nils-herrmann/clean_scirepeval huggingface dataset:
A Dynamical Moedl of Context Dependencies for the Vestibulo-Ocular Reflex

The vestibulo-ocular reflex (VOR) stabilizes images on the retina during rapid head motions. The gain of the VOR (the ratio of eye to head rotation velocity) is typically around -1 when the eyes are focused on a distant target. However, to stabilize images accurately, the VOR gain must vary with context (eye position, eye vergence and head translation). We first describe a kinematic model of the VOR which relies solely on sensory information available from the semicircular canals (head rotation), the otoliths (head translation), and neural correlates of eye position and vergence angle. We then propose a dynamical model and compare it to the eye velocity responses measured in monkeys. The dynamical model reproduces the observed amplitude and time course of the modulation of the VOR and suggests one way to combine the required neural signals within the cerebellum and the brain stem. It also makes predictions for the responses of neurons to multiple inputs (head rotation and translation, eye position, etc.) in the oculomotor system.
An Information-Maximization Approach to Blind Separation and Blind Deconvolution

We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalization of principal components analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to 10 speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information maximization provides a unifying framework for problems in "blind" signal processing.
'Balancing' of Conductances May Explain Irregular Cortical Spiking

Five related factors are identified which enable single compartment Hodgkin-Huxley model neurons to convert random synaptic input into irregular spike trains similar to those seen in in vivo cortical recordings. We suggest that cortical neurons may operate in a narrow parameter regime where synaptic and intrinsic conductances are balanced to reflect, through spike timing, detailed correlations in the inputs. 'Please send comments to tony@salk.edu. The reference for this paper is: Technical Report no. INC-9502, February 1995, Institute for Neural Computation, UCSD, San Diego, CA 92093-0523.
Adaptive separation of mixed broadband sound sources with delays by a beamforming Herault-Jutten network

The Herault-Jutten network has been used to separate independent sound sources that have been linearly mixed. The problem of separating a mixture of several independent signals in free-field conditions or a signal and echoes in confined spaces is compounded by propagation time delays between the source(s) and the microphones because the conventional Herault-Jutten network cannot tolerate time delays. In this paper, we combine a symmetrically balanced beamforming array with the conventional Herault-Jutten network. The resulting system can adaptively separate signals that include delays introduced by the propagation medium. The proposed algorithm has been simulated in digital communication multipath channels where intersymbol interference exists. The simulation results show two clear advantages of the proposed method over the conventional adaptive equalization: (1) there is no penalty for very long impulse responses caused by long delays, and (2) no training signals are needed for equalization. The design of a multibeamformer to handle the source separation of multiple broad-band signals is also presented.
Compartmental Models of Single Cells and Small Networks in the Primary Visual Cortex

Bush, P. C. & Sejnowski, T. J. Large-scale compartment model of a cerebellar Purkinje cell. Neuroscience Abstracts. 16, 1298, 1990. Bush, P. C., Li, S. & Sejnowski, T. J. Quantal analysis of superimposed EPSPs from multiple synapses. Neuroscience Abstracts. 17, 385, 1991. Bonds, A. B., Snider, R. K., Kabara. J., Bush, P. and Sejnowski, T. J. On the origins of oscillation in cells of the cat striate cortex.. ARVO. 34/4 1032. 1993. Bush, P. C., Gray, C. & Sejnowski, T. J. Realistic simulations of synchronization in networks of layer V neurons in cat primary visual cortex. Neuroscience Abstracts. 1993
Time for a new neural code?

by vaporization, cannot be used as building blocks of the planets. So these papers provide no support for popular planetary building modelss7. Incidentally, they also dispose of the old idea8 that the depletion of sodium and potassium in lunar basalts was due to evaporation as the basaltic lavas were extruded on to the hard vacuum and low temperatures of the Moon's surface. The low abundance of the alkali elements in the Moon (less than 20% of the Earth's already depleted budget) is now shown to be an inherent property of the material that formed the Moon. Evaporative loss of potassium during the formation of chondrules and refractory inclusions is also ruled out, so that the depletion of volatile elements in chondrules can no longer be ascribed to loss during the flash-melting episode that melted the precursor silicate dust balls. Chondrules are major components of the chondrites, dated at 4,563 million years old9; the refractory inclusions give slightly earlier ages of 4,566 million years9. So it seems that the depletion of volatile elements occurred before the formation of the chondrules, or the refractory inclusions, and thus before 4,566 million years ago. Because these are the earliest available samples of the Solar System that we can measure, we must retreat to a remoter epoch in our search for the mechanism for depletion. Although Humayun and Clayton2 incline to condensation from an initially hot nebula as the cause, considerable problems remain. The observed oxygen isotopic heterogeneity in meteorites seems unlikely to have survived in such a hightemperature environment, while condensation might be expected to affect the isotopicratios. It is tempting to ascribe the depletion to early violent solar activity that swept water and other volatiles out to a 'snow line' at five astronomical units from the Sun, where condensation enabled the rapid, early growth of Jupiter. All of these problems provide rich pickings for further investigation. About 20 years ago, oxygen isotope data from Clayton's laboratory'0 clarified the relationships among the meteorite
A Perceptron Reveals the Face of Sex

Recognizing the sex of conspecifics is important. Humans rely primarily on visual pattern recognition for this task. A wide variety of linear and nonlinear models have been developed to understand this task of sex recognition from human faces.' These models have used both pixelbased and feature-based representations of the face for input. Fleming and Cottrell (1990) and Golomb et a!. (1991) utilized first an autoencoder compression network on a pixel-based representation, and then a classification network. Brunelli and Poggio (1993) used a type of radial basis function network with geometrical face measurements as input. O'Toole and colleagues (1991, 1993) represented faces as principal components. When the hidden units of an autoencoder have a linear output function, then the N hidden units in the network span the first N principal components of the input (Baldi and Hornik 1989). Bruce et al. (1993) constructed a discriminant function for sex with 2-D and 3-D facial measures. In this note we compare the performance of a simple perceptron and a standard multilayer perceptron (MLP) on the sex classification task. We used a range of spatial resolutions of the face to determine how the reliability of sex discrimination is related to resolution. A normalized pixel-based representation was used for the faces because it explicitly retained texture and shape information while also maintaining geometric relationships. We found that the linear perceptron model can classify sex from facial images with 81% accuracy, compared to 92% accuracy with compression coding on the same data set (Golomb et al. 1991). The advantage of using a simple linear perceptron with normalized pixelbased inputs is that it allows us to see explicitly those regions of the face
Reliability of spike timing in neocortical neurons.

It is not known whether the variability of neural activity in the cerebral cortex carries information or reflects noisy underlying mechanisms. In an examination of the reliability of spike generation using recordings from neurons in rat neocortical slices, the precision of spike timing was found to depend on stimulus transients. Constant stimuli led to imprecise spike trains, whereas stimuli with fluctuations resembling synaptic activity produced spike trains with timing reproducible to less than 1 millisecond. These data suggest a low intrinsic noise level in spike generation, which could allow cortical neurons to accurately transform synaptic input into spike sequences, supporting a possible role for spike timing in the processing of cortical information by the neocortex.
Synaptic Currents, Neuromodulation and Kinetic Models Kinetic Models of Synaptic Currents 1 1 Introduction

Synaptic interactions are essential to neural network models of all levels of complexity. Synaptic interactions in \realistic" network models pose a particular challenge, since the aim is not to only to capture the essence of synaptic mechanisms, but also to do so in a computationally eecient manner to facilitate simulations of large networks. In this paper, we review several types of models which address these goals. Synaptic currents are mediated by ion channels activated by neurotransmitter released from presynaptic terminals. Kinetic models are a powerful formalism for the description of channel behavior, and are therefore well-suited to the description of synaptic interactions, both traditional and neuromodulatory. Although full representation of the molecular details of the synapse generally requires highly complex kinetic models, we focus on simpler kinetic models which are very eecient to compute. We show how these models capture the time courses of several types of synaptic responses as well as the important phenomena of summation, saturation and desensitization. For neural models that do not include action potentials, synaptic currents are typically modeled as a direct function of the some presynaptic activity measure. In the simplest case, synaptic interactions are described by a sigmoid function, and presynaptic activity is interpreted as the average ring rate of the aaerent neuron. Alternatively, the postsynaptic currents can be described by a rst-order diierential equation in which one term depends on the presynaptic membrane potential through a sigmoid function (Wang and Rinzel, 1992). Another possibility is to interpret the activity level as the fraction of neurons active per unit of time, thus representing the interaction between neural populations rather than single neurons (Wilson and Cowan, 1973). For spiking neurons, a popular model of postsynaptic currents (PSC's) is the alpha function r(t ? t 0) = (t ? t 0) 1 expp?(t ? t 0)== 1 ] (1) (Rall, 1967), where r(t) resembles the time course of experimentally-recorded postsy-naptic potentials (PSP's) with a time constant 1. The alpha function and its double-exponential generalization can be used to approximate most synaptic currents with a small number of parameters and, if implemented properly, at low computation and stor-2 age requirements (Srinivasan and Chiel, 1993). Other types of template function were also proposed for spiking neurons (Tsodyks et al., 1990; Traub and Miles, 1991). The disadvantages of the alpha-function, or related approaches, include the lack of correspondence to a plausible biophysical mechanism and the absence of a …
Time series prediction of plasma hormone concentration. Evidence for differences in predictability of parathyroid hormone secretion between osteoporotic patients and normal controls.

Recent evidence links osteoporosis, a disease of bone remodeling, to changes in the dynamics of parathyroid hormone secretion. We use nonlinear and linear time series prediction to characterize the secretory dynamics of parathyroid hormone in both healthy human subjects and patients with osteoporosis. Osteoporotic patients appear to lack the periods of high predictability found in normal humans. Our results may provide an explanation for why an intermittent administration of parathyroid hormone is effective in restoring bone mass in osteoporotic patients.
Sex Recognition from Faces Using Neural Networks

Recognizing the sex of conspecifics is important: Expending one’s attentions Q or one’s seed Q on the wrong parity of partner could put one at a competitive disadvantage. While some animals use pheromones to recognize sex, in humans this task is primarily visual: “Many socially living animals ... recognize each other as members of the same species, as individuals, and as social partners by means of visual signals and communicate their mood and intentions by the same sensory modality. In many primate species the individual structure of the face is the most important visual characteristic of each group member” (Grusser, Selke, & Zynda 1985). A core issue is how sex is recognized from face; yet until recently this received little attention.
Empirical Entropy Manipulation for Real-World Problems

No finite sample is sufficient to determine the density, and therefore the entropy, of a signal directly. Some assumption about either the functional form of the density or about its smoothness is necessary. Both amount to a prior over the space of possible density functions. By far the most common approach is to assume that the density has a parametric form. 
 
By contrast we derive a differential learning rule called EMMA that optimizes entropy by way of kernel density estimation. Entropy and its derivative can then be calculated by sampling from this density estimate. The resulting parameter update rule is surprisingly simple and efficient. 
 
We will show how EMMA can be used to detect and correct corruption in magnetic resonance images (MRI). This application is beyond the scope of existing parametric entropy models.
G-Protein Activation Kinetics And Spill-Over Of Gaba May Account For Differences Between Inhibitory

We have developed a model of GABAergic synaptic transmission mediated by GABA A and GABA B receptors, including cooperativity in the G-protein cascade mediating the activation of K + channels by GABA B receptors. If the binding of several G-proteins is needed to activate the K + channels, then only a prolonged activation of GABA B receptors evoked detectable currents. This could occur if strong stimuli evoked release in adjacent terminals, and the spill-over resulted in the prolonged activation of the receptors, leading to inhibitory responses similar to those observed in hippocampal slices. The same model also reproduced thalamic GABA B responses to high-frequency bursts of stimuli. In this case, the prolonged activation of the receptors was due to high-frequency release conditions. This model provides new insights into the function of GABA B receptors in normal and epileptic discharges.
Blind separation and blind deconvolution: an information-theoretic approach

Blind separation and blind deconvolution are related problems in unsupervised learning. In this contribution, static non-linearities are used in combination with an information-theoretic objective function, making the approach more rigorous than previous ones. We derive a new algorithm and with it perform nearly perfect separation of up to 10 digitally mixed human speakers, better performance than any previous algorithms for blind separation. When used for deconvolution, the technique automatically cancels echoes and reverberations and reverses the effects of low-pass filtering.
RAPID STATE SWITCHING IN BALANCED CORTICAL NETWORK MODELS

We have explored a network model of cortical microcircuits based on integrate-and-fire neurons in a regime where the reset following a spike is small, recurrent excitation is balanced by feedback inhibition, and the activity is highly irregular. This regime cannot be described by a mean-field theory based on average activity levels because essential features of the model depend on fluctuations from the average. We propose a new way of scaling the strength of synaptic interaction with the size of the network: rather than scale the amplitude of the synapse we scale the neurotransmitter release probabilities with the number of inputs to keep the average input constant. This is consistent with the low transmitter release probability observed in a majority of hippocampal synapses. Another prominent feature of this regime is the ability of the network to switch rapidly between different states, as demonstrated in a model based on an orientation columns in the mammalian visual cortex. Both network and intrinsic ...
A Model of Spatial Representations in Parietal Cortex Explains Hemineglect

We have recently developed a theory of spatial representations in which the position of an object is not encoded in a particular frame of reference but, instead, involves neurons computing basis functions of their sensory inputs. This type of representation is able to perform nonlinear sensorimotor transformations and is consistent with the response properties of parietal neurons. We now ask whether the same theory could account for the behavior of human patients with parietal lesions. These lesions induce a deficit known as hemineglect that is characterized by a lack of reaction to stimuli located in the hemispace contralateral to the lesion. A simulated lesion in a basis function representation was found to replicate three of the most important aspects of hemineglect: i) The models failed to cross the leftmost lines in line cancellation experiments, ii) the deficit affected multiple frames of reference and, iii) it could be object centered. These results strongly support the basis function hypothesis for spatial representations and provide a computational theory of hemineglect at the single cell level.
G protein activation kinetics and spillover of gamma-aminobutyric acid may account for differences between inhibitory responses in the hippocampus and thalamus.

We have developed a model of gamma-aminobutyric acid (GABA)ergic synaptic transmission mediated by GABAA and GABAB receptors, including cooperativity in the guanine nucleotide binding protein (G protein) cascade mediating the activation of K+ channels by GABAB receptors. If the binding of several G proteins is needed to activate the K+ channels, then only a prolonged activation of GABAB receptors evoked detectable currents. This could occur if strong stimuli evoked release in adjacent terminals and the spillover resulted in prolonged activation of the receptors, leading to inhibitory responses similar to those observed in hippocampal slices. The same model also reproduced thalamic GABAB responses to high-frequency bursts of stimuli. In this case, prolonged activation of the receptors was due to high-frequency release conditions. This model provides insights into the function of GABAB receptors in normal and epileptic discharges.
Using Feedforward Neural Networks to Monitor Alertness from Changes in EEG Correlation and Coherence

We report here that changes in the normalized electroencephalographic (EEG) cross-spectrum can be used in conjunction with feedforward neural networks to monitor changes in alertness of operators continuously and in near-real time. Previously, we have shown that EEG spectral amplitudes covary with changes in alertness as indexed by changes in behavioral error rate on an auditory detection task [6,4]. Here, we report for the first time that increases in the frequency of detection errors in this task are also accompanied by patterns of increased and decreased spectral coherence in several frequency bands and EEG channel pairs. Relationships between EEG coherence and performance vary between subjects, but within subjects, their topographic and spectral profiles appear stable from session to session. Changes in alertness also covary with changes in correlations among EEG waveforms recorded at different scalp sites, and neural networks can also estimate alertness from correlation changes in spontaneous and unobtrusively-recorded EEG signals.
Classifying Facial Action

The Facial Action Coding System, (FACS), devised by Ekman and Friesen (1978), provides an objective means for measuring the facial muscle contractions involved in a facial expression. In this paper, we approach automated facial expression analysis by detecting and classifying facial actions. We generated a database of over 1100 image sequences of 24 subjects performing over 150 distinct facial actions or action combinations. We compare three different approaches to classifying the facial actions in these images: Holistic spatial analysis based on principal components of graylevel images; explicit measurement of local image features such as wrinkles; and template matching with motion flow fields. On a dataset containing six individual actions and 20 subjects, these methods had 89%, 57%, and 85% performances respectively for generalization to novel subjects. When combined, performance improved to 92%.
A Dynamical Model of Context Dependencies for the Vestibulo-Ocular Reflex

The vestibulo-ocular reflex (VOR) stabilizes images on the retina during rapid head motions. The gain of the VOR (the ratio of eye to head rotation velocity) is typically around -1 when the eyes are focused on a distant target. However, to stabilize images accurately, the VOR gain must vary with context (eye position, eye vergence and head translation). We first describe a kinematic model of the VOR which relies solely on sensory information available from the semicircular canals (head rotation), the otoliths (head translation), and neural correlates of eye position and vergence angle. We then propose a dynamical model and compare it to the eye velocity responses measured in monkeys. The dynamical model reproduces the observed amplitude and time course of the modulation of the VOR and suggests one way to combine the required neural signals within the cerebellum and the brain stem. It also makes predictions for the responses of neurons to multiple inputs (head rotation and translation, eye position, etc.) in the oculomotor system.
Bee foraging in uncertain environments using predictive hebbian learning

RECENT work has identified a neuron with widespread projections to odour processing regions of the honeybee brain whose activity represents the reward value of gustatory stimuli1,2. We have constructed a model of bee foraging in uncertain environments based on this type of neuron and a predictive form of hebbian synaptic plasticity. The model uses visual input from a simulated three-dimensional world and accounts for a wide range of experiments on bee learning during foraging, including risk aversion. The predictive model shows how neuromodulatory influences can be used to bias actions and control synaptic plasticity in a way that goes beyond standard correlational mechanisms. Although several behavioural models of conditioning in bees have been proposed37, this model is based on the neural substrate and was tested in a simulation of bee flight.