Conversational success in Williams syndrome: communication in the face of cognitive and linguistic limitations

Williams syndrome (WS) is characterized by apparent relative strengths in language, facial processing and social cognition but by profound impairment in spatial cognition, planning and problem solving. Following recent research which suggests that individuals with WS may be less linguistically able than was once thought, in this paper we begin to investigate why and how they may give the impression of linguistic proficiency despite poor standardized test results. This case study of Brendan, a 12-year-old boy with WS, who presents with a considerable lack of linguistic ability, suggests that impressions of linguistic competence may to some extent be the result of conversational strategies which enable him to compensate for various cognitive and linguistic deficits with a considerable degree of success. These conversational strengths are not predicted by his standardized language test results, and provide compelling support for the use of approaches such as Conversation Analysis in the assessment of individuals with communication impairments.

The effect of postnatal depression on mother-infant interaction, infant response to the still-face perturbation, and performance on an instrumental learning task

A representative community sample of primiparous depressed women and a nondepressed control group were assessed while in interaction with their infants at 2 months postpartum. At 3 months, infants were assessed on the Still-face perturbation of face to face interaction, and a subsample completed an Instrumental Learning paradigm. Compared to nondepressed women, depressed mothers' interactions were both less contingent and less affectively attuned to infant behavior. Postnatal depression did not adversely affect the infant's performance in either the Still-face perturbation or the Instrumental Learning assessment. Maternal responsiveness in interactions at 2 months predicted the infant's performance in the Instrumental Learning assessment but not in the Still-face perturbation. The implications of these findings for theories of infant cognitive and emotional development are discussed.

Prefrontal social cognition network dysfunction underlying face encoding and social anxiety in fragile X syndrome

Individuals with fragile X syndrome (FXS) commonly display characteristics of social anxiety, including gaze aversion, increased time to initiate social interaction, and difficulty forming meaningful peer relationships. While neural correlates of face processing, an important component of social interaction, are altered in FXS, studies have not examined whether social anxiety in this population is related to higher cognitive processes, such as memory. This study aimed to determine whether the neural circuitry involved in face encoding was disrupted in individuals with FXS, and whether brain activity during face encoding was related to levels of social anxiety. A group of 11 individuals with FXS (5 M) and 11 age-and gender-matched control participants underwent fMRI scanning while performing a face encoding task with onlineeye-tracking. Results indicate that compared to the control group, individuals with FXS exhibited decreased activation of prefrontal regions associated with complex social cognition, including the medial and superior frontal cortex, during successful face encoding. Further, the FXS and control groups showed significantly different relationships between measures of social anxiety (including gaze-fixation) and brain activity during face encoding. These data indicate that social anxiety in FXS may be related to the inability to successfully recruit higher level social cognition regions during the initial phases of memory formation. (C) 2008 Elsevier Inc. All rights reserved.

Development of multi-electrode array screening for anticonvulsants in acute rat brain slices

The acute hippocampal brain slice preparation is an important in vitro screening tool for potential anticonvulsants. Application of 4-aminopyridine (4-AP) or removal of external Mg2+ ions induces epileptiform bursting in slices which is analogous to electrical brain activity seen in status epilepticus states. We have developed these epileptiform models for use with multi-electrode arrays (MEAs), allowing recording across the hippocampal slice surface from 59 points. We present validation of this novel approach and analyses using two anticonvulsants, felbamate and phenobarbital, the effects of which have already been assessed in these models using conventional extracellular recordings. In addition to assessing drug effects on commonly described parameters (duration, amplitude and frequency), we describe novel methods using the MEA to assess burst propagation speeds and the underlying frequencies that contribute to the epileptiform activity seen. Contour plots are also used as a method of illustrating burst activity. Finally, we describe hitherto unreported properties of epileptiform, bursting induced by 100 mu M 4-AP or removal of external Mg2+ ions. Specifically, we observed decreases over time in burst amplitude and increase over time in burst frequency in the absence of additional pharmacological interventions. These MEA methods enhance the depth, quality and range of data that can be derived from the hippocampal slice preparation compared to conventional extracellular recordings. it may also uncover additional modes of action that contribute to anti-epileptiform drug effects. (C) 2009 Elsevier B.V. All rights reserved.

Autonomous execution of tasks by swarm carrier agents in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing system? The work reported in this paper is motivated towards bridging this gap by proposing swarm-array computing, a novel technique to achieve autonomy for distributed parallel computing systems. Among three proposed approaches, the second approach, namely 'Intelligent Agents' is of focus in this paper. The task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier. agents and can be seamlessly transferred between cores in the event of a pre-dicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed approach is validated on a multi-agent simulator.

Intelligent agents: are they feasible in swarm-array computing?

The work reported in this paper proposes 'Intelligent Agents', a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Intelligent cores and intelligent agents: two approaches to achieve autonomy in swarm-array computing

How can a bridge be built between autonomic computing approaches and parallel computing systems? How can autonomic computing approaches be extended towards building reliable systems? How can existing technologies be merged to provide a solution for self-managing systems? The work reported in this paper aims to answer these questions by proposing Swarm-Array Computing, a novel technique inspired from swarm robotics and built on the foundations of autonomic and parallel computing paradigms. Two approaches based on intelligent cores and intelligent agents are proposed to achieve autonomy in parallel computing systems. The feasibility of the proposed approaches is validated on a multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Building reliable systems for space applications using swarm-array computing

Space applications demand the need for building reliable systems. Autonomic computing defines such reliable systems as self-managing systems. The work reported in this paper combines agent-based and swarm robotic approaches leading to swarm-array computing, a novel technique to achieve self-managing distributed parallel computing systems. Two swarm-array computing approaches based on swarms of computational resources and swarms of tasks are explored. FPGA is considered as the computing system. The feasibility of the two proposed approaches that binds the computing system and the task together is simulated on the SeSAm multi-agent simulator.

Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

Investigating the feasibility of a swarm-array computing approach

The work reported in this paper proposes a novel synergy between parallel computing and swarm robotics to offer a new computing paradigm, 'swarm-array computing' that can harness and apply autonomic computing for parallel computing systems. One approach among three proposed approaches in swarm-array computing based on landscapes of intelligent cores, in which the cores of a parallel computing system are abstracted to swarm agents, is investigated. A task is executed and transferred seamlessly between cores in the proposed approach thereby achieving self-ware properties that characterize autonomic computing. FPGAs are considered as an experimental platform taking into account its application in space robotics. The feasibility of the proposed approach is validated on the SeSAm multi-agent simulator.

An autonomic approach in swarm-array computing

Can autonomic computing concepts be applied to traditional multi-core systems found in high performance computing environments? In this paper, we propose a novel synergy between parallel computing and swarm robotics to offer a new computing paradigm, `Swarm-Array Computing' that can harness and apply autonomic computing for parallel computing systems. One approach among three proposed approaches in swarm-array computing based on landscapes of intelligent cores, in which the cores of a parallel computing system are abstracted to swarm agents, is investigated. A task gets executed and transferred seamlessly between cores in the proposed approach thereby achieving self-ware properties that characterize autonomic computing. FPGAs are considered as an experimental platform taking into account its application in space robotics. The feasibility of the proposed approach is validated on the SeSAm multi-agent simulator.