Hongbao Li

Neural Decoding using a Parallel Sequential Monte Carlo method on Point Processes with Ensemble Effect

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.

Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain–Machine Interfaces

Classic brain-machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.

Optogenetic control of thalamus as a tool for interrupting penicillin induced seizures.

Penicillin epilepsy model, whose discharge resembles that of human absence epilepsy, is one of the most useful acute experimental epilepsy models. Though closed-loop optogenetic strategy of interrupting seizures was proved sufficient to switch off epilepsy by controlling thalamus in the post-lesion partial chronic epilepsy model, doubts still exist in absence epilepsy attenuation through silencing thalamus. Here we directly arrested the thalamus to modulate penicillin-induced absence seizures through pseudorandom responsive stimulation on eNpHR-transfected rats. Our data suggested that the duration of epileptiform bursts under light conditions, compared with no light conditions, did not increase or decrease when modulated specific eNpHR-expressing neurons in thalamus.

Predicting Spike Trains from PMd to M1 Using Discrete Time Rescaling Targeted GLM

The computational model for spike train prediction with inputs from other related cerebral cortices is important in revealing the underlying connection among different cortical areas. To evaluate goodness-of-fit of the model, the time rescaling Kolmogorov–Smirnov (KS) statistic is usually applied, of which the calculation is separated from optimization procedure of the model. If the KS statistic could be embedded into objective function of the optimization procedure, precision of the firing probability series generated by the model would be increased directly. This paper presents a linear-nonlinear-Poisson cascade framework for prediction of spike trains, whose objective function is changed from maximizing log-likelihood of the spike trains to minimizing the penalization of discrete time rescaling KS statistic to eliminate the separation between optimization and evaluation of the model. We apply our model on the task of predicting firing probability of neurons from primary motor cortex with spike trains from dorsal premotor cortex as input, which are two cerebral cortices associated with movements planning and executing. The experimental results show that by introducing the goodness-of-fit metric into the objective function, results of the model will gain a significant improvement, which outperforms the state of the art.

Quantized Attention-Gated Kernel Reinforcement Learning for Brain–Machine Interface Decoding

Reinforcement learning (RL)-based decoders in brain–machine interfaces (BMIs) interpret dynamic neural activity without patients’ real limb movements. In conventional RL, the goal state is selected by the user or defined by the physics of the problem, and the decoder finds an optimal policy essentially by assigning credit over time, which is normally very time-consuming. However, BMI tasks require finding a good policy in very few trials, which impose a limit on the complexity of the tasks that can be learned before the animal quits. Therefore, this paper explores the possibility of letting the agent infer potential goals through actions over space with multiple objects, using the instantaneous reward to assign credit spatially. A previous method, attention-gated RL employs a multilayer perceptron trained with backpropagation, but it is prone to local minima entrapment. We propose a quantized attention-gated kernel RL (QAGKRL) to avoid the local minima adaptation in spatial credit assignment and sparsify the network topology. The experimental results show that the QAGKRL achieves higher successful rates and more stable performance, indicating its powerful decoding ability for more sophisticated BMI tasks as required in clinical applications.

Decoding the Non-Stationary Neuron Spike Trains by Dual Monte Carlo Point Process Estimation in Motor Brain Machine Interfaces

Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration of the decoding performance when the model is fixed. To track the non-stationary neuron tuning during decoding, we propose a dual model approach based on Monte Carlo point process filtering method that enables the estimation also on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning parameters, which raises a promising way to design a long-term-performing model for Brain Machine Interfaces decoder.

Nonlinear Modeling of Neural Interaction for Spike Prediction Using the Staged Point-Process Model

Neurons communicate nonlinearly through spike activities. Generalized linear models (GLMs) describe spike activities with a cascade of a linear combination across inputs, a static nonlinear function, and an inhomogeneous Bernoulli or Poisson process, or Cox process if a self-history term is considered. This structure considers the output nonlinearity in spike generation but excludes the nonlinear interaction among input neurons. Recent studies extend GLMs by modeling the interaction among input neurons with a quadratic function, which considers the interaction between every pair of input spikes. However, quadratic effects may not fully capture the nonlinear nature of input interaction. We therefore propose a staged point-process model to describe the nonlinear interaction among inputs using a few hidden units, which follows the idea of artificial neural networks. The output firing probability conditioned on inputs is formed as a cascade of two linear-nonlinear (a linear combination plus a static nonlinear function) stages and an inhomogeneous Bernoulli process. Parameters of this model are estimated by maximizing the log likelihood on output spike trains. Unlike the iterative reweighted least squares algorithm used in GLMs, where the performance is guaranteed by the concave condition, we propose a modified Levenberg-Marquardt (L-M) algorithm, which directly calculates the Hessian matrix of the log likelihood, for the nonlinear optimization in our model. The proposed model is tested on both synthetic data and real spike train data recorded from the dorsal premotor cortex and primary motor cortex of a monkey performing a center-out task. Performances are evaluated by discrete-time rescaled Kolmogorov-Smirnov tests, where our model statistically outperforms a GLM and its quadratic extension, with a higher goodness-of-fit in the prediction results. In addition, the staged point-process model describes nonlinear interaction among input neurons with fewer parameters than quadratic models, and the modified L-M algorithm also demonstrates fast convergence.

Prior Knowledge of Target Direction and Intended Movement Selection Improves Indirect Reaching Movement Decoding

Objective. Previous studies have demonstrated that target direction information presented by the dorsal premotor cortex (PMd) during movement planning could be incorporated into neural decoder for achieving better decoding performance. It is still unknown whether the neural decoder combined with only target direction could work in more complex tasks where obstacles impeded direct reaching paths. Methods. In this study, spike activities were collected from the PMd of two monkeys when performing a delayed obstacle-avoidance task. We examined how target direction and intended movement selection were encoded in neuron population activities of the PMd during movement planning. The decoding performances of movement trajectory were compared for three neural decoders with no prior knowledge, or only target direction, or both target direction and intended movement selection integrated into a mixture of trajectory model (MTM). Results. We found that not only target direction but also intended movement selection was presented in neural activities of the PMd during movement planning. It was further confirmed by quantitative analysis. Combined with prior knowledge, the trajectory decoder achieved the best performance among three decoders. Conclusion. Recruiting prior knowledge about target direction and intended movement selection extracted from the PMd could enhance the decoding performance of hand trajectory in indirect reaching movement.

Maximum correntropy based attention-gated reinforcement learning designed for brain machine interface

Reinforcement learning is an effective algorithm for brain machine interfaces (BMIs) which interprets the mapping between neural activities with plasticity and the kinematics. Exploring large state-action space is difficulty when the complicated BMIs needs to assign credits over both time and space. For BMIs attention gated reinforcement learning (AGREL) has been developed to classify multi-actions for spatial credit assignment task with better efficiency. However, the outliers existing in the neural signals still make interpret the neural-action mapping difficult. We propose an enhanced AGREL algorithm using correntropy as a criterion, which is more insensitive to noise. Then the algorithm is tested on the neural data where the monkey is trained to do the obstacle avoidance task. The new method converges faster during the training period, and improves from 44.63% to 68.79% on average in success rate compared with the original AGREL. The result indicates that the combination of correntropy criterion and AGREL can reduce the effect of the outliers with better performance when interpreting the mapping between neural signal and kinematics.