nils-herrmann/clean_scirepeval huggingface dataset:
A novel methodology to quantify dense EEG in cognitive tasks

Cognition emerges from complex interaction amongst widespread brain areas. In this paper, we use a novel methodology for temporal networks quantification for EEG. We model the spatiotemporal structure of dependencies across different electrodes with respect to a single electrode as a local probability density function. This enables immediately the use of information theoretic quantities (information divergences) to quantify brain connectivity in simple two-dimensional graphs. We show that for a visual-motor-driven task, we are able to cluster subjects that performed the task with higher attention-coefficient, in an unsupervised-fashion. We test this methodology with two measures of functional connectivity: correlation coefficient and a measure of association.
Efficient total least squares method for system modeling using minor component analysis

We present two algorithms to solve the total least-squares (TLS) problem. The algorithms are on-line with O(N/sup 2/) and O(N) complexity. The convergence of the algorithms is significantly faster than the traditional methods. A mathematical analysis of convergence is also provided along with simulations to substantiate the claims. We also apply the TLS algorithms for FIR system identification with known model order in the presence of noise.
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.
Deformable Bayesian Network: A Robust Framework for Underwater Sensor Fusion

The dynamic tree (DT) graphical model is a popular analytical tool for image segmentation and object classification tasks. A DT is a useful model in this context because its hierarchical property enables the user to examine information in multiple scales and its flexible structure can more easily fit complex region boundaries compared to rigid quadtree structures such as tree-structured Bayesian networks. This paper proposes a novel framework for data fusion called a deformable Bayesian network (DFBN) by using a DT model to fuse measurements from multiple sensing platforms into a nonredundant representation. The structural flexibility of the DFBN will be used to fuse common information across different sensor measurements. The appropriate structure update strategies for the DFBN and its parameters for the data fusion application are discussed. A real-world example application using sonar images collected from a survey mission is presented. The fusion results using the presented DFBN framework are shown to outperform state-of-the-art approaches such as the Gaussian mean shift and spectral clustering algorithms. The DFBN's complexity and scalability are discussed to address its potential for a larger data set.
Do Hebbian synapses estimate entropy?

Hebbian learning is one of the mainstays of biologically inspired neural processing. Hebb's (1949) rule is biologically plausible, and it has been extensively utilized in both computational neuroscience and in unsupervised training of neural systems. In these fields, Hebbian learning became synonymous for correlation learning. But it is known that correlation is a second order statistic of the data, so it is sub-optimal when the goal is to extract as much information as possible from the sensory data stream. We demonstrate how information learning can be implemented using Hebb's rule. Thus the paper brings a new understanding to how neural systems could, through Hebb's rule, extract information theoretic quantities rather than merely correlation.
Spectral Clustering of Synchronous Spike Trains

In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophys-iological studies. In this context such activity is thought to be associated with functional structures in the brain. In addition, clustering has the potential to analyze large volumes of data. The algorithm couples a distance between two spike trains recently proposed in the literature with spectral clustering. Finally, the algorithm is illustrated in sets of computer generated spike trains and analyzed for the dependence on its parameters and accuracy with respect to features of interest.
A New Nonlinear Similarity Measure for Multichannel Biological Signals

We propose a novel similarity measure, called the correntropy coefficient, sensitive to higher order moments of the signal statistics based on a similarity function called crosscorrentopy. Crossorrentropy nonlinearly maps the original time series into a high-dimensional reproducing kernel Hilbert space (RKHS). The correntropy coefficient computes the cosine of the angle between the transformed vectors. Preliminary experiments with simulated data and multichannel electroencephalogram (EEG) signals during behavior studies elucidate the performance of the new measure versus the well established correlation coefficient.
An adaptive kernel width update method of correntropy for channel estimation

The maximum correntropy criterion (MCC) is a robust adaptation criterion, which has been successfully applied in various signal processing applications. The kernel width plays an important role in MCC adaptive filtering and has significant influence on convergence rate and steady-state performance. In this paper, we develop an adaptive kernel width update method for correntropy. The proposed algorithm may achieve faster convergence speed and lower steady-state excess mean square error (EMSE) than the original MCC algorithm. More importantly, the new method involves no extra free parameters and is computationally very simple.
Evaluating dependence in spike train metric spaces

Assessing dependence between two sets of spike trains or between a set of input stimuli and the corresponding generated spike trains is crucial in many neuroscientific applications, such as in analyzing functional connectivity among neural assemblies, and in neural coding. Dependence between two random variables is traditionally assessed in terms of mutual information. However, although well explored in the context of real or vector valued random variables, estimating mutual information still remains a challenging issue when the random variables exist in more exotic spaces such as the space of spike trains. In the statistical literature, on the other hand, the concept of dependence between two random variables has been presented in many other ways, e.g. using copula, or using measures of association such as Spearman's ρ, and Kendall's τ. Although these methods are usually applied on the real line, their simplicity, both in terms of understanding and estimating, make them worth investigating in the context of spike train dependence. In this paper, we generalize the concept of association to any abstract metric spaces. This new approach is an attractive alternative to mutual information, since it can be easily estimated from realizations without binning or clustering. It also provides an intuitive understanding of what dependence implies in the context of realizations. We show that this new methodology effectively captures dependence between sets of stimuli and spike trains. Moreover, the estimator has desirable small sample characteristic, and it often outperforms an existing similar metric based approach.
Time based gain control for the analog to pulse converter

This study presents an approach for time based automatic gain control (TGC) for the analog to pulse converter (APC). APC converts the area under analog signal into a sequence of pulses, with the spacing between pulses corresponding to the time taken to accumulate a fixed area. In this paper, we demonstrate that the gain of signal amplifier can be automatically controlled using the information in the time between pulses; thereby, ensuring the pulse density of APC remains within desired range despite fluctuations in the input. The performance of the TGC is quantified in terms of settling time and error bounds. We study the proposed system theoretically and show that its response is equivalent to conventional AGCs. Moreover, it is tested on physiological signals corrupted by a fading profile and the results demonstrate the effectiveness of the proposed system.
A Reproducing Kernel Hilbert Space Framework for Spike Train Signal Processing

This letter presents a general framework based on reproducing kernel Hilbert spaces (RKHS) to mathematically describe and manipulate spike trains. The main idea is the definition of inner products to allow spike train signal processing from basic principles while incorporating their statistical description as point processes. Moreover, because many inner products can be formulated, a particular definition can be crafted to best fit an application. These ideas are illustrated by the definition of a number of spike train inner products. To further elicit the advantages of the RKHS framework, a family of these inner products, the cross-intensity (CI) kernels, is analyzed in detail. This inner product family encapsulates the statistical description from the conditional intensity functions of spike trains. The problem of their estimation is also addressed. The simplest of the spike train kernels in this family provide an interesting perspective to others' work, as will be demonstrated in terms of spike train distance measures. Finally, as an application example, the RKHS framework is used to derive a clustering algorithm for spike trains from simple principles.
Real time signal reconstruction from spikes on a digital signal processor

There has been significant interest in encoding information into asynchronous timing events or spikes using models of biological neurons. It has been shown that these neurons can act as lossless encoders and software algorithms can reconstruct the encoded information. We present here, a real time implementation of a reconstruction algorithm on a TI TMS320C6713 DSK board. This implementation reconstructs the signal from a spike train which was encoded by a fabricated neuron chip. A signal to error ratio of 32 dB with an average firing rate of 8 KHz is obtained. We discuss the practical implementation, challenges and possible future directions.
Training MLPs layer-by-layer with the information potential

In the area of information processing one fundamental issue is how to measure the relationship between two variables based only on their samples. In a previous paper, the idea of information potential which was formulated from the so called quadratic mutual information was introduced, and successfully applied to problems such as blind source separation and pose estimation of SAR (synthetic aperture radar) 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.
A gradient-based variable step-size LMS algorithm

An algorithm is presented which uses the power of the filtered gradient estimate to form the step-size parameter mu . Each weight coordinate has its own variable step-size parameter to update more accurately the weight vector in the direction of the minimum of the performance surface. Performance results are presented which show that the gradient-based variable step-size algorithm is capable of faster convergence with less misadjustment than the classic LMS (least mean square) algorithm. This performance improvement holds in general for various signal-to-noise ratios and eigenvalue spreads, and for both stationary and nonstationary signals. The complexity is only 2-3 times that of the LMS algorithm, making it an attractive choice for many adaptive filtering applications. A drawback of this algorithm is that the misadjustment is directly proportional to the minimum mean square error, making this algorithm dependent upon the mean value of the signals to be filtered.<<ETX>>
A real-time reconstruction algorithm for the integrate and fire sampler

We provide a real-time recovery algorithm for the Integrate and Fire (IF) sampler. Specifically, we compare three reconstruction algorithms in terms of their accuracy and speed. The algorithms differ in their model of the input which include, 1) Global Fourier series, 2) Uniform cubic B-spline and 3) Radial basis with cubic B-spline generators. These are tested on neural recordings used in state-of-the-art Brain Machine Interfaces (BMI). We show that for a family of neural recordings and sampler parameters we obtain compression and accurate reconstruction in comparison to traditional uniform schemes.
Symbiotic Brain-Machine Interface decoding using simultaneous motor and reward neural representation

In this work, we design and test a framework for neural decoding in Brain-Machine Interfaces based on the Perception Action Reward Cycle (PARC). Here the neural decoder in the BMI learns to translate motor neural states in the primary motor cortex (M1) into actions based on a reward signal estimated directly from Neucleus Accumbens (NAcc). The control architecture was designed based on the Actor-Critic method of Reinforcement Learning. We tested the decoding performance by simultaneous recording the M1 and NAcc neural data in a rat during a robot-assisted reaching task. This work shows that a BMI can be trained from a naïve state to perform a reaching task using motor and error feedback signals directly from the brain.
An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS)

This paper proposes a nonlinear adaptive decoder for somatosensory micro-stimulation based on the kernel least mean square (KLMS) algorithm applied directly on the space of spike trains. Instead of using a binned representation of spike trains, we transform the vector of spike times into a function in reproducing kernel Hilbert space (RKHS), where the inner product of two spike time vectors is defined by a nonlinear cross intensity kernel. This representation encapsulates the statistical description of the point process that generates the spike trains, and bypasses the curse of dimensionality-resolution of the binned spike representations. We compare our method with two other methods based on binned data: GLM and KLMS, in reconstructing biphasic micro-stimulation. The results indicate that the KLMS based on RKHS for spike train is able to detect the timing, the shape and the amplitude of the biphasic stimulation with the best accuracy.
Spatio-temporal self-organizing feature maps

Thus far, the success of capturing and classifying temporal information with neural networks has been limited. Our methodology adds a spatio-temporal coupling to the self-organizing feature map (SOFM) which creates temporally and spatially localized neighborhoods in the map. The spatio-temporal coupling is based on traveling waves of activity which start at each winning node and are naturally attenuated over time. When these traveling waves reinforce each other, temporal activity wavefronts are created which are then used to enhance a node's possibility of winning the next competition. The spatiotemporal coupling is easily implemented with only local connectivity and calculations. Once trained, the spatio-temporal SOFM can be used for detection or for partial pattern recall. The methodology gracefully handles time-warping and multiple patterns with overlapping input vectors.
A Unified Framework for Quadratic Measures of Independence

This paper proposes a unified framework for several available measures of independence by generalizing the concept of information theoretic learning (ITL). The key component of ITL is the use of inner product between two density functions as a measure of similarity between two random variables. We show that by generalizing the inner product using a symmetric strictly positive-definite kernel and by choosing appropriate kernels, it is possible to reproduce a number of popular measures of independence. This unified framework also allows the design of new strictly positive-definite kernels and corresponding measures of independence. Following this framework we explore a new measure of independence and apply it in the context of linear independent component analysis (ICA). An attractive property of the proposed method is that it does not involve any free parameter and we demonstrate that it performs equally well compared to the existing methods for ICA.
Optimal parameter estimation of the Laguerre filter via the complex performance function

The adaptation of the optimal Laguerre filter parameters to model the impulse response of an unknown ARMA system is a parametric least squares problem. Unfortunately the squared error (SE) performance function is nonlinear with respect to the feedback parameter and is difficult to solve. We offer a new approach to determine the optimal feedback parameter, also called the time scale. The methodology used here is one of extending the SE performance function to the complex domain. This is done by defining a new complex performance function. We show how this performance function is derived, how it relates to the conventional function, and the additional information it offers. In particular, we observe (experimentally) that for ARMA systems the imaginary component of the new function can be used to obtain a set of values that contains the optimal (in the minimum SE sense) time scale parameter of the Laguerre model.
Analysis of coarse parallel architectures for artificial neural processing

A methodology for comparing various neural architectures and implementations is illustrated. The methodology consists of writing the artificial neural network (ANN) equations in a summation form and the applying a tool termed algorithmic timing parameter decomposition (ATPD). ATPD decomposes an algorithm or set of equations into a computation time formula comprising basic system primitives. A particular architecture has a corresponding computational time formula. Similarly, the primitive elements are dependent on the actual hardware realization and thus will change with the processor used in the system. Computation times therefore can be estimated for different parallel architectures. Implementation of a multilayer perceptron is analyzed in several digital signal processor (DSP)-based parallel architectures.<<ETX>>
Robust C-Loss Kernel Classifiers

The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.
Mixed generative and supervised learning modes in Deep Predictive Coding Networks

In this paper we propose a modification of the Cognitive Architectures for Sensory Processing proposed by Chalasani and Principe. Here we keep the bottom-up data representation through generative models as before, but propose a top-down flow based on backpropagation of gradients for recognition. By treating the bottom-up procedure involved in the inference step as a recursive neural network, we show that supervised learning can be used in conjunction with other layers commonly used for Deep Learning. Also, this allows us to learn models that incorporate at the same time data classification and statistical modeling of the input. We show that this combination provides classification results that are robust to input noise.
A gamma memory neural network for system identification

A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static backpropagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.<<ETX>>
Simultaneous extraction of Principal Components using givens rotations and output variances

Principal Components Analysis (PCA) is an invaluable statistical tool in signal processing. In many cases, an on-line algorithm to adapt the PCA network to determine the principal projections in the input space is desired. Algorithms proposed until now use the traditional deflation or the inflation procedure to determine the intermediate components sequentially, after the convergence of the principal or minor component is achieved. In this paper, we propose a constrained linear network and a robust cost function to determine any number of principal components simultaneously. The topology exploits the fact that the eigenvector matrix sought is orthonormal. A gradient-based algorithm named SIPEX-G is also presented.
Fixed-budget kernel recursive least-squares

We present a kernel-based recursive least-squares (KRLS) algorithm on a fixed memory budget, capable of recursively learning a nonlinear mapping and tracking changes over time. In order to deal with the growing support inherent to online kernel methods, the proposed method uses a combined strategy of growing and pruning the support. In contrast to a previous sliding-window based technique, the presented algorithm does not prune the oldest data point in every time instant but it instead aims to prune the least significant data point. We also introduce a label update procedure to equip the algorithm with tracking capability. Simulations show that the proposed method obtains better performance than state-of-the-art kernel adaptive filtering techniques given similar memory requirements.