nils-herrmann/clean_scirepeval huggingface dataset:
Extended ICA Removes Artifacts from Electroencephalographic Recordings

Severe contamination of electroencephalographic (EEG) activity by eye movements, blinks, muscle, heart and line noise is a serious problem for EEG interpretation and analysis. Rejecting contaminated EEG segments results in a considerable loss of information and may be impractical for clinical data. Many methods have been proposed to remove eye movement and blink artifacts from EEG recordings. Often regression in the time or frequency domain is performed on simultaneous EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. However, EOG records also contain brain signals [1, 2], so regressing out EOG activity inevitably involves subtracting a portion of the relevant EEG signal from each recording as well. Regression cannot be used to remove muscle noise or line noise, since these have no reference channels. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records. The method is based on an extended version of a previous Independent Component Analysis (ICA) algorithm [3, 4] for performing blind source separation on linear mixtures of independent source signals with either sub-Gaussian or super-Gaussian distributions. Our results show that ICA can effectively detect, separate and remove activity in EEG records from a wide variety of artifactual sources, with results comparing favorably to those obtained using regression-based methods.
Effects of cholinergic modulation on responses of neocortical neurons to fluctuating input.

Neocortical neurons in vivo are spontaneously active and intracellular recordings have revealed strongly fluctuating membrane potentials arising from the irregular arrival of excitatory and inhibitory synaptic potentials. In addition to these rapid fluctuations, more slowly varying influences from diffuse activation of neuromodulatory systems alter the excitability of cortical neurons by modulating a variety of potassium conductances. In particular, acetylcholine, which effects learning and memory, reduces the slow alterhyperpolarization, which contributes to spike frequency adaptation. We used whole-cell patch-clamp recordings of pyramidal neurons in neocortical slices and computational simulations to show, first, that when fluctuating inputs were added to a constant current pulse, spike frequency adaptation was reduced as the amplitude of the fluctuations was increased. High-frequency, high-amplitude fluctuating inputs that resembled in vivo conditions exhibited only weak spike frequency adaptation. Second, bath application of carbachol, a cholinergic agonist, significantly increased the firing rate in response to a fluctuating input but minimally displaced the spike times by < 3 ms, comparable to the spike jitter observed when a visual stimulus is repeated under in vivo conditions. These results suggest that cholinergic modulation may preserve information encoded in precise spike timing, but not in interspike intervals, and that cholinergic mechanisms other than those involving adaptation may contribute significantly to cholinergic modulation of learning and memory.
A MODEL OF RETICULAR THALAMIC SPINDLE OSCILLATIONS MEDIATED BY GABAA DEPOLARIZATION

Intracellular recordings from reticular thalamic (RE) neurons in vivo suggest that in RE cells hyperpolarized below the Clreversal potential, reversed inhibitory postsynaptic potentials (IPSPs) can directly trigger a low-threshold Ca2+ spike. In a large-scale, two-dimensional (2D) network model of the RE nucleus with membrane potentials lower than the Clreversal potential, GABAA-mediated synaptic excitation led to propagating patterns of spike-burst activity, which could be transformed into self-sustained oscillations ( 1999). However, the mean membrane potential of the majority of RE cells in vivo is more positive than the Clreversal potential. Here we investigate whether the patterns of RE excitation can propagate or sustain in the 2D RE network model if only a small fraction of RE cells is hyperpolarized below the GABAA reversal potential. Resting potentials of RE cells were randomly set between -65 and -75 mV. Self-sustained oscillations were found in the 2D RE network when only 25% of RE cells were hyperpolarized below the Clreversal potential. Activity patterns appeared in the form of spiral waves moving irregularly over the network domain. The size of these patterns decreased when the fraction of cells depolarized above -70 mV increased. The average membrane potential exhibited recurring periods of oscillations in the frequency range 10-15 Hz with duration that depended on network size and synaptic and intrinsic parameters of RE cells. Thus, this model predicts that GABAA-mediated excitation can robustly generate sequences of spindle-like oscillations in the isolated RE nucleus. Supported by: HHMI, MRC, HFSP
A simple model for development and function of long-range connections in neocortex

We present a model for the development of long-range horizontal connections in cortex. Fixed short-range interactions with local excitatory feedback and an inhibitory surround induce localized attractors (‘activity blobs’) that depend sensitively on small variations of the input. This provides the basic mechanism for the putative role of the long-range horizontal connections: to switch between different attractors and to organize rapidly the grouping of distant neurons. We show that Hebbian adaptation of sparse long-range horizontal connections under these conditions is sufficient to break the initial symmetry of homogeneous connectivity. This can induce a strong dependency of the local responses on the input from outside the classical receptive fields. This grouping mechanism may explain a wide range of physiological and psychophysical observations.
Controversies and issues in developmental theories of mind: Some constructive remarks

As the commentaries reveal, cognitive neuroscience's first steps toward a theory of development are marked by vigorous debate, ranging from basic points of definition to the fine details of mechanism. In this Response, we present the neural constructivist position on this broad spectrum of issues, from basic questions such as what sets constructivism apart from other theories (particularly selectionism) to its relation to behavioral theories and to its underlying mechanisms. We conclude that the real value of global theories at this stage of cognitive neuroscience is not just their answers but the new set of research questions they pose.
Technology. His Current Research Interests In- Clude Neural Computation, Statistical Learning Theory, and Handwriting Recognition. Kwok and Yeung: Constructive Algorithms for Structure Learning \some Approximation Properties of Projection Pursuit Learning Networks," in Advances in Neural Information

15 63] L.K. Jones, \A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training," The Annals of Statistics,cursive dynamic node creation in multilayer neural networks," \Use of a quasi-Newton method in a feedforward neural network construction algorithm," IEEE Wahba, \Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation," Numerische Mathe-13 vergence is also an important theoretical yet crucial practical issue. Recent results 62], 63], 131], 132], 133] have shown that, under certain regularity conditions, the approximation error typically improves as O(1 n), where n is the number of hidden units in the network. However, as mentioned in Section II-D.3, some of these results are not applicable to constructive algorithms when a greedy approach is taken, whereas others are applicable to greedy algorithms but with the detailed conditions diierent from those used in practice. For example, in both 62] and 63], the iterative sequence of network estimates is formed from a convex combination of the previous network function f n?1 and the new hidden unit function g n , (7) where 0 n 1. In algorithms like the cascade-correlation algorithm (Section IV-C), however, the new f n is formed from full linear combination of the old and new hidden unit functions. The weights connecting the old hidden units to the output unit are thus not constrained as a group, as in (7). Besides, in (7), n must be learned together with the new hidden unit g n , while in the cascade-correlation algorithm, the parameters of the new hidden unit are rst learned and then the output layer weights. Moreover, the objective function to be minimized in 62], 63] is diierent from that in the cascade-correlation algorithm. Modifying and applying these useful theoretical results to the analysis of diierent constructive algorithms is thus beneecial. Some initial progress has been reported in 119], 120], 121]. As mentioned in Section II-C, one must decide when to stop the constructive algorithm. This is important to achieve a proper bias-variance trade-oo. A good stopping criterion will be one based on an estimate of the generalization performance of the network. But computing an accurate estimation eeciently for neural networks is not an easy problem to solve, and this will still be an important research topic. Up to now, a comprehensive performance comparison of diierent constructive algorithms is …
Paradoxical Effects of External Modulation of Inhibitory Interneurons

The neocortex, hippocampus, and several other brain regions contain populations of excitatory principal cells with recurrent connections and strong interactions with local inhibitory interneurons. To improve our understanding of the interactions among these cell types, we modeled the dynamic behavior of this type of network, including external inputs. A surprising finding was that increasing the direct external inhibitory input to the inhibitory interneurons, without directly affecting any other part of the network, can, in some circumstances, cause the interneurons to increase their firing rates. The main prerequisite for this paradoxical response to external input is that the recurrent connections among the excitatory cells are strong enough to make the excitatory network unstable when feedback inhibition is removed. Because this requirement is met in the neocortex and several regions of the hippocampus, these observations have important implications for understanding the responses of interneurons to a variety of pharmacological and electrical manipulations. The analysis can be extended to a scenario with periodically varying external input, where it predicts a systematic relationship between the phase shift and depth of modulation for each interneuron. This prediction was tested by recording from interneurons in the CA1 region of the rat hippocampusin vivo, and the results broadly confirmed the model. These findings have further implications for the function of inhibitory and neuromodulatory circuits, which can be tested experimentally.
oint invariant face recognition using endent component analysis and at tractor networks

We have explored two approaches to recognizing faces across changes in pose. First, we developed a representation of face images based on independent component analysis (ICA) and compared it to a principal component analysis (PCA) representation for face recognition. The ICA basis vectors for this data set were more spatially local than the PCA basis vectors and the ICA representation had greater invariance to changes in pose. Second, we present a model for the development of viewpoint invariant responses to faces from visual experience in a biological system. The temporal continuity of natural visual experience was incorporated into an attractor network model by Hebbian learning following a lowpass temporal filter on unit activities. When combined with the temporal filter, a basic Hebbian update rule became a generalization of Griniasty et al. (1993), which associates temporally proximal input patterns into basins of attraction. The system acquired representations of faces that were largely independent of pose. 1 Independent component representations of faces Important advances in face recognition have employed forms of principal component analysis, which considers only second-order moments of the input (Cottrell & Metcalfe, 1991; Turk & Pentland 1991). Independent component analysis (ICA) is a generalization of principal component analysis (PCA), which decorrelates the higher-order moments of the input (Comon, 1994). In a task such as face recognition, much of the important information is contained in the high-order statistics of the images. A representational basis in which the high-order statistics are decorrelated may be more powerful for face recognition than one in which only the second order statistics are decorrelated, as in PCA representations. We compared an ICAbased representation to a PCA-based representation for recognizing faces across changes in pose.  Examples from image set (Beymer, 1994). The image set contained 200 images of faces, consisting of 40 subjects at each of five poses . The images were converted to vectors and comprised the rows of a 200 x 3600 data matrix, X. We consider the face images in X to be a linear mixture of an unknown set of statistically independent source images S, where A is an unknown mixing matrix . The sources are recovered by a matrix of learned filters, W, which produce statistically independent outputs, U .
Correlation Coding in Stochastic Neural Networks

Stimulus-dependent changes have been observed in the correlations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. A simple neural model can reproduce most of the characteristics of these experimental observations based on model neurons having leaky integration and fire-and-reset spikes and with Poisson-distributed, balanced input. The source of the synchrony in the model was common sensory input. The outputs of neurons in the model appear noisy (almost Poisson owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons.
Learning Nonlinear Overcomplete Representations for Efficient Coding

We derive a learning algorithm for inferring an overcomplete basis by viewing it as probabilistic model of the observed data. Overcomplete bases allow for better approximation of the underlying statistical density. Using a Laplacian prior on the basis coefficients removes redundancy and leads to representations that are sparse and are a nonlinear function of the data. This can be viewed as a generalization of the technique of independent component analysis and provides a method for blind source separation of fewer mixtures than sources. We demonstrate the utility of overcomplete representations on natural speech and show that compared to the traditional Fourier basis the inferred representations potentially have much greater coding efficiency.
Spatial Transformations in the Parietal Cortex Using Basis Functions

Sensorimotor transformations are nonlinear mappings of sensory inputs to motor responses. We explore here the possibility that the responses of single neurons in the parietal cortex serve as basis functions for these transformations. Basis function decomposition is a general method for approximating nonlinear functions that is computationally efficient and well suited for adaptive modification. In particular, the responses of single parietal neurons can be approximated by the product of a Gaussian function of retinal location and a sigmoid function of eye position, called a gain field. A large set of such functions forms a basis set that can be used to perform an arbitrary motor response through a direct projection. We compare this hypothesis with other approaches that are commonly used to model population codes, such as computational maps and vectorial representations. Neither of these alternatives can fully account for the responses of parietal neurons, and they are computationally less efficient for nonlinear transformations. Basis functions also have the advantage of not depending on any coordinate system or reference frame. As a consequence, the position of an object can be represented in multiple reference frames simultaneously, a property consistent with the behavior of hemineglect patients with lesions in the parietal cortex.
A new view of hemineglect based on the response properties of parietal neurones.

Lesion studies of the parietal cortex have led to a wide range of conclusions regarding the coordinate reference frame in which hemineglect is expressed. A model of spatial representation in the parietal cortex has recently been developed in which the position of an object is not encoded in a particular frame of reference, but instead involves neurones computing basis functions of sensory inputs. In this type of representation, a nonlinear sensorimotor transformation of an object is represented in a population of units having the response properties of neurones that are observed in the parietal cortex. A simulated lesion in a basis-function representation was found to replicate three of the most important aspects of hemineglect: (i) the model behaved like parietal patients in line-cancellation and line-bisection experiments; (ii) the deficit affected multiple frames of reference; and (iii) the deficit could be object-centred. These results support the basis-function hypothesis for spatial representations and provide a testable computational theory of hemineglect at the level of single cells.
A conjugate neural representation of visual objects in three dimensions

An arbitrary movement of a rigid object in 3-D space can always be decomposed instantaneously into a translation plus a rotation. How should the neural representation of a static view of a 3-D object be updated according to its instantaneous motion? Assuming that a motion-sensitive neuron detects the rate of change of an arbitrary function of the static view of an object, we predict that the generic response properties of such a neuron can always be specified by a preferred translation direction and a preferred rotation axis in 3-D space, with cosine directional or axial tuning and linear firing-rate modulation by speed or angular speed. This theoretical framework includes known properties of some motion-sensitive cells as special cases. From the activity of a population of these neurons, a feedforward network can readily extract the instantaneous rotation axis, the angular speed, and the translation velocity, and thereby completely determine the motion of the object. We propose that neurons tuned to 3-D motion (same view, different possible rotation axes) and neurons tuned to static views (same rotation axis, different possible views) can form a conjugate representation of a 3-D object. In this way, the internal neural representation can contain both static and dynamic information and effectively mimic how the static view of an object changes during arbitrary movement.
The neural basis of cognitive development: A constructivist manifesto

How do minds emerge from developing brains? According to “neural constructivism,” the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: “constructive learning” minimizes the need for prespecification in accordance with recent neurobiological evidence that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning.
Chapter 7 Synchronized Oscillations in Thalamic Networks : Insights from Modeling Studies

The early work of Morison and Bassett 1945 strongly suggested that the thalamus generates coherent r h ythmicity in the spindle frequency range 7-14 Hz. Since then the cellular correlates of this rhythmicity h a ve been investigated in detail by in vivo and in vitro techniques reviewed in Steriade et al., 1993a, and Steriade et al. in this volume. The interaction between thalamocortical TC and thalamic reticular RE cells is involved in the genesis of this rhythmic activity, but the exact mechanisms are still uncertain and contrasting data remain to be resolved. Andersen and Rutjord 1964 proposed a model of thalamic rhythmicity o ver thirty y ears ago that depended on rebound bursts in thalamic neurons following inhibition. Although the anatomical details of their model need to be revised, and much more in now known about the intrinsic currents of thalamic neurons as well as their synaptic receptors, the primary mechanism for thala-mic rhythmicity in their model is now believed to be substantially correct, based on a wide range of experimental and theoretical studies. Computational models have been used to study the dynam-ical behavior of single thalamic cells, which are suuciently complex that the consequences of their intrinsic ionic mechanisms are often diicult to predict. Based on precise voltage-clamp data for these ionic currents, well-deened computational models have been used to explore the spatial and temporal organization of coherent r h ythmicity in networks of neurons in thalamic nuclei. In this chapter, we review recent computational models for thalamic oscillations that were developed independently by several investigators. These thalamic models are more mature than those developed for other parts the mammalian central nervous system, and are as mature as the best models of invertebrate central pattern generators Selverston, 1985. These advances should lead