We present a cortical-based model for computing the perceptual salience of contours embedded in noisy images. It has been suggested (Gilbert, 1992; Field, Hayes & Hess, 1993) that horizontal intra-cortical connections in primary visual cortex may modulate contrast detection thresholds and pre-attentive "popout ". In our model, horizontal connections mediate context-dependent facilitatory and inhibitory interactions among oriented cells. Strongly facilitated cells undergo temporal synchronization; and perceptual salience is determined by the level of synchronized activity. The model accounts for a range of reported psychophysical and physiological effects of contour salience (Polat & Sagi, 1993, 1994; Kapadia, Ito, Gilbert & Westheimer, 1995; Field et al., 1993; Kovács, Polat & Norcia, 1996; Pettet, McKee & Grzywacz, 1996). In particular, the model proposes that intrinsic properties of synchronization account for the increased salience of smooth, closed contours (Kovács & Julesz, 1993, ...

Although perseveration---the inappropriate repetition of previous responses---is quite common among patients with neurological damage, relatively few detailed computational accounts of its various forms have been put forth. A particularly well-documented variety involves the pattern of errors made by "optic aphasic" patients, who have a selective deficit in naming visually-presented objects. Based on our previous work in modeling impaired reading for meaning in deep dyslexia, we develop a connectionist simulation of visual object naming. The major extension in the present work is the incorporation of short-term correlational weights that bias the network towards reproducing patterns of activity that have occurred on recently preceding trials. Under damage, the network replicates the complex semantic and perseverative effects found in the optic aphasic error pattern. Further analysis reveals that the perseverative effects are strongest when the lesions are near or within semanti...

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A model of orientation and direction selective cells is proposed. The dendritic connections of each cell are constructed with a local viewpoint. No consideration of the global function of the entire network is considered. Intermediate cells are not needed to make a cell selective, instead other cells with the same selectivity are used. The mechanism of the system is to update the previous activity rather than continually recreate selective detection. In the circuitry, spontaneous activity plays a functional role. This is needed for the circuitry to carry out initial detection of stimuli. The model allows a reduction of complexity in the circuits. In addition to a presentation of the models, two simulations implemented on computer are discussed. 1

An implementation of a distributed connectionist network of spiking neuron-like elements is presented. Spiking nodes fire at a precise moment and transmit their activation, with particular strengths and delays, to nodes connected to them. The receiving nodes accumulate potential, but also slowly their potential through decay. When the potential of the node reaches a particular threshold, it emits a spike. Thereafter, the potential is reset to a resting value. As with real neurons, there is a short refractory period during which this node will be completely insensitive to incoming signals, after which its sensitivity will slowly increase. Precise timing properties are used to represent symbols in a distributed manner and to solve the problems of variable binding and multiple instantiation. Several predictions about human short-term memory, predicate processing, complex reasoning, and multiple instantiation arise from this model. This network shows how symbolic processing can be achieved using neurologically and psychologically plausible mechanisms that also have the advantage of generalization and noise tolerance found in connectionist networks.

We discuss the problem of segmentation in pattern recognition. We adopt the model and the general approach in the landmark paper by Wang, Buhmann and von der Malsburg (Neural Computation, (1990), 2, 94--106), and expand their model in a number of ways. We review their solution to the segmentation problem in associative memory, which consists in feature binding being expressed by synchrony relations between oscillators or populations of neurons. We extend the model by introducing a law of synaptic change, which allows the network to learn by structuring itself in response to stimuli with relevant features. We discuss the problem of interference between pattern completion and the learning of new memories. We also propose a form of multiplexing of input information taking advantage of the time-structure of the neurons' response. It is based on the assessment of analog as well as of binary properties of the stimuli and provides for an enhancement of the network's processing capacity. The relevance of the results for biological systems is pointed out. # 2000 Elsevier Science Ltd. All rights reserved.

It has been observed that cortical neurons display synchronous firing for some stimuli and not for others. The resulting synchronous cell assemblies are thought to form the basis of object perception. In this paper this 'dynamic linking' phenomenon is demonstrated in networks of binary neurons with stochastic dynamics. Analytical treatment within the mean field theory and linear response theory is possible and is compared with simulations. We establish that correlations are a sensitive function of the spatial coherence in the stimulus. We discuss the possibility to use these correlations as a mechanism for scene segmentation. PACS numbers: 02.70.-c, 05.50.+q, 87.10.+e, 87,22.As 1 Introduction It is well established, that the behavior of sensory neurons in the visual cortex can be described by a receptive field: A neuron is sensitive to certain specific stimuli and not to others [1]. It is often assumed that the role of individual cells is to represent local visual features, such as ed...

ABSTRACT. Recent findings in neuroscience strongly suggest that an object’s features (e.g., its color, texture, shape, etc.) are represented in separate areas of the visual cortex. Although represented in separate neuronal areas, somehow the feature representations are brought together as a single, unified object of visual consciousness. This raises a question of binding: how do neural activities in separate areas of the visual cortex function to produce a feature-unified object of visual consciousness? Several prominent neuroscientists have adopted neural synchrony and attention-based approaches to explain object feature binding. I argue that although neural synchrony and/or attentional mechanisms might function to disambiguate an object’s features, it is difficult to see how either of these mechanisms could fully explain the unity of an object’s features at the level of visual consciousness. After presenting a detailed critique of neural synchrony and attention-based approaches to object feature binding, I propose interactive hierarchical structuralism (IHS). This view suggests that a unified percept (i.e., a feature-unified object

Distributed connectionists models of reasoning must solve the problem of multiple instantiation for two reasons. First, reasoning can involve two or more instantiations of the same predicate or object. Second, in a distributed representation, two closely related concepts must share common resources or nodes. Reasoning with these two concepts requires that nodes pertaining to them be instantiated twice. This paper presents a model (INFERNET) that uses temporal synchrony variable binding. It proposes a particular solution to the problem of multiple instantiation that involves the use of different oscillation frequencies. This solution implies some predictions. These predictions are tested on human participants, and the results are presented here. They confirm model predictions. Introduction Multiple instantiation involves the simultaneous use of the same parts of the knowledge base in different ways. Knowing that "John is in love with Rita" and that "Rita is in love with John", you can ...

ABSTRACT: A central issue in philosophy and neuroscience is the problem of unified visual consciousness. This problem has arisen because we now know that an object’s stimulus features (e.g., its color, texture, shape, etc.) generate activity in separate areas of the visual cortex (Felleman & Van Essen, 1991). For example, recent evidence indicates that there are very few, if any, neural connections between specific visual areas, such as those that correlate with color and motion (Bartels & Zeki, 2006; Zeki, 2003). So how do unified objects arise in visual consciousness? Some neuroscientists propose that neural synchrony is the mechanism that binds an object’s features into a unity (e.g., see Crick,