Salience models: a computational cognitive neuroscience review
The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model - so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely its contribution to our theoretical, neural and computational understanding of visual processing. Further, the model showed how salience could be used to make predictions for both spatial and temporal distributions of fixations. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modeling, many of which tried to augment the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks, however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modeling salience, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
Our sentences about the world are organized to properly convey the constantly changing visual environment. This skill develops early in life. When fully developed, it entails constant, regular, and automatic mappings from elements of a visual scene onto sentence constituents and the grammatical relations between them. The visual system contributes initially to this process by providing perceptual information for conceptual and linguistic analysis but the perceptual information that enters the language production system is not organized indiscriminately. The attentional system filters information for processing based on its noticeability, importance, or relevance. This process allows representing salience parameters in linguistic output. Individual languages’ grammars have specific devices responsible for this representation.
Background Used / introduction of The early eye tracking studies of descriptive Yarbus Provided Evidence That an observer's task Influences patterns of eye Movements, a leading to the a tantalizing prospect That an observer's Intentions Could the BE inferred from Their saccade behavior. This is a dynamic and dynamic cognitive companion using a Dynamic Bayesian Network (DBN). Understanding how it comes to human cognitive goals. This model provides a Bayesian, cognitive approach to pre-frontal areas with the colliculus. This method has been previously shown. This is an analysis of the observer’s task. Secondly, it is a state cognitive state . Finally, we ’ve been able to make a difference . Results This is the only factor that influences observers' saccadic behavior. It has been shown that it has been shown that it has been selected for the paradigms. Conclusions Given the generative nature of this model in real time. We have shown that it has been closely coordinated with those of human observers. Many current models of vision The area of interest is within the visual scene. There are three ways to add top-down knowledge and knowledge . First of all, it is given the information available to the visual system. Matches influential theories This of bias signals by Miller & Cohen (2001), and implements selection of state without simply shifting the decision to an external homunculus. Second, our model is a generative and capable of those found in visual search. Third, our model generates relative saccadic vector information as opposed to absolute spatial coordinates. This match is more closely associated with the colliculus.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. We show our unsupervised learning pipeline may help segmentation models. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.
his volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics.
Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.
The world that we perceive and describe changes constantly. If we believe our descriptions of the world to be accurate and consistent, we must assume that the content and the structure of our individual sentences accurately and consistently reflect the world’s constantly changing nature. If so, a comprehensive production system must model the sentence generation process taking into account this basic assumption: Words, their linear arrangement, and the structures they are inserted in must somehow reflect the corresponding parameters of the observed and described event. This system must include representation of salience as one integral component resulting in interplay that involves constant, regular, and automatic mappings between elements of a visual scene, their varying salience, and the structural arrangement of the sentence constituents and the grammatical relations between them. In this interplay, perceptual input contributes initially to this mapping process by providing information for further conceptual and linguistic encoding. Importantly, this information is not processed in an unconstrained fashion; instead, it is systematically filtered, selected, and relayed based on a regular interface between the aspects of attention and their corresponding counterparts in the conceptual and linguistic structures. Bottom-up and top-down features of this interface include noticeability, importance, or relevance. As a result, linguistic output reflects the event’s conceptual organization including the attentional state of the speaker in a regular way. This mapping between attentional focus and structural choice is a part of a more complex mapping mechanism that we will refer to as Cognition-Language Interface or CLI. Specifically, this Chapter will consider theoretical and empirical knowledge about the complex interplay between the speaker’s attentional state and the structural choices they make during sentence production.
We study the behavior of a minimal model of synaptically sustained persistent activity that consists of two quadratic integrate-and-fire neurons mutually coupled via excitatory synapses. Importantly, each of the neurons is excitable, as opposed to an oscillator; hence when uncoupled it sits at a subthreshold rest state. When the constituent neurons are mutually coupled via sufficiently strong fast excitatory synapses, the system demonstrates bistability between a fixed point (quiescent background state) and a limit cycle (memory state with synaptically driven spiking activity). Previous work showed that this persistent activity can be stopped by an excitatory input that synchronizes the network. Here we analyzed how this persistent state reacts to partial synchronization. We considered three types of progressively more complex excitatory synaptic kernels: delta pulse, square, and exponential. The first two cases were treated analytically, and the latter case numerically. Using phase-plane methods, we characterized the shape of the region, such that all orbits starting within it correspond to infinite spike trains; this constitutes the persistent activity region. In the case of instant coupling, all such active orbits were neutrally stable; in the case of noninstant coupling, the activity region contained a unique stable limit cycle (so the activity region was the basin of attraction for the limit cycle). This limit cycle corresponded to purely antiphase spiking of two neurons. Increasing synchronization shifted the system toward the border of the activity region, eventually terminating spiking activity. We calculated three measures of robustness of the active state: width of the activity region in the phase plane, critical level of synchronization that can be tolerated by the persistent spiking activity, and speed of reconvergence to the limit cycle. Our analysis revealed that the self-sustained activity is more robust to synchronization when each individual neuron is closer to SNIC bifurcation (closer to being an intrinsic oscillator), the recurrent synaptic excitation is stronger, and the synaptic decay is slower, which is in agreement with the existing data on local circuits in the cortex that show sustained activity.
Applications that cater to the needs of disaster incident response generate large amount of data and demand large computational resource access. Such datasets are usually collected in real-time at the incident scenes using different Internet of Things (IoT) devices. Hierarchical clouds, i.e., core and edge clouds, can help these applications’ real-time data orchestration challenges as well as with their IoT operations scalability, reliability and stability by overcoming infrastructure limitations at the ad-hoc wireless network edge. Routing is a crucial infrastructure management orchestration mechanism for such systems. Current geographic routing or greedy forwarding approaches designed for early wireless ad-hoc networks lack efficient solutions for disaster incident-supporting applications, given the high-speed and low-latency data delivery that edge cloud gateways impose. In this paper, we present a novel Artificial Intelligent (AI)-augmented geographic routing approach, that relies on an area knowledge obtained from the satellite imagery (available at the edge cloud) by applying deep learning. In particular, we propose a stateless greedy forwarding that uses such an environment learning to proactively avoid the local minimum problem by diverting traffic with an algorithm that emulates electrostatic repulsive forces. In our theoretical analysis, we show that our Greedy Forwarding achieves in the worst case a path stretch approximation bound with respect to the shortest path, without assuming presence of symmetrical links or unit disk graphs. We evaluate our approach with both numerical and event-driven simulations, and we establish the practicality of our approach in a real incident-supporting hierarchical cloud deployment to demonstrate improvement of application level throughput due to a reduced path stretch under severe node failures and high mobility challenges of disaster response scenarios.
The distractive effects on attentional task performance in different paradigms are analyzed in this paper. I demonstrate how distractors may negatively affect (interference effect), positively (redundancy effect) or neutrally (null effect). Distractor effects described in literature are classified in accordance with their hypothetical source. The general rule of the theory is also introduced. It contains the formal prediction of the particular distractor effect, based on entropy and redundancy measures from the mathematical theory of communication (Shannon, 1948). Single- vs dual-process frameworks are considered for hypothetical mechanisms which underpin the distractor effects. Distractor profiles (DPs) are also introduced for the formalization and simple visualization of experimental data concerning the distractor effects. Typical shapes of DPs and their interpretations are discussed with examples from three frequently cited experiments. Finally, the paper introduces hierarchical hypothesis that states the level-fashion modulating interrelations between distractor effects of different classes.
This article describes the expierence of studying factors influencing the social well-being of educational migrants as mesured by means of a psychological well-being scale (A. Perrudet-Badoux, G.A. Mendelsohn, J.Chiche, 1988) previously adapted for Russian by M.V. Sokolova. A statistical analysis of the scale's reliability is performed. Trends in dynamics of subjective well-being are indentified on the basis the correlations analysis between the condbtbions of adaptation and its success rate, and potential mechanisms for developing subjective well-being among student migrants living in student hostels are described. Particular attention is paid to commuting as a factor of adaptation.