nils-herrmann/clean_scirepeval huggingface dataset:
Representing Documents Using an Explicit Model of Their Similarities

A method is proposed for creating vector space representations of documents based on modeling target inter-document similarity values. The target similarity values are assumed to capture semantic relationships, or associations, between the documents. The vector representations are chosen so that the inner product similarities between document vector pairs closely match their target inter-document similarities. The method is closely related to the Latent Semantic Indexing approach; in fact, they are equivalent when the target similarities are derived directly from document similarities based on term co-occurrence. However, our method allows for external sources of inter-document semantic constraints to be used in the indexing, though at greater computational expense. The method is applied to three standard text databases from the information retrieval literature. On the CISI database of information science abstracts, performance (measured by precision averaged over a range of recall levels) improves by 28% compared to a weighted term-vector approach, and improves 10% compared to Latent Semantic Indexing. Similar improvement is obtained on the Cranneld database, but no improve-1 Bartell, p. 2 ment is obtained for the artiicial MED database of medical abstracts. The generally favorable performance suggests interesting potential for methods which explicitly modify the retrieval system to meet inter-document semantic constraints.
Programming the User-friendly Dog

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Learning in recurrent finite difference networks

A recurrent learning algorithm based on a finite difference discretization of continuous equations for neural networks is derived. This algorithm has the simplicity of discrete algorithms while retaining some essential characteristics of the continuous equations. In discrete networks learning smooth oscillations is difficult if the period of oscillation is too large. The network either grossly distorts the waveforms or is unable to learn at all. We show how the finite difference formulation can explain and overcome this problem. Formulas for learning time constants and time delays in this framework are also presented.
Neurons Tune to the Earliest Spikes Through STDP

Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.
Rapid categorization of achromatic natural scenes: how robust at very low contrasts?

The human visual system is remarkably good at categorizing objects even in challenging visual conditions. Here we specifically assessed the robustness of the visual system in the face of large contrast variations in a high‐level categorization task using natural images. Human subjects performed a go/no‐go animal/nonanimal categorization task with briefly flashed grey level images. Performance was analysed for a large range of contrast conditions randomly presented to the subjects and varying from normal to 3% of initial contrast. Accuracy was very robust and subjects were performing well above chance level (≈ 70% correct) with only 10–12% of initial contrast. Accuracy decreased with contrast reduction but reached chance level only in the most extreme condition (3% of initial contrast). Conversely, the maximal increase in mean reaction time was ≈ 60 ms (at 8% of initial contrast); it then remained stable with further contrast reductions. Associated ERPs recorded on correct target and distractor trials showed a clear differential effect whose amplitude and peak latency were correlated respectively with task accuracy and mean reaction times. These data show the strong robustness of the visual system in object categorization at very low contrast. They suggest that magnocellular information could play a role in ventral stream visual functions such as object recognition. Performance may rely on early object representations which lack the details provided subsequently by the parvocellular system but contain enough information to reach decision in the categorization task.
Exploration of rank order coding with spiking neural networks for speech recognition

Speech recognition is very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural network processors. We illustrate the potential of such non-linear processing of speech by means of a preliminary test with recognition of French spoken digits from a small speech database
RIVAGe Feedback during Visual Integration : towards a Generic Architecture

We consider the comparative study of visual process integration within either a biological system (i.e. the parieto-ventral and parieto-dorsal pathways of the cortical visual system in the primate) or an artificial system. Both systems deliver an estimation of [where], that is to say the motion and structure of the observed scene and of [what], i.e. the perceptual grouping and labeling of objects in the scene. The long term goal is to elaborate a common theory about precise questions in both neurosciences and algorithms and their architecture in artificial vision, including computer vision applications. Within this framework, the function and behavior of adaptive feedback mechanisms is a key point and on the leading edge of biological studies. The core of this idea is that visual processing is build around: 1) a first computational step allowing to pre-process the input information, provide initial estimates, generate hypotheses about which models to use, ? 2) a refinement step using iterative mechanisms of optimization of the visual perception. Such mechanisms occur, sometimes implicitly, in artificial vision processes. They are mainly related to such problems as the combination of visual attributes, computed from different sources and then fused for ``what'' and ``where'' perceptual tasks; the use of a-priori information, obtained from higher-level visual modules. These modules define models estimated from the data. These models are either given a-priori (e.g. rigidity, shape regularity, ..) or chosen thanks to object labeling obtained during the first computational step. The present work is a theoretical study in three steps: (a) a systematic analysis of existing results in neuro-science, (b) an interpretation of these results from the viewpoint of the variational approach widely used in computer vision (c) a specification of a simulation tool of parts of the visual cortex.
The orbitofrontal cortex: Neuronal activity in the behaving monkey

SummarySingle unit recording of neurons in the orbitofrontal cortex of the alert rhesus monkey was used to investigate responses to sensory stimulation. 32.4% of the neurons had visual responses that had typical latencies of 100–200 ms, and 9.4% responded to gustatory inputs. Most neurons were selective, in that they responded consistently to some stimuli such as foods or aversive objects, but not to others. In a number of cases the neurons responded selectively to particular foods or aversive stimuli. However, this high selectivity could not be explained by simple sensory features of the stimulus, since the responses of some neurons could be readily reversed if the meaning of the stimulus (i.e. whether it was food or aversive) was changed, even though its physical appearance remained identical. Further, some bimodal neurons received convergent visual and gustatory inputs, with matching selectivity for the same stimulus in both modalities, again suggesting that an explanation in terms of simple sensory features is inadequate.Neurons were also studied during the performance of tasks known to be disrupted by orbitofrontal lesions, including a go/no go visual discrimination task and its reversal. 8.6% of neurons had differential responses to the two discriminative stimuli in the task, one of which indicated that reward was available and the other saline. Reversing the meaning of the two stimuli showed that whereas some differential units were closely linked to the sensory features of the stimuli, and some to their behavioural significance, others were conditional, in that they would only respond if a particular stimulus was present, and if it was the one being currently rewarded. Other neurons had activity related to the outcome of the animal's response, with some indicating that reinforcement had been received and others, that an error had been made and that a reversal was required.Thus, neurons in the orbitofrontal cortex possess highly coded information about which stimuli are present, as well as information about the consequences of the animal's own responses. It is suggested that together they may constitute a neuronal mechanism for determining whether particular visual stimuli continue to be associated with reinforcement, as well as providing for the modification of the animal's behavioural responses to such stimuli when those responses are no longer appropriate.
Coding static natural images using spiking event times: do neurons Cooperate?

To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of visual processing for static images. We will first present the retinal model which was introduced by Van Rullen and Thorpe and which represents the multiscale contrast values of the image using an orthonormal wavelet transform. These analog values activate a set of spiking neurons which each fire once to produce an asynchronous wave of spikes. According to this model, the image may be progressively reconstructed from this spike wave thanks to regularities in the statistics of the coefficients determined with natural images. Here, we study mathematically how the quality of information transmission carried by this temporal representation varies over time. In particular, we study how these regularities can be used to optimize information transmission by using a form of temporal cooperation of neurons to code analog values. The original model used wavelet transforms that are close to orthogonal. However, the selectivity of realistic neurons overlap, and we propose an extension of the previous model by adding a spatial cooperation between filters. This model extends the previous scheme for arbitrary-and possibly nonorthogonal-representations of features in the images. In particular, we compared the performance of increasingly over-complete representations in the retina. Results show that this algorithm provides an efficient spike coding strategy for low-level visual processing which may adapt to the complexity of the visual input.
SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons

Many biological neural network models face the problem of scalability because of the limited computational power of today's computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modelled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (<0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.
Ultra-Rapid Scene Categorization with a Wave of Spikes

Recent experimental work has shown that the primate visual system can analyze complex natural scenes in only 100-150 ms. Such data, when combined with anatomical and physiological knowledge, seriously constrains current models of visual processing. In particular, it suggests that a lot of processing can be achieved using a single feed-forward pass through the visual system, and that each processing layer probably has no more than around 10 ms before the next stage has to respond. In this time, few neurons will have generated more than one spike, ruling out most conventional rate coding models. We have been exploring the possibility of using the fact that strongly activated neurons tend to fire early and that information can be encoded in the order in which a population of cells fire. These ideas have been tested using SpikeNet, a computer program that simulates the activity of very large networks of asynchronously firing neurons. The results have been extremely promising, and we have been able to develop artificial visual systems capable of processing complex natural scenes in real time using standard computer hardware (see http://www.spikenet-technology.com).
Parallel processing in high-level categorization of natural images

Models of visual processing often include an initial parallel stage that is restricted to relatively low-level features, whereas activation of higher-level object descriptions is generally assumed to require attention. Here we report that even high-level object representations can be accessed in parallel: in a rapid animal versus non-animal categorization task, both behavioral and electrophysiological data show that human subjects were as fast at responding to two simultaneously presented natural images as they were to a single one. The implication is that even complex natural images can be processed in parallel without the need for sequential focal attention.
ULTRA-RAPID VISUAL CATEGORISATION USING ONE SPIKE PER NEURON

1.  In 1989, I argued that the response latency of face-selective visual responses in high order visual areas such as the primate inferotemporal cortex poses severe problems for almost all models of visual processing that rely on iterative processing (Thorpe & Imbert, 1989). Such neurones start firing 80-100 ms after stimulus onset, which leaves only about 10 ms for processing at each stage of the visual pathway. The original argument could be criticised on the grounds that face processing could be a special case and that in such neurophysiological studies, the stimuli are often highly predictable. However, more recent experiments have shown that very fast processing also occurs in much more challenging visual tasks where subjects must decide whether a briefly flashed natural image contains a target category such as an animal or a means of transport (The most serious implication of such experiments is that very sophisticated visual processing must be possible under conditions where individual neurones only have time to emit one spike. Clearly, this rules out most coding schemes that require the firing rates of individual neurones to be determined with precision. However, it leaves open a number of options in which the precise timing of spikes across a population of cells is used.
A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes

The processing required to decide whether a briefly flashed natural scene contains an animal can be achieved in 150 msec (Thorpe, Fize, & Marlot, 1996). Here we report that extensive training with a subset of photographs over a 3-week period failed to increase the speed of the processing underlying such Rapid Visual Categorizations: Completely novel scenes could be categorized just as fast as highly familiar ones. Such data imply that the visual system processes new stimuli at a speed and with a number of stages that cannot be compressed. This rapid processing mode was seen with a wide range of visual complex images, challenging the idea that short reaction times can only be seen with simple visual stimuli and implying that highly automatic feed-forward mechanisms underlie a far greater proportion of the sophisticated image analysis needed for everyday vision than is generally assumed.
Detection of animals in natural images using far peripheral vision

It is generally believed that the acuity of the peripheral visual field is too poor to allow accurate object recognition and, that to be identified, most objects need to be brought into foveal vision by using saccadic eye movements. However, most measures of form vision in the periphery have been done at eccentricities below 10° and have used relatively artificial stimuli such as letters, digits and compound Gabor patterns. Little is known about how such data would apply in the case of more naturalistic stimuli. Here humans were required to categorize briefly flashed (28 ms) unmasked photographs of natural scenes (39° high, and 26° across) on the basis of whether or not they contained an animal. The photographs appeared randomly in nine locations across virtually the entire extent of the horizontal visual field. Accuracy was 93.3% for central vision and decreased almost linearly with increasing eccentricity (89.8% at 13°, 76.1% at 44.5° and 71.2% at 57.5°). Even at the most extreme eccentricity, where the images were centred at 70.5°, subjects scored 60.5% correct. No evidence was found for hemispheric specialization. This level of performance was achieved despite the fact that the position of the image was unpredictable, ruling out the use of precued attention to target locations. The results demonstrate that even high‐level visual tasks involving object vision can be performed using the relatively coarse information provided by the peripheral retina.
Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex

It is often supposed that the messages sent to the visual cortex by the retinal ganglion cells are encoded by the mean firing rates observed on spike trains generated with a Poisson process. Using an information transmission approach, we evaluate the performances of two such codes, one based on the spike count and the other on the mean interspike interval, and compare the results with a rank order code, where the first ganglion cells to emit a spike are given a maximal weight. Our results show that the rate codes are far from optimal for fast information transmission and that the temporal structure of the spike train can be efficiently used to maximize the information transfer rate under conditions where each cell needs to fire only one spike.
Is it a Bird? Is it a Plane? Ultra-Rapid Visual Categorisation of Natural and Artifactual Objects

Visual processing is known to be very fast in ultra-rapid categorisation tasks where the subject has to decide whether a briefly flashed image belongs to a target category or not. Human subjects can respond in under 400 ms, and event-related-potential studies have shown that the underlying processing can be done in less than 150 ms. Monkeys trained to perform the same task have proved even faster. However, most of these experiments have only been done with biologically relevant target categories such as animals or food. Here we performed the same study on human subjects, alternating between a task in which the target category was ‘animal’, and a task in which the target category was ‘means of transport’. These natural images of clearly artificial objects contained targets as varied as cars, trucks, trains, boats, aircraft, and hot-air balloons. However, the subjects performed almost identically in both tasks, with reaction times not significantly longer in the ‘means of transport’ task. These reaction times were much shorter than in any previous study on natural-image processing. We conclude that, at least for these two superordinate categories, the speed of ultra-rapid visual categorisation of natural scenes does not depend on the target category, and that this processing could rely primarily on feed-forward, automatic mechanisms.
The Time Course of Visual Processing: From Early Perception to Decision-Making

Experiments investigating the mechanisms involved in visual processing often fail to separate low-level encoding mechanisms from higher-level behaviorally relevant ones. Using an alternating dual-task event-related potential (ERP) experimental paradigm (animals or vehicles categorization) where targets of one task are intermixed among distractors of the other, we show that visual categorization of a natural scene involves different mechanisms with different time courses: a perceptual, task-independent mechanism, followed by a task-related, category-independent process. Although average ERP responses reflect the visual category of the stimulus shortly after visual processing has begun (e.g. 75-80 msec), this difference is not correlated with the subject's behavior until 150 msec poststimulus.