nils-herrmann/clean_scirepeval huggingface dataset:
ASSOCIATIVE MEMORY AND HIPPOCAMPAL PLACE CELLS

Many hippocampal pyramidal neurons in rats are selectively activated at specific places in the environment. We present a network model for the CA3 area of the hippocampus. The network produced place selective activity even when the external sensory input was broadly tuned and noisy. The model predicts that the place fields should be nonuniformly distributed, clustering in the places where the synaptic interactions between neurons is strongeest. This may occur at locations of special significance, such as locations where there has been food in the past.
Fast Kinetic Models for Simulating AMPA, NMDA, GABA A and GABA B Receptors

Since the introduction of the alpha function by Rall in 1967 , there has been significant progress in our understanding of the molecular events underlying synaptic transmission. Particular receptor types have been identified and their activation kinetics characterized. It is now possible to develop models of these receptors, using a formalism similar to that introduced by Hodgkin and Huxley . In this paper, we present recently-introduced models obtained by simplifying more detailed biophysical models of postsynaptic receptors . The simplified models are fully compatible with the Hodgkin-Huxley formalism, are very efficient to simulate, and account for important phenomena such as synaptic summation and desensitization. These models should be useful in large-scale network simulations.
Learning and memory in the vestibulo-ocular reflex.

Studies of the neural basis of learning and memory in intact animals must, by their nature, start "from the top" by choosing a behavior that can be modified through learning, revealing how iaeuronal activity gives rise to that behavior, and then investigating, in the awake, behaving animal, changes in neural signaling that are associated with learning. Such studies also must recognize that the learning and memory expressed in the behavior of an animal will reflect both the properties of the neural network that mediates the behavior and the nature of the underlying changes in the operation of cells or synapses. In the past 10 years, there has been an explosion of information about learning and memory in the vestibulo-ocular reflex (VOR) of the awake, behaving monkey. At the same time, there have been unprecedented advances in understanding mechanisms of cellular plasticity such as long-term potentiation (LTP) in the hippocampus and long-term depression (LTD) in the cerebellum. A prerequisite for understanding learning and memory is to elevate specific mechanisms of cellular plasticity into cellular mechanisms of learning by establishing their function in the context of a neural system that mediates learning and memory in a particular behavior. Our review synthesizes the
Independent Component Analysis of Electroencephalographic Data

Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smearing of EEG data by volume conduction does not involve significant time delays, however, suggesting that the Independent Component Analysis (ICA) algorithm of Bell and Sejnowski  is suitable for performing blind source separation on EEG data. The ICA algorithm separates the problem of source identification from that of source localization. First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show: (1) ICA training is insensitive to different random seeds. (2) ICA may be used to segregate obvious artifactual EEG components (line and muscle noise, eye movements) from other sources. (3) ICA is capable of isolating overlapping EEG phenomena, including alpha and theta bursts and spatially-separable ERP components, to separate ICA channels. (4) Nonstationarities in EEG and behavioral state can be tracked using ICA via changes in the amount of residual correlation between ICA-filtered output channels.
Fast robust subject‐independent magnetoencephalographic source localization using an artificial neural network

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg‐Marquardt routine using the MLP output as its initial guess took 15 ms and improved accuracy to 0.53 cm, which approaches the natural limit on accuracy imposed by noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually assisted commercial software.  2005.
Bounds on Query Convergence

The problem of finding an optimum using noisy evaluations of a smooth cost function arises in many contexts, including economics, business, medicine, experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t - x*)^2 ] >= O(1/sqrt(t)) on the rate of convergence of a sequence (x_0, x_1, >...) generated by an unbiased feedback process observing noisy evaluations of an unknown quadratic function maximised at x*. The bound is tight, as the proof leads to a simple algorithm which meets it. We further establish a bound on the total regret, E[ sum_{i=1..t} (x_i - x*)^2 ] >= O(sqrt(t)) These bounds may impose practical limitations on an agent's performance, as O(eps^-4) queries are made before the queries converge to x* with eps accuracy.
Multimodal Integration: fMRI, MRI, EEG, MEG

This chapter provides a comprehensive survey of the motivations, assumptions and pitfalls associated with combining signals such as fMRI with EEG or MEG. Our initial focus in the chapter concerns mathematical approaches 
for solving the localization problem in EEG and MEG. Next we document the most recent and promising ways in which these signals can be combined with fMRI. Specically, we look at correlative analysis, decomposition techniques, equivalent dipole tting, distributed sources modeling, beamforming, and Bayesian methods. Due to difculties in assessing ground truth of a combined signal in any realistic experiment difculty further confounded by lack of accurate biophysical models of BOLD signal we are cautious to be optimistic about multimodal integration. Nonetheless, as we highlight and explore the technical and methodological difculties of fusing heterogeneous signals, it seems likely that correct fusion of multimodal data will allow previously inaccessible spatiotemporal structures to be visualized and formalized and thus eventually become a useful tool in brain imaging research.
Neuronal Predictions of Sparse Linear Representations

A striking feature of many sensory processing problems is that there appear to be many more neurons engaged in the internal representations of the signal than in its transduction. For example, humans have about 30,000 cochlear neurons, but at least a thousand times as many neurons in the auditory cortex. Although such apparently redundant internal representations have sometimes been proposed as necessary to overcome neuronal noise. We instead posit that they directly subserve computations of interest. We first review how sparse overcomplete linear representations can be used for source separation, reusing a particularly difficult case, the HRTF cue (the differential filtering imposed on a source by its path from its origin to the cochlea) as an example. We then explore some robust and generic predictions about neuronal representations that follow from taking sparse linear representations as a model of neuronal sensory processing.
Perturbation Confusion and Referential Transparency:Correct Functional Implementation of Forward-Mode AD

It is tempting to incorporate dierentiation operators into 
functional-programming languages. Making them rst-class citizens, however, is an enterprise fraught with danger. We discuss a potential problem with forward-mode AD common to many AD systems, including all attempts to integrate a forward-mode AD operator into Haskell. In particular, we show how these implementations fail to preserve referential 
transparency, and can compute grossly incorrect results when the dierentiation operator is applied to a function that itself uses that operator. The underlying cause of this problem is perturbation confusion, a failure to distinguish between distinct perturbations introduced by distinct invocations of the dierentiation operator. We then discuss how perturbation confusion can be avoided.
Survey of sparse and non‐sparse methods in source separation

Source separation arises in a variety of signal processing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing process and by making assumptions about the sources. When the information about the mixing process and sources is limited, the problem is called ‘blind’. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that solutions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non‐sparse methods. Indeed, sparse methods provide a powerful approach to the separation of linear mixtures of independent data. This paper surveys the recent arrival of sparse blind source separation methods and the previously existing non‐sparse methods, providing insights and appropriate hooks into theliterature along the way.
Soft-LOST: EM on a Mixture of Oriented Lines

Robust clustering of data into overlapping linear subspaces is a common problem. Here we consider one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We present an algorithm that identifies these subspaces using an EM procedure, where the E-step calculates posterior probabilities assigning data points to lines and M-step repositions the lines to match the points assigned to them. This method, combined with a transformation into a sparse domain and an L1-norm optimisation, constitutes a blind source separation algorithm for the under-determined case.
Attention and Optimal Sensory Codes

Neuronal activity can be modulated by attention even while the sensory stimulus is held fixed. This modulation implies changes in the tuning curve (or receptive field) of the neurons involved in sensory processing. We propose an information-theoretic hypothesis for the purpose of this modulation, and show using computer simulation that the similar modulation emerges in a system that is optimally encoding a sensory stimulus when the system is informed about the changing relevance of different features of the input. We present a simple model that learns a covert attention mechanism, given input patterns and tradeoff 
requirements. After optimization, the system gains the ability to reorganize its computational resources (or coding strategy) depending on the incoming covert attentional signal, using only threshold shifts in neurons throughout the network. The modulation of activity 
of the encoding units for different attentional states qualitatively matches that observed in animal selective attention experiments. Due to its generality, the model can be applied to any modality, and to any attentional goal.
Monaural Source Separation Using Spectral Cues

The acoustic environment poses at least two important challenges. First, animals must localise sound sources using a variety of binaural and monaural cues; and second they must separate sources into distinct auditory streams (the “cocktail party problem”). Binaural cues include intra-aural intensity and phase disparity. The primary monaural cue is the spectral filtering introduced by the head and pinnae via the head-related transfer function (HRTF), which imposes different linear filters upon sources arising at different spatial locations.
Transformations of Gaussian Process Priors

Gaussian process prior systems generally consist of noisy measurements of samples of the putatively Gaussian process of interest, where the samples serve to constrain the posterior estimate. Here we consider the case where the measurements are instead noisy weighted sums of samples. This framework incorporates measurements of derivative information and of filtered versions of the process, thereby allowing GPs to perform sensor fusion and tomography; allows certain group invariances (ie symmetries) to be weakly enforced; and under certain conditions suitable application allows the dataset to be dramatically reduced in size. The method is applied to a sparsely sampled image, where each sample is taken using a broad and non-monotonic point spread function. It is also applied to nonlinear dynamic system identification applications where a nonlinear function is followed by a known linear dynamic system, and where observed data can be a mixture of irregularly sampled higher derivatives of the signal of interest.
Hard-LOST: modified k-means for oriented lines

Robust clustering of data into linear subspaces is a common problem. Here we treat clustering into one-dimensional subspaces that cross the origin. This problem arises in blind source separation, where the subspaces correspond directly to columns of a mixing matrix. We present an algorithm that identifies these subspaces using a modified k-means procedure, where line orientations and distances from a line replace the cluster centres and distance from cluster centres of conventional k-means. This method, combined with a transformation into a sparse domain and an L1-norm optimisation, constitutes a blind source separation algorithm for the under-determined case.
Sparse separation: principles and tricks

Blind separation of linearly mixed white Gaussian sources is impossible, due to rotational symmetry. For this reason, all blind separation algorithms are based on some assumption concerning the fashion in which the situation departs from that insoluble case. Here we discuss the assumption of sparseness and try to put various algorithms that make the sparseness assumption in a common framework. The main objective of this paper is to give some rough intuitions, and to provide suitable hooks into the literature.
A Normative Model of Attention: Modulation of Neural Response

When a sensory stimulus is encoded in a lossy fashion for ef- ficient transmission, there are necessarily tradeoffs between the represented fidelity of various aspects of the input pat- tern. In the model of attention presented here, a top-down signal informs the encoder of these tradeoffs. Given an en- semble of input patterns and tradeoff requirements, our sys- tem can learn to encode its inputs optimally. This general model is instantiated in a simple network: an autoencoder with a bottleneck, innervated by a top-down attentional sig- nal, trained using backpropagation. The only information the encoder receives concerning the semantics of the top-down attentional signal is from the optimization criterion, which penalizes the system more heavily for errors made near a simple attentional spotlight. The modulation of neural activ- ity learned by this model qualitatively matches that measured in animals during covert visual attention tasks.
MEG source localization using an MLP with a distributed output representation

We present a system that takes realistic magnetoencephalographic (MEG) signals and localizes a single dipole to reasonable accuracy in real time. At its heart is a multilayer perceptron (MLP) which takes the sensor measurements as inputs, uses one hidden layer, and generates as outputs the amplitudes of receptive fields holding a distributed representation of the dipole location. We trained this Soft-MLP on dipolar sources with real brain noise and converted the network's output into an explicit Cartesian coordinate representation of the dipole location using two different decoding strategies. The proposed Soft-MLPs are much more accurate than previous networks which output source locations in Cartesian coordinates. Hybrid Soft-MLP-start-LM systems, in which the Soft-MLP output initializes Levenberg-Marquardt, retained their accuracy of 0.28 cm with a decrease in computation time from 36 ms to 30 ms. We apply the Soft-MLP localizer to real MEG data separated by a blind source separation algorithm, and compare the Soft-NMP dipole locations to those of a conventional system.
Filtered Gaussian Processes for Learning with Large Data-Sets

Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a small-dimensional set of filtered data that keeps a high proportion of the information contained in the original large data-set. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.
Subject-Independent Magnetoencephalographic Source Localization by a Multilayer Perceptron

We describe a system that localizes a single dipole to reasonable accuracy from noisy magnetoencephalographic (MEG) measurements in real time. At its core is a multilayer perceptron (MLP) trained to map sensor signals and head position to dipole location. Including head position overcomes the previous need to retrain the MLP for each subject and session. The training dataset was generated by mapping randomly chosen dipoles and head positions through an analytic model and adding noise from real MEG recordings. After training, a localization took 0.7 ms with an average error of 0.90 cm. A few iterations of a Levenberg-Marquardt routine using the MLP's output as its initial guess took 15 ms and improved the accuracy to 0.53 cm, only slightly above the statistical limits on accuracy imposed by the noise. We applied these methods to localize single dipole sources from MEG components isolated by blind source separation and compared the estimated locations to those generated by standard manually-assisted commercial software.
Independent components of magnetoencephalography: Localization and single-trial response onset detection

Independent component analysis (ICA) is a class of decomposition methods that separate sources from mixtures of signals. In this chapter, we used second order blind identification (SOBI), one of the ICA method, to demonstrate its advantages in identifying magnetic signals associated with neural information processing. Using 122-channel MEG data collected during both simple sensory activation and complex cognitive tasks, we explored SOBI’s ability to help isolate and localize underlying neuronal sources, particularly under relatively poor signal-to-noise conditions. For these identified and localized neuronal sources, we developed a simple threshold-crossing method, with which single-trial response onset times could be measured with a detection rate as high as 96%. These results demonstrated that, with the aid of ICA, it is possible to non-invasively measure human single trial response onset times with millisecond resolution for specific neuronal populations from multiple sensory modalities. This capability makes it possible to study a wide range of perceptual and memory functions that critically depend on the timing of 
discrete neuronal events.
Localization and Single-Trial Response Onset Detection

Independent component analysis (ICA) is a class of decomposition methods that separate sources from mixtures of signals. In this chapter, we used second order blind identification (SOBI), one of the ICA method, to demonstrate its advantages in identifying magnetic signals associated with neural information processing. Using 122-channel MEG data collected during both simple sensory activation and complex cognitive tasks, we explored SOBI’s ability to help isolate and localize underlying neuronal sources, particularly under relatively poor signal-to-noise conditions. For these identified and localized neuronal sources, we developed a simple threshold-crossing method, with which single-trial response onset times could be measured with a detection rate as high as 96%. These results demonstrated that, with the aid of ICA, it is possible to non-invasively measure human single trial response onset times with millisecond resolution for specific neuronal populations from multiple sensory modalities. This capability makes it possible to study a wide range of perceptual and memory functions that critically depend on the timing of discrete neuronal events.
Algorithmic Differentiation through Convergent Loops

We consider an explicit iterate-to-fixedpoint operator and derive associated rules for both forward and reverse mode algorithmic differentiation. Like other AD transformation rules, these are exact and efficient. In this case, they generate code which itself invokes the iterate-to-fixedpoint operator. Loops which iterate until a variable changes less than some tolerance should be regarded as approximate iterate-to-fixedpoint calculations. After a convergence analysis, we contend that it is best both pragmatically and theoretically to find the approximate fixedpoint of the adjoint system of the actual desired fixedpoint calculation, rather than find the adjoint of the approximate primal fixedpoint calculation. Our exposition unifies and formalizes a number of techniques already known to the AD community, introduces a convenient and powerful notation, and opens the door to fully automatic efficient AD of a broadened class of codes.
Nonlinear time series analysis of human alpha rhythm

Nonlinearity is often deduced by showing that a dataset signi£cantly deviates from its phase randomized versions, i.e. surrogate data. For real data, however, non-stationarities like artifacts and onsets and offsets of rhythmic activity will cause false positives. We propose a new test which detects dynamical nonlinearity by measuring time-asymmetry, using surrogate data merely to estimate the standard deviation of the process. 
The method is applied to multi-channel MEG measurements of ongoing alpha-band activity modulated by a simple visual 
memory task involving motor activity. The signal to noise ratio was enhanced using ICA, and the analysis was performed 
on a single separated source. We found that, if the peak at 10 Hz is accompanied by a substantial higher harmonic, time 
asymmetry can be detected signifcantly in virtually any epoch of 3 second duration. Finally, we applied our recently proposed method to estimate correlation dimension for noisy data. We found very satisfactory scaling plots with dimension around 1.5. As a byproduct, we showed that the nondeterministic fraction can be explained almost completely by external noise.