Architectonic subdivisions of the amygdalar complex of a primitive marsupial (Didelphis aurita)

The architecture of the amygdaloid complex of a marsupial, the opossum Didelphis aurita, was analyzed using classical stains like Nissl staining and myelin (Gallyas) staining, and enzyme histochemistry for acetylcholinesterase and NADPH-diaphorase. Most of the subdivisions of the amygdaloid complex described in eutherian mammals were identified in the opossum brain. NADPH-diaphorase revealed reactivity in the neuropil of nearly all amygdaloid subdivisions with different intensities, allowing the identification of the medial and lateral subdivisions of the cortical posterior nucleus and the lateral subdivision of the lateral nucleus. The lateral, central, basolateral and basomedial nuclei exhibited acetylcholinesterase positivity, which provided a useful chemoarchitectural criterion for the identification of the anterior basolateral nucleus. Myelin stain allowed the identification of the medial subdivision of the lateral nucleus, and resulted in intense staining of the medial subdivisions of the central nucleus. The medial, posterior, and cortical nuclei, as well as the amygdalopiriform area did not exhibit positivity for myelin staining. On the basis of cyto- and chemoarchitectural criteria, the present study highlights that the opossum amygdaloid complex shares similarities with that of other species, thus supporting the idea that the organization of the amygdala is part of a basic plan conserved through mammalian evolution. (C) 2008 Elsevier Inc. All rights reserved.

Chaotic synchronization in general network topology for scene segmentation

Chaotic synchronization has been discovered to be an important property of neural activities, which in turn has encouraged many researchers to develop chaotic neural networks for scene and data analysis. In this paper, we study the synchronization role of coupled chaotic oscillators in networks of general topology. Specifically, a rigorous proof is presented to show that a large number of oscillators with arbitrary geometrical connections can be synchronized by providing a sufficiently strong coupling strength. Moreover, the results presented in this paper not only are valid to a wide class of chaotic oscillators, but also cover the parameter mismatch case. Finally, we show how the obtained result can be applied to construct an oscillatory network for scene segmentation.

Chaotic synchronization in 2D lattice for scene segmentation

Synchronization and chaos play important roles in neural activities and have been applied in oscillatory correlation modeling for scene and data analysis. Although it is an extensively studied topic, there are still few results regarding synchrony in locally coupled systems. In this paper we give a rigorous proof to show that large numbers of coupled chaotic oscillators with parameter mismatch in a 2D lattice can be synchronized by providing a sufficiently large coupling strength. We demonstrate how the obtained result can be applied to construct an oscillatory network for scene segmentation. (C) 2007 Elsevier B.V. All rights reserved.

Endogenous-like orienting of visual attention in rats

This study investigated the orienting of visual attention in rats using a 3-hole nose-poke task analogous to Posner, Information processing in cognition: the Loyola Symposium, Erlbaum, Hillsdale, (1980) covert attention task for humans. The effects of non-predictive (50% valid and 50% invalid) and predictive (80% valid and 20% invalid) peripheral visual cues on reaction times and response accuracy to a target stimulus, using Stimuli-Onset Asynchronies (SOAs) varying between 200 and 1,200 ms, were investigated. The results showed shorter reaction times in valid trials relative to invalid trials for both subjects trained in the non-predictive and predictive conditions, particularly when the SOAs were 200 and 400 ms. However, the magnitude of this validity effect was significantly greater for subjects exposed to predictive cues, when the SOA was 800 ms. Subjects exposed to invalid predictive cues exhibited an increase in omission errors relative to subjects exposed to invalid non-predictive cues. In contrast, valid cues reduced the proportion of omission errors for subjects trained in the predictive condition relative to subjects trained in the non-predictive condition. These results are congruent with those usually reported for humans and indicate that, in addition to the exogenous capture of attention promoted by both predictive and non-predictive peripheral cues, rats exposed to predictive cues engaged an additional slower process equivalent to human`s endogenous orienting of attention. To our knowledge, this is the first demonstration of an endogenous-like process of covert orienting of visual attention in rats.

Effects of intensity and positional predictability of a visual stimulus on simple reaction time

The influence of visual stimuli intensity on manual reaction time (RT) was investigated under two different attentional settings: high (Experiment 1) and low (Experiment 2) stimulus location predictability. These two experiments were also run under both binocular and monocular viewing conditions. We observed that RT decreased as stimulus intensity increased. It also decreased as the viewing condition was changed from monocular to binocular as well as the location predictability shifted from low to high. A significant interaction was found between stimulus intensity and viewing condition, but no interaction was observed between neither of these factors and location predictability. These findings support the idea that the stimulus intensity effect arises from purely sensory, pre-attentive mechanisms rather than deriving from more efficient attentional capture. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

A network of integrate and fire neurons for visual selection

Biological systems have facility to capture salient object(s) in a given scene, but it is still a difficult task to be accomplished by artificial vision systems. In this paper a visual selection mechanism based on the integrate and fire neural network is proposed. The model not only can discriminate objects in a given visual scene, but also can deliver focus of attention to the salient object. Moreover, it processes a combination of relevant features of an input scene, such as intensity, color, orientation, and the contrast of them. In comparison to other visual selection approaches, this model presents several interesting features. It is able to capture attention of objects in complex forms, including those linearly nonseparable. Moreover, computer simulations show that the model produces results similar to those observed in natural vision systems.