Binless Kernel Machine: Modeling Spike Train Transformation for Cognitive Neural Prostheses

Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement

Spike prediction models effectively predict downstream spike trains from upstream neural activity for neural prostheses. Such prostheses could potentially restore damaged neural communication pathways using predicted patterns to guide electrical stimulations on downstream. Since the ground truth of downstream neural activity is unavailable for subjects with the damage, reinforcement learning (RL) with behavior-level rewards becomes necessary for model training. However, existing models do not involve any constraint on the generated firing patterns and neglect the correlations among neural activities. Thus, the model outputs can greatly deviate from the natural range of neural activities, causing concerns for clinical usage. This study proposes the neural manifold constraint to solve this problem, shaping RL-generated spike trains in the feature space. The constraint terms describe the first and second order statistics of the neural manifold estimated from neural recordings during subjects' freely moving period. Then, the models can be optimized within the neural manifold by behavioral reinforcement. We test the method to predict primary motor cortex (M1) spikes from medial prefrontal (mPFC) spikes when rats perform the two-lever discrimination task. Results show that the neural activity generated by constrained models resembles the real M1 recordings. Compared with models without constraints, our approach achieves similar behavioral success rates, but reduces the mean squared error of neural firing by 61%. The constraints also increase the model's robustness across data segments and induce realistic neural correlations. Our method provides a promising tool to restore transregional communication with high behavioral performance and more realistic microscopic patterns.

Applying Neural Manifold Constraint on Point Process Model for Neural Spike Prediction

Neural prostheses can compensate for functional losses caused by blocked neural pathways by modeling neural activities among cortical areas. Existing methods generally utilize point process models to predict neural spikes from one area to another, and optimize the model by maximizing the log-likelihood between model predictions and recorded activities of individual neurons. However, single-neuron recordings can be distorted, while neuron population activity tends to reside within a stable subspace called the neural manifold, which reflects the connectivity and correlation among output neurons. This paper proposes a neural manifold constraint to modify the loss function for model training. The constraint term minimizes the distance from model predictions to the empirical manifold to amend the model predictions from distorted recordings. We test our methods on synthetic data with distortion on output spike trains and evaluate the similarity between model predictions and original output spike trains by the Kolmogorov-Smirnov test. The results show that the models trained with constraint have higher goodness-of-fit than those trained without constraint, which indicates the potential better approach for neural prostheses in noisy environments.

Decoding Ensemble Spike States from Extracellular Field Potentials

Behaviors are encoded by multi-scale brain signals, from microscopic spike signals to macroscopic extracellular Field Potentials (FPs). Extracting neuronal spike information from FPs is an important, yet challenging problem. Because FPs stem from summed contributions of a large population of neurons. Previous work inferred single-neuron spiking activity from the FPs using a generalized linear model (GLM). However, FPs reflect the states of neural ensembles more than single-neuron spike trains. In this paper, we propose a computational model to decode ensemble spike states from FPs. This framework first extracts transient features in FPs, and then detects typical ensemble spike patterns and assigns state labels accordingly. Finally, we use a neural network to decode the ensemble spike states from the FP neuromodulations. This FP-Spike decoder is tested on the FP and spike data from the M1 area of an SD rat. We show that our model can effectively decode multi-neuron spike states. Compared with the GLM method for single-neuron spike prediction, our model exhibits 37% less ensemble spike pattern decoding error. These preliminary results show that we can decode informative spike states from FPs, indicating that the decode results can further benefit long-term stable brain-machine interfaces.

A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size

We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.