nils-herrmann/clean_scirepeval huggingface dataset:
An adaptive inverse controller for online somatosensory microstimulation optimization

Precise control of neural circuits via microstimulation is an indispensable but challenging objective in neuro-engineering. The effect of electrical stimulation is imprecise and has a spatio-temporal blurring. At the neuron level, the effects are obfuscated by the complexity of neural dynamics. This paper proposes an online multiple-input-multiple-output (MIMO) adaptive inverse controller for somatosensory microstimulation. The control of the target firing pattern is achieved by including an adaptive controller before the stimulator whose transfer function is always adjusted to be the inverse of the neural circuit transfer function. In this paper a synthetic neural circuit is built from LIF neurons to model the neural circuit. Considering a Poisson model for the target spike train, we identify the LIF neural model using a Generalized Linear Model (GLM) fitted with a maximum likelihood (ML) criterion. The controller architecture becomes the inverse of the GLM and its parameters are periodically adjusted to ensure that the input to the LIF model approximates the target spike time response. In synthetic data, the results show that this control scheme successfully determines the impulse timing and amplitude of the desired stimuli and drives the dynamic neural circuit output to follow the target firing pattern. With the simulated model, the method is able to preserve the temporal precision of neural spike trains.
Nonlinear independent component analysis by homomorphic transformation of the mixtures

Independent component analysis is often approached from an information theoretic perspective employing specific sample estimates for the mutual information between the separated outputs. These approximations involve the nonparametric estimation of signal entropies. The common approach involves the estimation of these quantities and adaptation based on these criteria. In contrast, in this paper, we propose a Gaussianization-based approach, where the separation is performed in two stages: Gaussianization of the mixtures using a homomorphic nonlinearity and separation of the independent components using principal component analysis (both stages possibly adaptive). Due to the rotation uncertainty in nonlinear ICA, the original sources cannot be recovered solely by the independence assumption. The proposed ICA methodology is applicable to instantaneous linear and nonlinear mixtures. The idea also generalizes easily to complex-valued nonlinear ICA.
Training MLPs layer-by-layer with the information potential

In the area of information processing one fundamental issue is how to measure the statistical relationship between two variables based only on their samples. The authors previously (1998) presented the idea of information potential which was formulated from the quadratic mutual information, and successfully applied it to problems such as blind source separation and pose estimation of SAR images. This paper shows how information potential can be used to train a MLP (multilayer perceptron) layer-by-layer, which provides evidence that the hidden layer of a MLP serves as an "information filter" which tries to best represent the desired output in that layer in the statistical sense of mutual information.
Reproducing kernel Hilbert spaces for spike train analysis

This paper introduces a generalized cross-correlation (GCC) measure for spike train analysis derived from reproducing kernel Hilbert spaces (RKHS) theory. An estimator for GCC is derived that does not depend on binning or a specific kernel and it operates directly and efficiently on spike times. For instantaneous analysis as required for real-time use, an instantaneous estimator is proposed and proved to yield the GCC on average. We finalize with two experiments illustrating the usefulness of the techniques derived.
Modified embedding for multi-regime detection in nonstationary streaming data

Many practical data streams are typically composed of several states known as regimes. In this paper, we invoke phase space reconstruction methods from non-linear time series and dynamical systems for regime detection. But the data collected from sensors is normally noisy, does not have constant amplitude and is sometimes plagued by shifts in the mean. All these aspects make modeling even more difficult. We propose a representation of the time series in the phase space with a modified embedding, which is invariant to translation and scale. The features we use for regime detection are based on comparing trajectory segments in the modified embedding space with cross-correntropy, which is a generalized correlation function. We apply our algorithm to non-linear oscillations, and compare its performance with the standard time delay embedding.
Analysis of Amplitude Modulated Control Features for ECoG Neuroprosthetics

Electrocorticogram recordings for neuroprosthetics provide an intermediate level of abstraction between EEG and microwire single neuron recordings. For adaptive filtering methodologies used in neuroprosthetics, extraction of spatio-control parameters remains a difficulty. Since amplitude modulation in extracellular recordings plays a key role in both neuronal activation and rate coding, seeking spatial pattern classification and temporally intermittent population synchronization in terms of increased voltage may provide viable control signals. This study seeks to explore preprocessing modalities that emphasize amplitude modulation in the ECoG above the level of noise and background fluctuations in order to derive the commands for complex control tasks. The decoding performance of the amplitude modulation across the recording spectra was found to be spatially specific in the cortex
Multiresolution Representations and Data Mining of Neural Spikes for Brain-Machine Interfaces

In brain-machine interface (BMI) applications, neural firing activities have been represented by spike counts with a fixed-width time bin. Adaptive models have been designed to utilize these bin counts for mapping the associated behavior which is typically 2D or 3D hand movement. However, the representation of the firing activities can be enriched by binning neural spikes with multiple time scales based on multiresolution analysis. This multiresolution representation of neural activities can provide more accurate prediction of the hand movement parameters. Data mining techniques must be applied to models using multiresolution representation in order to avoid over-fitting. In this paper, we demonstrate that the multiresolution representation improves the performance of the linear model for BMIs compared to the model with the fixed-width time bin
Robust Adaptive Minimum Entropy Beamformer in Impulsive Noise

This paper considers the problem of adaptive beamforming in alpha-stable (non-Gaussian) noise using the constrained minimum output entropy (MOE) based algorithm. Following the same rational that lead to the least mean p-norm (LMP), the weight update adjustment for minimum output entropy is constrained by statistics higher than second order. Also, the MOE algorithm is very robust to impulsive noise due to its M-estimator property derived from the fact that MOE constrains the output entropy. We explain these results analytically, and through simulations.
Compression of Spike Data Using the Self-Organizing Map

Motivated by current attempts to use wireless in brain-machine interfaces (BMIs), this paper presents a method for the compression of spike data. Supported by vector quantization (VQ) theory, we use a 1-dimensional self-organizing map (SOM) to quantize vectors of input samples. The indices are entropy coded to further reduce the necessary bandwidth, taking advantage of the non-uniform frequency of firing of the SOM processing elements (PEs). The complexity of the use of the SOM is also considered and addressed. After training several SOMs, the method was simulated with real data achieving compression ratios as high as 185.7:1, i.e. a bitrate of 862 bits-per-second-per-channel, assuming sampling at 20 kHz with 8 bits-per-sample (bps)
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.
On-line Detection of Perceptual Signatures in Multichannel ECoG

Neocortical ECoG studies have unveiled the presence of active states spatial patterns of amplitude modulation- in the beta-gamma ranges in the presence of conditioned stimuli that resemble cinematographic frames. These sequences of active frames emerge with abrupt phase resettings, followed by resynchronization and stabilization over channels, and magnified intensity. An online pattern recognizer that captures the spatial and spectral characteristics of the active frames is presented. The results of detection are confirmed via high occurrences of pragmatic information, defined by the ratio of pattern intensity to pattern stability
Water Inflow Forecasting using the Echo State Network: a Brazilian Case Study

A type of recurrent neural network has been proposed by H. Jaeger. This model, called Echo State Network (ESN), possesses a highly interconnected and recurrent topology of nonlinear processing elements, which constitutes a "reservoir of rich dynamics" and contains information about the history of input or/and output patterns. The interesting property of ESN is that only the memoryless readout is trained, whereas the recurrent topology has fixed connection weights. This reduces the complexity of recurrent neural network training to simple linear regression while preserving a recurrent topology. In this paper, the ESN is used to forecast hydropower plant reservoir water inflow, which is a fundamental information to the hydrothermal power system operation planning. A database of average monthly water inflows of Furnas plant, one of the Brazilian hydropower plants, was used as source of training and test data. The performance of the ESN is compared with SONARX network, RBF network and ANFIS model. The results show that the Echo State Network provides pretty good results for one-step ahead water inflow forecasting, providing a valuable information for the system operator.
Blind source separation of time-varying instantaneous mixtures using an on-line algorithm

A blind source separation algorithm is presented that performs online separation of an unknown, time-varying, instantaneous mixture of independent sources. This procedure utilizes the information-theoretic MeRMald-SIG criterion and an on-line PCA algorithm, referred to as SIPEX-G, that has been recently submitted for publication. Results indicate that the combination of the two on-line criteria is able to track a rapidly changing mixing matrix. A performance comparison of separation criteria is also made using the same, aforementioned on-line PCA algorithm. Results show the superior performance of the proposed method.
An Adaptive Algorithm Based On The Sigmoidal Function

In this work, we show the development of an adaptive algorithm based on the Ln(cosh \varepsilon) as cost function applied upon the error, called Sigmoidal Algorithm (SA). That function generates a surface which yields fast convergence along with lower misadjustment. It is similar to the family of algorithms proposed by Walach and Widrow . The later ones were shown to behave poorer than the LMS algorithm , when the noise was Gaussian. We study the SA algorithm convergence behavior and find equations for the misadjustment and the learning time. Results showed that the SA had a better performance than the LMS when the noise had a Gaussian distribution.
Linear discriminant analysis with an information divergence criterion

Linear discriminant analysis seeks to find a one-dimensional projection of a dataset to alleviate the problems associated with classifying high-dimensional data. The earliest methods, based on second-order statistics often fail on multimodal datasets. Information-theoretic criteria do not suffer in such cases, and allow for projections to spaces higher than one dimension and with multiple classes. These approaches are based on maximizing mutual information between the projected data and the labels. However, mutual information is computationally demanding and vulnerable to datasets with class imbalance. In this paper we propose an information-theoretic criterion for learning discriminants based on the Euclidean distance divergence between classes. This objective more directly seeks projections which separate classes and performs well in the midst of class imbalance. We demonstrate the effectiveness on real datasets, and provide extensions to the multi-class and multi-dimension cases.
Information Theoretic-Learning auto-encoder

We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a paritition function. This paper also formalizes, generative moment matching networks under the ITL framework.
Compression of neural signals using discriminative coding for wireless applications

One of the most critical tasks when designing a portable wireless neural recording system is to limit power consumption. This paper proposes a new compression technique applied to neuronal recordings in real-time. The signal is compressed before transmission using a discriminative vector quantization algorithm and then it is reconstructed on the receiver side. Results show that power consumption is decreased while more efficiently using the limited bandwidth. A discriminative Linde-Buzo-Gray algorithm (DLBG) preserves action potential regions of the neuronal signal where information is contained while efficiently filtering background activity. The compression algorithm has been tested in real time on a hardware platform (PICO DSP ) that has a Digital Signal Processor (DSP) which performs the algorithm before sending the compressed data to a wireless transmitter. The compression ratios obtained range between 20:1 and 70:1 depending on the embedding size of the signal and the number of code-vectors used.
Non-linear dynamic modeling of glucose in type 1 diabetes with kernel adaptive filters

We propose a non-linear recursive solution to the problem of short-term prediction of glucose in type 1 diabetes. The Fixed Budget Quantized Kernel Least Mean Square (QKLMS-FB) algorithm is employed to construct a univariate model of subcutaneous glucose concentration, which: (i) handles nonlinearities by transforming the input space into a high-dimensional Reproducing Kernel Hilbert Space and, (ii) finds a sparse solution by retaining a representative subset of the training input vectors. The dataset comes from the continuous multi-day recordings of 15 type 1 patients in free-living conditions. QKLMS-FB produces an average root mean squared error of 18.66±3.19 mg/dl for a prediction horizon of 30 min with 82.04% of hypoglycemic readings and 93.30% of hyperglycemic ones being classified as clinically accurate or with benign errors. The effect of the prediction horizon is more evident in the hypoglycemic range.
Functional data representation using correntropy locally linear embedding

Unlike classical linear dimensionality reduction techniques, nonlinear ones are capable of discovering the nonlinear degrees of freedom that are present in natural observations, by assuming that the data lie on an embedded nonlinear manifold within an observed high dimensional feature space. Nevertheless, when measured objects are actually functional data, nonlinear dimensionality reduction techniques do not produce suitable unfolded results, particularly in the locally linear embedding (LLE) algorithm. In this case, the Euclidean distance employed in cost function does not correctly represent the similarity between objects, because this distance does not take into account the intrinsical relations presented in functional data, besides, it is easily distorted by non-gaussian noise, such as an artifacts, impulsive noise, etc. The main contribution in this paper is to use (inside the LLE algorithm) a localized similarity measure called correntropy, which has a particular metric that allows it to conform to suitable neighborhoods, and indeed convenient representations of the objects, avoiding distortions.
Single-trial ERP estimation based on spatio-temporal filtering

This paper introduces a new approach to the problem of single-trial event-related potentials (ERP) estimation in EEG data with very low signal-to-noise (SNR) ratio. The method relies on modeling of the ERP component descriptors to be estimated and utilizes a spatial filter to constrain the output in the temporal domain. Results on cognitive EEG data with very low SNR show that our method successfully extracts the P300 component while other traditional methods fail. Some possible improvements on the present method are also discussed.
Speaker verification and identification using gamma neural networks

Gamma neural networks are used for speaker verification and identification in this paper. When input features are cepstral coefficients, the memory depth of the gamma networks can be adjusted online and the outputs of the gamma memory may be viewed as a combination of cepstral and delta cepstral coefficients with adaptable weights. So, gamma networks are very suitable to grasp the dynamics of speech. Simulation results show that the gamma networks outperform other neural approaches for speaker identification and verification in TIMIT database experiments.
BMI cyberworkstation: Enabling dynamic data-driven brain-machine interface research through cyberinfrastructure

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.
Correntropy-based spectrum sensing for wireless microphones in man-made noise environments

Correntropy is a generalized similarity measure that is here applied to estimate the spectral density which we called the correntropy spectral density. When the Gaussian kernel is utilized in correntropy, there is a free parameter. In this paper, we propose a weighted method to reduce the concerns about the free parameter selection. The proposed method is applied for detecting the presence of narrow-band wireless microphone signals in TV white space. The simulation results show that the proposed method has higher detection ability than the conventional power spectral density method in man-made noise environments and very close to the conventional in Gaussian noise, while maintaining low false alarms in both.
A Spatiotemporal Filtering Methodology for Single-Trial ERP Component Estimation

A new spatiotemporal filtering method for single-trial event-related potential (ERP) estimation is proposed. Instead of attempting to model the entire ERP waveform, the method relies on modeling ERP component descriptors (amplitude and latency) thru the spatial diversity of multichannel recordings, and thus, it is tailored to extract signals in negative SNR conditions. The model allows for both amplitude and latency variability in the ERP component under investigation. The extracted ERP component is constrained through a spatial filter to have minimal distance (with respect to some metric) in the temporal domain from a user-designed template component. The spatial filter may be interpreted as a noise canceller in the spatial domain. Study with both simulated data and real cognitive ERP data shows the effectiveness of the proposed method.
Improved Linear BMI Systems via Population Averaging

We investigate population averaging as a preprocessing stage for linear FIR BMIs. Population averaging is a biologically-inspired technique based on spatial constraints and neuronal correlation. We achieve a statistically significant improvement in accuracy while substantially (45%) reducing model parameters. Further analysis is performed to show that population averaging improves model accuracy by reducing variance in estimating the firing rate from spike bins. However, we find that population averaging provides a greater accuracy improvement than other groupings which also reduce firing rate variance. Our results suggest that appropriate spatial organization of neural signals enhances BMI performance
Convergence of a Fixed-Point Algorithm under Maximum Correntropy Criterion

The maximum correntropy criterion (MCC) has received increasing attention in signal processing and machine learning due to its robustness against outliers (or impulsive noises). Some gradient based adaptive filtering algorithms under MCC have been developed and available for practical use. The fixed-point algorithms under MCC are, however, seldom studied. In particular, too little attention has been paid to the convergence issue of the fixed-point MCC algorithms. In this letter, we will study this problem and give a sufficient condition to guarantee the convergence of a fixed-point MCC algorithm.
Information Theoretic Mean Shift Algorithm

In this paper we introduce a new cost function called information theoretic mean shift algorithm to capture the "predominant structure" in the data. We formulate this problem with a cost function which minimizes the entropy of the data subject to the constraint that the Cauchy-Schwartz distance between the new and the original dataset is fixed to some constant value. We show that Gaussian mean shift and the Gaussian blurring mean shift are special cases of this generalized algorithm giving a whole new perspective to the idea of mean shift. Further this algorithm can also be used to capture the principal curve of the data making it ubiquitous for manifold learning.