Above are papers related to the following paper: [Paper_ID] Paper #1140543. 	[Title] Computational modeling of spatial attention [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #523010. 	[Title] The end of the line for a brain-damaged model of unilateral neglect [Abstract] For over a century, it has been known that damage to the right hemisphere of the brain can cause patients to be unaware of the contralesional side of space. This condition, known as unilateral neglect, represents a collection of clinically related spatial disorders characterized by the failure in free vision to respond, explore, or orient to stimuli predominantly located on the side of space opposite the damaged hemisphere. Recent studies using the simple task of line bisection, a conventional diagnostic test, have proved surprisingly revealing with respect to the spatial and attentional impairments involved in neglect. In line bisection, the patient is asked to mark the midpoint of a thin horizontal line on a sheet of paper. Neglect patients generally transect far to the right of the center. Extensive studies of line bisection have been conducted, manipulating|among other factors|line length, orientation, and position. We have simulated the pattern of results using an existing computational model of visual perception and selective attention called morsel (Mozer, 1991). morsel has already been used to model data in a related disorder, neglect dyslexia (Mozer & Behrmann, 1990). In this earlier work, morsel was "lesioned" in accordance with the damage we suppose to have occurred in the brains of Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1140548. 	[Title] Lending Direction to Neural Networks [Abstract] We present a general formulation for a network of stochastic directional units. This formulation is an extension of the Boltzmann machine in which the units are not binary, but take on values on a cyclic range, between 0 and 2 radians. This measure is appropriate to many domains, representing cyclic or angular values, e.g., wind direction, days of the week, phases of the moon. The state of each unit in a Directional-Unit Boltzmann Machine (DUBM) is described by a complex variable, where the phase component specifies a direction; the weights are also complex variables. We associate a quadratic energy function, and corresponding probability, with each DUBM configuration. The conditional distribution of a unit's stochastic state is a circular version of the Gaussian probability distribution, known as the von Mises distribution. In a mean-field approximation to a stochastic dubm, the phase component of a unit's state represents its mean direction, and the magnitude component specifies the degree of certainty associated with this direction. This combination of a value and a certainty provides additional representational power in a unit. We present a proof that the settling dynamics for a mean-field DUBM cause convergence to a free energy minimum. Finally, we describe a learning algorithm and simulations that demonstrate a mean-field DUBM's ability to learn interesting mappings. fl To appear in: Neural Networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135589. 	[Title] Book Review New Kids on the Block way in the field of connectionist modeling. The [Abstract] Connectionist Models is a collection of forty papers representing a wide variety of research topics in connectionism. The book is distinguished by a single feature: the papers are almost exclusively contributions of graduate students active in the field. The students were selected by a rigorous review process and participated in a two week long summer school devoted to connectionism 2 . As the ambitious editors state in the foreword: These are bold claims and, if true, the reader is presented with an exciting opportunity to sample the frontiers of connectionism. Their words imply two ways to approach the book. The book must be read not just as a random collection of scientific papers, but also as a challenge to evaluate a controversial field. 2 This summer school is actually the third in a series, previous ones being held in 1986 and 1988. The proceedings of the 1988 summer school (which I had the priviledge of participating in) are reviewed by Nigel Goddard in [4]. Continuing the pattern, a fourth school is scheduled to be held in 1993 in Boulder, CO. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #135464. 	[Title] Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification [Abstract] Technical report CNS-TR-95-1 Center for Neural Systems McMaster University Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #739816. 	[Title] Learning to segment images using dynamic feature binding an isolated object in an image is [Abstract] Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalization of recurrent back propagation to complex-valued units. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #264556. 	[Title] A Delay-Line Based Motion Detection Chip [Abstract] Inspired by a visual motion detection model for the rabbit retina and by a computational architecture used for early audition in the barn owl, we have designed a chip that employs a correlation model to report the one-dimensional field motion of a scene in real time. Using subthreshold analog VLSI techniques, we have fabricated and successfully tested a 8000 transistor chip using a standard MOSIS process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #255628. 	[Title] Efficient Visual Search: A Connectionist Solution [Abstract] Searching for objects in scenes is a natural task for people and has been extensively studied by psychologists. In this paper we examine this task from a connectionist perspective. Computational complexity arguments suggest that parallel feed-forward networks cannot perform this task efficiently. One difficulty is that, in order to distinguish the target from distractors, a combination of features must be associated with a single object. Often called the binding problem, this requirement presents a serious hurdle for connectionist models of visual processing when multiple objects are present. Psychophysical experiments suggest that people use covert visual attention to get around this problem. In this paper we describe a psychologically plausible system which uses a focus of attention mechanism to locate target objects. A strategy that combines top-down and bottom-up information is used to minimize search time. The behavior of the resulting system matches the reaction time behavior of people in several interesting tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1109873. 	[Title] Object Selection Based on Oscillatory Correlation [Abstract] 1 Technical Report: OSU-CISRC-12/96 - TR67, 1996 Abstract One of the classical topics in neural networks is winner-take-all (WTA), which has been widely used in unsupervised (competitive) learning, cortical processing, and attentional control. Because of global connectivity, WTA networks, however, do not encode spatial relations in the input, and thus cannot support sensory and perceptual processing where spatial relations are important. We propose a new architecture that maintains spatial relations between input features. This selection network builds on LEGION (Locally Excitatory Globally Inhibitory Oscillator Networks) dynamics and slow inhibition. In an input scene with many objects (patterns), the network selects the largest object. This system can be easily adjusted to select several largest objects, which then alternate in time. We further show that a twostage selection network gains efficiency by combining selection with parallel removal of noisy regions. The network is applied to select the most salient object in real images. As a special case, the selection network without local excitation gives rise to a new form of oscillatory WTA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120817. 	[Title] Control of Selective Visual Attention: Modeling the "Where" Pathway [Abstract] Intermediate and higher vision processes require selection of a subset of the available sensory information before further processing. Usually, this selection is implemented in the form of a spatially circumscribed region of the visual field, the so-called "focus of attention" which scans the visual scene dependent on the input and on the attentional state of the subject. We here present a model for the control of the focus of attention in primates, based on a saliency map. This mechanism is not only expected to model the functionality of biological vision but also to be essential for the understanding of complex scenes in machine vision. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120084. 	[Title] VISIT: An Efficient Computational Model of Human Visual Attention [Abstract] One of the challenges for models of cognitive phenomena is the development of efficient and exible interfaces between low level sensory information and high level processes. For visual processing, researchers have long argued that an attentional mechanism is required to perform many of the tasks required by high level vision. This thesis presents VISIT, a connectionist model of covert visual attention that has been used as a vehicle for studying this interface. The model is efficient, exible, and is biologically plausible. The complexity of the network is linear in the number of pixels. Effective parallel strategies are used to minimize the number of iterations required. The resulting system is able to efficiently solve two tasks that are particularly difficult for standard bottom-up models of vision: computing spatial relations and visual search. Simulations show that the networks behavior matches much of the known psychophysical data on human visual attention. The general architecture of the model also closely matches the known physiological data on the human attention system. Various extensions to VISIT are discussed, including methods for learning the component modules. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135894. 	[Title] L 0 |The First Four Years Abstract A summary of the progress and plans of [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1126044. 	[Title] A Brief History of Connectionism [Abstract] Connectionist research is firmly established within the scientific community, especially within the multi-disciplinary field of cognitive science. This diversity, however, has created an environment which makes it difficult for connectionist researchers to remain aware of recent advances in the field, let alone understand how the field has developed. This paper attempts to address this problem by providing a brief guide to connectionist research. The paper begins by defining the basic tenets of connectionism. Next, the development of connectionist research is traced, commencing with connectionism's philosophical predecessors, moving to early psychological and neuropsychological influences, followed by the mathematical and computing contributions to connectionist research. Current research is then reviewed, focusing specifically on the different types of network architectures and learning rules in use. The paper concludes by suggesting that neural network research|at least in cognitive science|should move towards models that incorporate the relevant functional principles inherent in neurobiological systems. Please summarise all the given information.

Paper #1140543 has the title `Computational modeling of spatial attention  
` and the abstract `nan`.
Question: Which category should Paper #1140543 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1140543 has the title `Computational modeling of spatial attention  
` and the abstract `nan`.
Question: Which category should Paper #1140543 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks