nils-herrmann/clean_scirepeval huggingface dataset:
Brain Areas Involved in Rapid Categorization of Natural Images: An Event-Related fMRI Study

Event-related fMRI was used to investigate brain activation during a visual go/no-go categorization task based on colored photographs of natural scenes, similar to a previous ERP study by Thorpe et al. (1996, ). Subjects had to press a key when an animal was present in the display. Stimuli were flashed for 33 ms using an intertrial interval of 5 s and a design that carefully balanced targets and distractors in a pseudo-random sequence. Activation produced by targets and distractors was compared with two different techniques, one based on correlations with the stimulation pattern, the other using simple t score statistics to compare selected scans. The contralateral primary motor cortex and the ipsilateral cerebellum were both more active following target trials than following distractors, thus confirming the sensitivity of the method. Differential activity was also seen in the posterior cingulate cortex, the fusiform, and the parahippocampic gyri. Activity in such structures could underlie the differential evoked-potentials reported previously in the same task. Surprisingly, in these visual structures, the signal was stronger following distractor trials than target ones. This result could be due to more prolonged processing on distractor trials. Alternatively, it could be that target detection induces strong activation of a small proportion of neurons, which, because of competitive inhibitory mechanisms, could result in a decrease in activity for the population as a whole. We suggest that this kind of mechanism could also account for the decreases in signal observed in perceptual priming experiments.
Reverse engineering of the visual system using networks of spiking neurons

Recent research has shown that the speed of image processing achieved by the human visual system is incompatible with conventional neural network approaches that use standard coding schemes based on firing rate. An alternative is to use networks of asynchronously firing spiking neurons and use the order of firing across a population of neurons as a code. In this paper we summarize results that demonstrate a number of advantages of such coding schemes: (1) they allow very efficient transmission of information, (2) they are intrinsically invariant to variations in stimulus intensity and contrast, (3) they can be used in very large scale processing architectures to solve difficult problems including categorization of objects in natural scenes, and (4) they are particularly suited for implementation in low-cost multi-processor hardware.
Rank-based Hebbian learning in a multilayered neural network

Recent work on biologically motivated networks have shown that the visual system can process a natural scene more quickly by encoding the order of neural firing rather than the frequency of firing. This `order of firing' encoding scheme has led to a rank-based approach which converts activation energy into a time-dependent pulse code. This paper focuses towards the contribution of unsupervised learning to the training of integrate and fire neurons within multi-layer networks. First, we propose an unsupervised learning algorithm and we test it on a simple recognition task. Then, we propose a multilayer architecture of integrate and fire neurons to solve a more complex vision task. This architecture is efficiently trained by an algorithm combining supervised and unsupervised rank-based hebbian learning. Further improvements are proposed in the final discussion.
Rank order coding

The idea that neurones transmit information using a rate code is extremely entrenched in the neuroscience community. The vast majority of neurophysiological studies simply describe neural responses in terms of firing rate, and while studies using Peri-Stimulus Time Histograms (PSTHs) are fairly common, only rarely does one get to see the underlying spikes in the form of a raster display. Even rarer are studies that provide information about how spikes are generated across a population of neurones.
Rapid categorization of extrafoveal natural images: impplications for biological models

Despite intensive research over the past 30 years, the performance of artificial visual systems in object recognition is still poor when compared with humans. For humans, the identification of objects in natural scenes appears both effortless and fast. Just how fast was a question we addressed recently in a study that associated behavioral measurements and event related potential (ERP) recordings1. The task used was a go/no-go visual categorization task in which human subjects had to respond when a photograph of a natural scene contained an animal. The photographs had never been seen before and were flashed centrally on a screen for only 20 ms. Humans scored 94% correct, moreover, their ERPs recorded on animal and non-animal trials showed a clear difference on all frontal electrodes that started around 150 ms after stimuli onset. Thus, it appears that the human visual system can process such previously unseen complex natural scenes in less than 150 ms, a level of performance well above that of any currently available artificial system. This is despite the fact that the neurons that constitute the human visual system are relatively slow — firing rates rarely exceed 200 Hz, a value far slower than the transistors of a modern microprocessor, which can change state over a million times faster.
On the speed of natural scene categorisation in human and non-human primates

Deux singes rhesus ont effectue une tâche de categorisation visuelle dans laquelle ils devaient repondre a la presence d'une cible donnee (aliment pour l'un, animal pour l'autre). Chaque stimulus etait une image naturelle provenant d'une collection disponible sur CD ROM et n'etait presente que pendant 80 ms afin d'eviter toute exploration oculaire. Chaque jour, parmi des images deja vues, 5 nouvelles cibles et 5 nouveaux distracteurs etaient proposes. La reponse a la premiere presentation de chaque nouvelle image etait analysee. Le taux de reussite des deux singes sur 200 nouvelles images a ete eleve (90.5% pour la categorisation aliment et 84% pour la categorisation animal). De plus, les temps de reaction des reponses etaient tres brefs (respectivement 344 ms and 245 ms de TR median). La categorisation des memes images a ete effectuee dans des conditions semblables par 10 sujets humains qui se sont reveles plus precis (95% de reussite dans chaque tâche) mais considerablement plus lents (TR median : 446 ms et 422 ms pour les classifications aliment et animal). De plus, les images induisant des erreurs de classification etaient souvent communes aux sujets humains et aux singes. Une derniere serie experimentale a montre que les performances des singes etaient similaires lorsque les images etaient proposees en noir et blanc. Les macaques peuvent effectuer des categorisations rapides et precises d'images qu'ils n'ont jamais vues auparavant, meme en absence de couleur, probablement sur la base de representations similaires a celles de l'homme. Leur rapidite montre que le traitement visuel requis doit etre massivement parallele et principalement feed-forward.
Rapid categorization of natural images by rhesus monkeys

TWO rhesus macaques were tested on a categorization task in which they had to classify previously unseen photographs flashed for only 80 ms. One monkey was trained to respond to the presence of an animal, the second to the presence of food. Although the monkeys were not quite as accurate as humans tested on the same material, they nevertheless performed this very challenging visual task remarkably well. Furthermore, their reaction times were considerably shorter than even the fastest human subject. Such data, combined with the detailed knowledge of the monkey's visual system, provide a severe challenge to current theories of visual processing. They also argue that this form of rapid visual categorization is fundamentally similar in both monkeys and humans.
Implementing Hebbian Learning in a Rank-Based Neural Network

Recent works have shown that biologically motivated net works of spiking neurons can potentially process information very quickly by encoding information in the latency at which different neurons fire, rather than by using frequency of firing as the code. In this paper, the relevant information is the rank vector of latency order of competing neurons. We propose here a Hebbian reinforcement, learning scheme to adjust the weights of a terminal layer of decision neurons in order to process this information. Then this learning rule is shown to be efficient in a simple pattern recognition task. We discuss in conclusion further extensions of that learning strategy for artificial vision.
The Effects of Spatial Frequency on Binocular Fusion: From Elementary to Complex Images

Factors limiting binocular fusion were studied using 2-dimensional difference of Gaussian (2D-DOG) stimuli. The proportion of fused stimuli and observer's response time were determined for stimuli that varied in spatial frequency composition between 0.22 and 4.8 cycles per degree. At small disparities, mean fusion response times were short and relatively stable but increased rapidly once the disparity reached a certain critical value. This 2-phase function implies the existence of 2 separate fusional mechanism: a rapid neurally based fusional process, which operates at small disparities, and a second mechanism involving reflexive vergence movements operating at disparities 2 to 3 times larger. Both mechanisms are highly influenced by spatial frequency, being 4 to 5 times more effective at low spatial frequencies. Additional experiments demonstrated that with compound stimuli, the fusion limit is not determined by the highest spatial frequency components (as had been reported previously) but, rather, can take advantage of the additional fusional range associated with low spatial frequencies. Such cooperation may be obvious only in the case of 2-dimensional stimuli.
How can the visual system process a natural scene in under 150 ms? On the role of asynchronous spike propagation

The human visual system can process previously unseen natural scenes in under 150 ms, even with extrafoveal viewing. Such data impose serious temporal constraints on the way in which information is processed. We argue that one of the keys to understanding this remarkable efficiency lies in the fact that biological vision makes use of asynchronous spike propagation, a feature absent in the vast majority of artificial neural networks. We describe our recent work that has explored the computational advantages of such asynchronous processing.
Can we build a machine that can process natural images as fast as humans

The human visual system can process previously unseen natural scenes in under 150 ms, even with extrafoveal viewing. Such data impose serious temporal constraints on the way in which information is processed. We argue that one of the keys to understanding this remarkable efficiency lies in the fact that biological vision makes use of asynchronous spike propagation, a feature absent in the vast majority of artificial neural networks. We describe our recent work that has explored the computational advantages of such asynchronous processing.
Rapid Visual Processing using Spike Asynchrony

We have investigated the possibility that rapid processing in the visual system could be achieved by using the order of firing in different neurones as a code, rather than more conventional firing rate schemes. Using SPIKENET, a neural net simulator based on integrate-and-fire neurones and in which neurones in the input layer function as analog-to-delay converters, we have modeled the initial stages of visual processing. Initial results are extremely promising. Even with activity in retinal output cells limited to one spike per neuron per image (effectively ruling out any form of rate coding), sophisticated processing based on asynchronous activation was nonetheless possible.
Rapid Categorisation of Natural Images with Extrafoveal Presentations

Last year, we showed that most of the visual processing for categorising centrally presented images could be done in 150 ms. The human subjects were given a go — no go categorisation task in which they had to release a button when they could identify an animal. All the pictures were natural scenes taken from a vast CD-ROM data bank allowing access to several thousands of stimuli so that each image was only seen once. Targets (animals) included fish, birds, mammals, reptiles, insects in their natural environment; distractors included landscapes, trees, flowers, fruits. They were presented for a very short duration (20 ms) in order to avoid eye movement. In the present study we wanted to evaluate how the performance was affected with extrafoveal stimulations. We used the same task, but the visual stimuli could be presented at random, either centrally where subjects had to maintain fixation or laterally (left or right hemifield). Behavioural data and concomitant evoked potentials were recorded. The subject's performance was extremely good with a mean score of 90% correct and a mean reaction time of 500 ms. Comparison of performance between central and lateral stimulations showed that most subjects could process lateral and central stimuli with the same speed (reaction time distributions were not statistically different), and that, although extrafoveal presentations induced a drop in the percentage of correct responses (mean decrease 5%), it was not significant in all subjects. Moreover, when compared with last year's study the increase of the number of spatial locations where the visual stimulus could appear did not induce a significant behavioural cost, in terms of speed or accuracy of the response. We also looked at a possible hemispheric specialisation with no clear results. The results obtained here show that even with shared spatial attention and extrafoveal vision, the human visual system is remarkably efficient in an identification task.
Speed of processing in the human visual system

How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used1, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output2. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.
Rapid neural computation using single spikes and time-dependent EPSP modulation

Evidence for rapid neural computation in visual cortex are now available: Event related potentials in human visual system have revealed a frontal negativity specific to no-go trials involving complex shape categorization that develops roughly after 150 ms stimulus onset. Furthermore, in Vl, in the inferotemporal cortex, and in the superior temporal sulcus, cells can discriminate different complex visual stimuli within 5 to 10 ms. Given the anatomical and physiological constraints imposed by our knowledge of the visual system, such data have major implications for theories of visual computation. The suggestion that firing rate is rarely in excess of one spike in a 5-10 ms period in cortex emphasizes this situation. Cortical maps, which reflect a precise topographic neuronal organization where neighboring sites of maximum relative activity on the cortical plane correspond to nearby points in parameter space, might offer a neural substrate for rapid computation.
Identifying Protein Complexes in High-Throughput Protein Interaction Screens Using an Infinite Latent Feature Model

We propose a Bayesian approach to identify protein complexes and their constituents from high-throughput protein-protein interaction screens. An infinite latent feature model that allows for multi-complex membership by individual proteins is coupled with a graph diffusion kernel that evaluates the likelihood of two proteins belonging to the same complex. Gibbs sampling is then used to infer a catalog of protein complexes from the interaction screen data. An advantage of this model is that it places no prior constraints on the number of complexes and automatically infers the number of significant complexes from the data. Validation results using affinity purification/mass spectrometry experimental data from yeast RNA-processing complexes indicate that our method is capable of partitioning the data in a biologically meaningful way. A supplementary web site containing larger versions of the figures is available at http://public.kgi.edu/wild/PSBO6/index.html.
Learning Multiple Related Tasks using Latent Independent Component Analysis

We propose a probabilistic model based on Independent Component Analysis for learning multiple related tasks. In our model the task parameters are assumed to be generated from independent sources which account for the relatedness of the tasks. We use Laplace distributions to model hidden sources which makes it possible to identify the hidden, independent components instead of just modeling correlations. Furthermore, our model enjoys a sparsity property which makes it both parsimonious and robust. We also propose efficient algorithms for both empirical Bayes method and point estimation. Our experimental results on two multi-label text classification data sets show that the proposed approach is promising.
Preference learning with Gaussian processes

In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relations in the Bayesian framework. The generalized formulation is also applicable to tackle many multiclass problems. The overall approach has the advantages of Bayesian methods for model selection and probabilistic prediction. Experimental results compared against the constraint classification approach on several benchmark datasets verify the usefulness of this algorithm.
Bilinear dynamical systems

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.
Randomized algorithms for fast Bayesian hierarchical clustering

We present two new algorithms for fast Bayesian Hierarchical Clustering on large data sets. Bayesian Hierarchical Clustering (BHC)  is a method for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. BHC has several advantages over traditional distancebased agglomerative clustering algorithms. It defines a probabilistic model of the data and uses Bayesian hypothesis testing to decide which merges are advantageous and to output the recommended depth of the tree. Moreover, the algorithm can be interpreted as a novel fast bottom-up approximate inference method for a Dirichlet process (i.e. countably infinite) mixture model (DPM). While the original BHC algorithm has O(n) computational complexity, the two new randomized algorithms are O(n log n) and O(n).
Nested sampling for Potts models

Nested sampling is a new Monte Carlo method by Skilling  intended for general Bayesian computation. Nested sampling provides a robust alternative to annealing-based methods for computing normalizing constants. It can also generate estimates of other quantities such as posterior expectations. The key technical requirement is an ability to draw samples uniformly from the prior subject to a constraint on the likelihood. We provide a demonstration with the Potts model, an undirected graphical model.
Infinite latent feature models and the Indian buffet process

We define a probability distribution over equivalence classes of binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features. We identify a simple generative process that results in the same distribution over equivalence classes, which we call the Indian buffet process. We illustrate the use of this distribution as a prior in an infinite latent feature model, deriving a Markov chain Monte Carlo algorithm for inference in this model and applying the algorithm to an image dataset.
Compact approximations to Bayesian predictive distributions

We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing the KL divergence between the true predictive density and a suitable compact approximation. We consider various methods for doing this, both sampling based approximations, and deterministic approximations such as expectation propagation. These methods are tested on a mixture of Gaussians model for density estimation and on binary linear classification, with both synthetic data sets for visualization and several real data sets. Our results show significant reductions in prediction time and memory footprint.
Gaussian Processes for Ordinal Regression

We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support vector machines on some benchmark and real-world data sets, including applications of ordinal regression to collaborative filtering and gene expression analysis. Experimental results on these data sets verify the usefulness of our approach.