 2014 ; Homer et al, 2014 ). While point process filters (for a review, see Koyama et al, 2010 ) offer a more realistic noise model, their use in decoding is still relatively rare (Shanechi et al, 2013 ; Velliste et al, 2014 ; Xu et al, 2014 ), due in part to their heavier computational burden. Recently, Citi et al ( 2013 ) extend point process methods to model refractory periods of neurons and allow for coarser time discretization by a factor of 10, which may ease this burden.…”

“…Xu et al ( 2014 ) included ensemble firing history, in addition to the standard tuning to kinematic variables, in the neural model. This paradigm helps model the background activity that is unrelated to movements.…”