nils-herrmann/clean_scirepeval huggingface dataset:
Learning the contributions of the motor, premotor, and posterior parietal cortices for hand trajectory reconstruction in a brain machine interface

The ability to record, in real-time, the activity of hundreds of cortical neurons gives the ability to selectively study the function of clusters of cortical neurons in Brain Machine Interface (BMI) experiments. We have demonstrated using a recursive multilayer perceptron (RMLP) that using the appropriate signal processing theory in a well-chosen parsimonious model, we can develop constructs that agree with basic physiological modeling of neural control. By looking through the trained model, we have found interesting relationships between the neuronal firing and the movement. The RMLP allows us to continuously study the relationship between neural activity and behavior without the active interference of the experimenter. The findings presented in this study offer an opportunity for the neuroscience community to compare the cortical interactions as constructed by the RMLP to what is known about motor neurophysiology.
Generalized eigendecomposition with an on-line local algorithm

This article presents a novel, on-line, local learning algorithm to obtain generalized eigenvalues and their corresponding eigenvectors in descending order with a linear adaptive filter. The filter is composed of a set of forward linear projections constrained by lateral inhibitions. Equilibrium points of the adaptation process and their stability are briefly analyzed. Simulations and experimental comparisons are given to verify the validity and effectiveness of the proposed algorithm.
Correntropy: Properties and Applications in Non-Gaussian Signal Processing

The optimality of second-order statistics depends heavily on the assumption of Gaussianity. In this paper, we elucidate further the probabilistic and geometric meaning of the recently defined correntropy function as a localized similarity measure. A close relationship between correntropy and M-estimation is established. Connections and differences between correntropy and kernel methods are presented. As such correntropy has vastly different properties compared with second-order statistics that can be very useful in non-Gaussian signal processing, especially in the impulsive noise environment. Examples are presented to illustrate the technique.
Generalized Oja's rule for linear discriminant analysis with Fisher criterion

Online learning rules for both principal component analysis (PCA) and linear discriminant analysis (LDA) with Fisher criterion are analyzed under the same framework, and a generalized Oja's rule for both is derived. For the LDA problem, the relationship between the Fisher criterion and the criterion of minimizing mean square error (MSE) is discussed. The experiments show that the convergence speed of the generalized Oja's rule as an adaptive method for the Fisher criterion is much faster than that of gradient descent method for the MSE criterion.
Chaotic dynamics of time-delay neural networks

A study is made of the nonlinear dynamical properties of a class of recurrent artificial neural networks with time delay that have been proposed in the literature to learn and recognize time-varying input patterns. Their nonlinear dynamics are studied to see if they have the potential to model nonlinear dynamical behavior of biological field potentials such as the electroencephalogram (EEG). It is shown that changing the value of the weights in a periodic fashion causes the output of time-delay neural networks to show a correlation dimension in the EEG range
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks

We propose a convolutional recurrent neural network, with Winner-Take-All dropout for high dimensional unsupervised feature learning in multi-dimensional time series. We apply the proposedmethod for object recognition with temporal context in videos and obtain better results than comparable methods in the literature, including the Deep Predictive Coding Networks previously proposed by Chalasani and Principe.Our contributions can be summarized as a scalable reinterpretation of the Deep Predictive Coding Networks trained end-to-end with backpropagation through time, an extension of the previously proposed Winner-Take-All Autoencoders to sequences in time, and a new technique for initializing and regularizing convolutional-recurrent neural networks.
Experimental upper bound for the performance of convolutive source separation methods

An important problem in the field of blind source separation (BSS) of real convolutive mixtures is the determination of the role of the demixing filter structure and the criterion/optimization method in limiting separation performance. This issue requires the knowledge of the optimal performance for a given structure, which is unknown for real mixtures. Herein, the authors introduce an experimental upper bound on the separation performance for a class of convolutive blind source separation structures, which can be used to approximate the optimal performance. As opposed to a theoretical upper bound, the experimental upper bound produces an estimate of the optimal separating parameters for each dataset in addition to specifying an upper bound on separation performance. Estimation of the upper bound involves the application of a supervised learning method to the set of observations found by recording the sources one at a time. Using the upper bound, it is demonstrated that structures other than the finite-impulse-response (FIR) structure should be considered for real (convolutive) mixtures, there is still much room for improvement in current convolutive BSS algorithms, and the separation performance of these algorithms is not necessarily limited by local minima.
Dynamic sparse coding with smoothing proximal gradient method

In this work we focus on the problem of estimating time-varying sparse signals from a sequence of under-sampled observations. We formulate this problem as estimating hidden states in a dynamic model and exploit the underlying temporal structure to find a more accurate solution, particularly when the information in the observations is at scarce. We propose an optimization procedure based on smoothing proximal gradient method to estimate these hidden states. We show that the proposed model is efficient and more robust to the noise in the system.
A synthetic averaging method on point process for motor Brain Machine Interfaces

Previous decoding algorithms for brain machine interfaces (BMIs) are normally utilized to estimate animalpsilas movement from binned spike rates, which loses spike timing resolution and may exclude rich neural dynamics due to single spikes. The Monte Carlo sequential estimation on point process enables the decoding directly from the multi-neuron spike trains without constraints on posterior density estimation. However, it poses the problem to bridge the time scale gap between the spike events and the behaviors, and generates stochastic kinematic estimation due to the randomness embedded in the time structure of the spike trains. We propose here a synthetic averaging method to mimic the neural population effects by generating synthetic spike trains from neural recordings and utilizing them as extra model observation. The decoding performance by Monte Carlo SE is averaged in the kinematics domain, which preserves the resolution of the neuron activity and bypasses the possible distortion by nonlinear tuning function due to the binning in spike domain. Tested in simulation and real data, synthetic averaging provides better reconstructions with less NMSE.
Dynamical analysis of neural oscillators in an olfactory cortex model

This paper presents a theoretical approach to understand the basic dynamics of a hierarchical and realistic computational model of the olfactory system proposed by W. J. Freeman. While the system's parameter space could be scanned to obtain the desired dynamical behavior, our approach exploits the hierarchical organization and focuses on understanding the simplest building block of this highly connected network. Based on bifurcation analysis, we obtain analytical solutions of how to control the qualitative behavior of a reduced KII set taking into consideration both the internal coupling coefficients and the external stimulus. This also provides useful insights for investigating higher level structures that are composed of the same basic structure. Experimental results are presented to verify our theoretical analysis.
Online learning using a Bayesian surprise metric

Dictionary.com defines learning as the process of acquiring knowledge. In psychology, learning is defined as the modification of behavior through training. In our work, we combine these definitions to define learning as the modification of a system model to incorporate the knowledge acquired by new observations. During learning, the system creates and modifies a model to improve its performance. As new samples are introduced, the system updates its model based on the new information provided by the samples. However, this update may not necessarily improve the model. We propose a Bayesian surprise metric to differentiate good data (beneficial) from outliers (detrimental), and thus help to selectively adapt the model parameters. The surprise metric is calculated based on the difference between the prior and the posterior distributions of the model when a new sample is introduced. The metric is useful not only to identify outlier data, but also to differentiate between the data carrying useful information for improving the model and those carrying no new information (redundant). Allowing only the relevant data to update the model would speed up the learning process and prevent the system from overfitting. The method is demonstrated in all three learning procedures: supervised, semi-supervised and unsupervised. The results show the benefit of surprise in both clustering and outlier detection.
Trimmed affine projection algorithms

The least trimmed squares (LTS) estimator is a robust estimator as it can avoid undue influence from outliers. The exact solution of the LTS estimation is however hard to And and if the number of data is large then the method is unfeasible. In this work, we apply the LTS criterion to adaptive filtering and develop the trimmed affine projection algorithm (TAPA) and kernel trimmed affine projection algorithm (KTAPA). The proposed adaptive algorithms are very robust to outliers and have low computational complexity. Simulation results conflrm their excellent and robust performance.
Sparse channel estimation with regularization method using convolution inequality for entropy

In this paper, we show that the sparse channel estimation problem can be formulated as a regularization problem between mean squared error (MSE) and the L1-norm constraint of the channel impulse response. A simple adaptive method to solve regularization problem using the convolution inequality for entropy is proposed. Performance of this proposed regularization method is compared to the Wiener filter, the matching pursuit (IMP) algorithm and the information criterion based method. The results show that the estimate of the sparse channel using the MSE criterion with the L1-norm constraint outperforms the Wiener filter and the conventional sparse solution methods in terms of MSE of the estimates and the generalization performance.
Design and performance analysis of a novel nanoscale associative memory

This paper proposes a nanoscale associative memory architecture. The type of associative memory considered is adept at handling the irregular patterning of nanoscale components but requires an encoding scheme to be useful. We show how a randomly configured interface between the micro and nano scales can accomplish this encoding. The resulting associative memory eliminates some of the supporting microscale components required by the original crossbar architecture at the cost of reduced storage capacity. If the projections made by Kuekes et al. about the potential size of their memory prove correct, this associative memory configuration may be a valuable alternative.
Estimation and visualization of neuronal functional connectivity in motor tasks

In brain-machine interface (BMI) modeling, the firing patterns of hundreds of neurons are used to reconstruct a variety of kinematic variables. The large number of neurons produces an explosion in the number of free parameters, which affects model generalization. This paper proposes a model-free measure of pairwise neural dependence to rank the importance of neurons in neural to motor mapping. Compared to a model-dependent approach such as sensitivity analysis, sixty percent of the neurons with the strongest dependence coincide with the top 10 most sensitive neurons trained through the model. Using this data-driven approach that operates on the input data alone, it is possible to perform neuron selection in a more efficient way that is not subject to assumptions about decoding models. To further understand the functional dependencies that influence neural to motor mapping, we use an open source available graph visualization toolkit called Prefuse to visualize the neural dependency graph and quantify the functional connectivity in motor cortex. This tool when adapted to the analysis of neuronal recordings has the potential to easily display the relationships in data of large dimension.
Long-term scalp epileptic EEG quantification with GMA dynamics

The paper concerns the problem of automatic seizure detection based on scalp EEG and proposes to employ the generalized measure of association (GMA) to quantify the statistical dependencies and infer the dynamical interactions of brain regions with the focus area. The experimental results with clinical recordings show that the estimated GMA values changes dramatically before and during epileptic seizures reflecting the dynamic coupling and decoupling between brain regions, which can be an useful measure to quantify epileptic EEG signals.
Velocity measurement of granular flow with a Hopfield network

The transport of granular flow is common to many industrial processes. This paper discusses a methodology to measure the velocity of dry granular solids down an inclined chute using high speed digital images. Acrylic particles have been used as granular solids in our experiment. First, particles are located using normalized correlation. A technique for measuring the velocities of individual acrylic particles is developed based on a Hopfield network to solve the particle correspondence problem between successive images. A new, rigidity constraint is applied to the Hopfield energy function, and the results show better performance than the conventional cost function.
Generalized feedforward filters with complex poles

The authors propose an extension to an existing structure, the gamma filter, replacing the real pole on the tap-to-tap transfer function with a pair of complex conjugate poles and a zero. The new structure is, like the gamma filter, an IIR filter with restricted feedback whose stability is trivial to check. While the gamma filter decouples the memory depth from the filter order for low-pass signals, the proposed structure decouples the memory depth and the central frequency from the filter order for band-pass signals. The learning equations of the model parameters are presented and shown to introduce an additive O(p) complexity to the backpropagation algorithm, where p is the filter order. The error surface for a linear filter is investigated in a system identification context, and the presence of local minima is confirmed. The performance of the proposed model was found to be better than that of the time-delay neural net in a nonlinear system identification context.<<ETX>>
Information Theoretic Vector Quantization with Fixed Point Updates

In this paper, we revisit information theoretic vector quantization (ITVQ) algorithm introduced in (T. Lehn-Schioler et al., 2005) and make it practical. We derive a fixed point update rule to minimize the Cauchy-Schwartz(CS) pdf divergence between the set of codewords and the actual data. In doing so, we overcome two severe deficiencies of the previous gradient based method namely, the number of parameters to be optimized and slow convergence rate, thus making this algorithm more efficient and useful as a compression algorithm.
Temporal self-organization through competitive prediction

Two self-organizing principles for the competitive identification and segmentation of piecewise stationary time series are described. In the first, a neighborhood map of one step predictors competes for the data during training. The winner is granted the largest parameter update, while other predictors are allowed smaller updates, decreasing with distance from the winner on the neighborhood map. In addition to performing piecewise segmentation and identification, the technique maps similar segments of the time series as neighbors on the neighborhood map. In the second, we propose a new cost function for competitive prediction that imbeds memory in the error metric and couples the memory with the degree of competition. Performing gradient descent on the cost function yields a self-annealing system that can also perform piecewise segmentation and identification of a time series.
Accurate initialization of neural network weights by backpropagation of the desired response

Proper initialization of neural networks is critical for a successful training of its weights. Many methods have been proposed to achieve this, including heuristic least squares approaches. In this paper, inspired by these previous attempts to train (or initialize) neural networks, we formulate a mathematically sound algorithm based on backpropagating the desired output through the layers of a multilayer perceptron. The approach is accurate up to local first order approximations of the nonlinearities. It is shown to provide successful weight initialization for many data sets by Monte Carlo experiments.
A fast on-line generalized eigendecomposition algorithm for time series segmentation

This paper presents a novel, fast converging on-line rule for generalized eigendecomposition (GED) and its application in time series segmentation. We adopt the concepts of deflation and power method to iteratively estimate the generalized eigencomponents. The algorithm is guaranteed to produce stable results. In the second half of the paper, we discuss the application of GED to segment time series. GED is tested for chaotic time series and speech. The simulation results are compared with the venerable Generalized Likelihood Ratio Test (GLR) as a benchmark to gauge performance.
Optimizing microstimulation using a reinforcement learning framework

The ability to provide sensory feedback is desired to enhance the functionality of neuroprosthetics. Somatosensory feedback provides closed-loop control to the motor system, which is lacking in feedforward neuroprosthetics. In the case of existing somatosensory function, a template of the natural response can be used as a template of desired response elicited by electrical microstimulation. In the case of no initial training data, microstimulation parameters that produce responses close to the template must be selected in an online manner. We propose using reinforcement learning as a framework to balance the exploration of the parameter space and the continued selection of promising parameters for further stimulation. This approach avoids an explicit model of the neural response from stimulation. We explore a preliminary architecture — treating the task as a k-armed bandit — using offline data recorded for natural touch and thalamic microstimulation, and we examine the methods efficiency in exploring the parameter space while concentrating on promising parameter forms. The best matching stimulation parameters, from k = 68 different forms, are selected by the reinforcement learning algorithm consistently after 334 realizations.
Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the data through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.
Invexity of the Minimum Error Entropy Criterion

In this letter, optimization properties of Minimization of Error Entropy (MEE) and Minimization of Error Entropy with Fiducial points (MEEF) are presented. It is proved that by varying the kernel parameter of the MEE and/or MEEF objective function, the resulting problem, in general leads to an invex problem. Furthermore, for certain values of the kernel parameter it is shown that the problems may transform to convex or pseudo-convex problems.
Performance and training strategies in feedforward neural networks: an application to sleep scoring

A comparison is made of the performance of single- and multilayer perceptrons in the scoring of sleep stages under different training conditions. The input to the neural network is a set of feature vectors, and the sleep staging is the output. Performance is the degree of agreement with the human scorer. For this application the single-layer perceptron performed at the same level as the multilayer perceptron. The best strategy for training the network is the use of human a priori knowledge. The neural network performed at the same level as other much more difficult to implement pattern recognition schemes.<<ETX>>
Training neural networks with additive noise in the desired signal

A new global optimization strategy for training adaptive systems such as neural networks and adaptive filters [finite or infinite impulse response (FIR or IIR)] is proposed in this paper. Instead of adding random noise to the weights as proposed in the past, additive random noise is injected directly into the desired signal. Experimental results show that this procedure also speeds up greatly the backpropagation algorithm. The method is very easy to implement in practice, preserving the backpropagation algorithm and requiring a single random generator with a monotonically decreasing step size per output channel. Hence, this is an ideal strategy to speed up supervised learning, and avoid local minima entrapment when the noise variance is appropriately scheduled.
Entropy manipulation of arbitrary nonlinear mappings

We discuss an unsupervised learning method which is driven by an information theoretic based criterion. The method differs from previous work in that it is extensible to a feed-forward multilayer perceptron with an arbitrary number of layers and makes no assumption about the underlying PDF of the input space. We show a simple unsupervised method by which multidimensional signals can be nonlinearly transformed onto a maximum entropy feature space resulting in statistically independent features.