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Excitatory versus inhibitory feedback in Bayesian formulations of scene construction

JOURNAL OF THE ROYAL SOCIETY INTERFACE 2019年第154期16卷 20180344.-20180344.页

作者： Abadi, Alireza Khatoon Yahya, Keyvan Amini, Massoud Friston, Karl Heinke, Dietmar Tarbiat Modares Univ Fac Math Sci Dept Math Tehran 14115134 Iran Tech Univ Chemnitz Fac Informat Str Nationen 62R B216 D-09111 Chemnitz Germany UCL Inst Neurol Wellcome Trust Ctr Neuroimaging 12 Queen Sq London WC1N 3BG England Univ Birmingham Sch Psychol Ctr Computat Neurosci & Cognit Robot Birmingham B15 2TT W Midlands England

The selective attention for identification model (SAIM) is an established model of selective visual attention. SAIM implements translation-invariant object recognition, in scenes with multiple objects, using the parallel distributed processing (PDP) paradigm. Here, we show that SAIM can be formulated as Bayesian inference. Crucially, SAIM uses excitatory feedback to combine top-down information (i.e. object knowledge) with bottom-up sensory information. By contrast, predictive coding implementations of Bayesian inference use inhibitory feedback. By formulating SAIM as a predictive coding scheme, we created a new version of SAIM that uses inhibitory feedback. Simulation studies showed that both types of architectures can reproduce the response time costs induced by multiple objects-as found in visual search experiments. However, due to the different nature of the feedback, the two SAIM schemes make distinct predictions about the motifs of microcircuits mediating the effects of top-down afferents. We discuss empirical (neuroimaging) methods to test the predictions of the two inference architectures.

关键词： selective visual attention computational modelling active inference parallel distributed processing neuroimaging

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Seismic Hazard Visualization from Big Simulation Data: Cluster Analysis of Long-Period Ground-Motion Simulation Data

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JOURNAL OF DISASTER RESEARCH 2017年第2期12卷 233-240页

作者： Maeda, Takahiro Fujiwara, Hiroyuki Natl Res Inst Earth Sci & Disaster Resilience 3-1 Tennodai Tsukuba Ibaraki 3050006 Japan

This paper describes a method of extracting the relation between the ground-motion characteristics of each area and a seismic source model, based on ground-motion simulation data output in planar form for many earthquake scenarios, and the construction of a parallel distributed processing system where this method is implemented. The extraction is realized using two-stage clustering. In the first stage, the ground-motion indices and scenario parameters are used as input data to cluster the earthquake scenarios within each evaluation mesh. In the second stage, the meshes are clustered based on the similarity of earthquake-scenario clustering. Because the mesh clusters can be correlated to the geographical space, it is possible to extract the relation between the ground-motion characteristics of each area and the scenario parameters by examining the relation between the mesh clusters and scenario clusters obtained by the two-stage clustering. The results are displayed visually;they are saved as GeoTIFF image files. The system was applied to the long-period ground-motion simulation data for hypothetical megathrust earthquakes in the Nankai Trough. This confirmed that the relation between the extracted ground-motion characteristics of each area and scenario parameters is in agreement with the results of ground-motion simulations.

关键词： long-period ground motion visualization simulation parallel distributed processing clustering

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A parallel Implementation of Reinforced Learning Model used in Analyzing Risky Decision Making 14

A Parallel Implementation of Reinforced Learning Model used ...

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International Workshop on parallel Optimization using/for Multi and Many-Core High Performance Computing (POMCO) in conjunction with 14th International Conference on High Performance Computing and Simulation (HPCS)

作者： Gavirangaswamy, Vinay B. Gupta, Ajay Gupta, Aakash Western Michigan Univ Dept Comp Sci Kalamazoo MI USA Thredup Inc San Francisco CA USA

ISBN: (纸本)9781509020881

Analyzing datasets for Risky Decision Making (RDM) is a challenging task involving the identification of varied decision making patterns and the categorization of individuals. Researchers from various fields as diverse as psychology and marketing are actively working to identify suitable techniques, which will allow understanding decision making processes better. Researchers have commonly used machine learning algorithms to model decision making processes. However, the high computational costs of most machine learning algorithms make such endeavors challenging for increasingly large datasets. One of the most promising approaches is to use ensemble clustering for RDM analysis. Ensemble clustering is computationally intensive and thus we propose to improve its performance. Our study reveals that computational overhead is introduced through the use of dimensions in ensemble cluster RDM analyses. Improving performance requires more than the parallelization of individual clustering techniques of the ensemble. We therefore propose a FIFO queue based implementation for analyzing RDM datasets using a HPC cluster on a distributed system. Our technique is able to achieve almost a linear speedup (e.g. 44.79x using 48 MPI threads). Possible shortcomings of the proposed method, opportunities for future work, and alternative parallelization scenarios are also discussed in this paper.

关键词： component ensemble clustering application machine learning MPI multi-objective optimization parallel distributed processing risky decision making

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Task dependent lexicality effects support interactive models of reading: A meta-analytic neuroimaging review

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NEUROPSYCHOLOGIA 2015年第0期67卷 148-158页

作者： McNorgan, Chris Chabal, Sarah O'Young, Daniel Lukic, Sladjana Booth, James R. Northwestern Univ Dept Commun Sci & Disorders Evanston IL 60208 USA Univ Texas Austin Dept Commun Sci & Disorders Austin TX 78712 USA SUNY Buffalo Dept Psychol Buffalo NY 14260 USA

Models of reading must explain how orthographic input activates a phonological representation, and elicits the retrieval of word meaning from semantic memory. Comparisons between tasks that theoretically differ with respect to the degree to which they rely on connections between orthographic, phonological and semantic systems during reading can thus provide valuable insight into models of reading, but such direct comparisons are not well-represented in the literature. An ALE meta-analysis explored lexicality effects directly contrasting words and pseudowords using the lexical decision task and overt or covert naming, which we assume rely most on the semantic and phonological systems, respectively. Interactions between task and lexicality effects demonstrate that different demands of the lexical decision and naming tasks lead to different manifestations of lexicality effects. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： fMRI Psycholinguistics Activation likelihood estimate Lexical decision Naming parallel distributed processing

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PSPs and ERPs: Applying the dynamics of post-synaptic potentials to individual units in simulation of temporally extended Event-Related Potential reading data

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BRAIN AND LANGUAGE 2014年 132卷 22-27页

作者： Laszlo, Sarah Armstrong, Blair C. SUNY Binghamton Dept Psychol Binghamton NY 13902 USA Basque Ctr Cognit Brain & Language San Sebastian 20009 Spain

The parallel distributed processing (PDP) framework is built on neural-style computation, and is thus well-suited for simulating the neural implementation of cognition. However, relatively little cognitive modeling work has concerned neural measures, instead focusing on behavior. Here, we extend a PDP model of reading-related components in the Event-Related Potential (ERP) to simulation of the N400 repetition effect. We accomplish this by incorporating the dynamics of cortical post-synaptic potentials the source of the ERP signal into the model. Simulations demonstrate that application of these dynamics is critical for model elicitation of repetition effects in the time and frequency domains. We conclude that by advancing a neurocomputational understanding of repetition effects, we are able to posit an interpretation of their source that is both explicitly specified and mechanistically different from the well-accepted cognitive one. (C) 2014 Elsevier Inc. All rights reserved.

关键词： parallel distributed processing ERPs N400 Visual word recognition Repetition effects Post-synaptic potentials Neural computation

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Translation Metamorphosis: a process-oriented approach to onomastics

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PERSPECTIVES-STUDIES IN TRANSLATOLOGY 2014年第2期22卷 271-281页

作者： Ilkhanipour, Negin Univ Tehran Dept Linguist Tehran Iran

Taking a process-oriented approach, the present paper employs two of the principal theories of the semantics of proper names, the description theory and the causal theory, in order to arrive at 'identifying function', the element responsible for retaining the effects of the original proper names into the target text. A continuum with two extremes of 'identifying-function-reduced' and 'identifying-function-embedded' translations is then proposed for the translation of proper names. The paper also attempts to show that, as representatives of individuals with different identifying functions, all the possible coexistent translations on this continuum are coactivated and compete for their realisation in the final actual product of translation. This coexistence, coactivation and competition will lead us to the concept of 'Translation Metamorphosis'.

关键词： identifying function causal theory proper name description theory parallel distributed processing Bilingual Bootstrapping

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Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition

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COGNITIVE SCIENCE 2014年第6期38卷 1139-1189页

作者： McClelland, James L. Mirman, Daniel Bolger, Donald J. Khaitan, Pranav Stanford Univ Dept Psychol Stanford CA 94305 USA Drexel Univ Dept Psychol Philadelphia PA 19104 USA Moss Rehabil Res Inst Philadelphia PA USA Univ Maryland Dept Psychol College Pk MD USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis-the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the interactive activation model of letter and word perception and the TRACE model of speech perception, as early attempts to explore this hypothesis, and review the experimental evidence relevant to their assumptions and predictions. We consider how well these models address the computational challenge posed by the problem of perception, and we consider how consistent they are with evidence from behavioral experiments. We examine empirical and theoretical controversies surrounding the idea of interactive processing, including a controversy that swirls around the relationship between interactive computation and optimal Bayesian inference. Some of the implementation details of early versions of interactive activation models caused deviation from optimality and from aspects of human performance data. More recent versions of these models, however, overcome these deficiencies. Among these is a model called the multinomial interactive activation model, which explicitly links interactive activation and Bayesian computations. We also review evidence from neurophysiological and neuroimaging studies supporting the view that interactive processing is a characteristic of the perceptual processing machinery in the brain. In sum, we argue that a computational analysis, as well as behavioral and neuroscience evidence, all support the Interactive Activation hypothesis. The evidence suggests that contemporar

关键词： Perception Interactive activation parallel distributed processing Connectionist models Optimal perceptual inference Neural networks

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Beyond Concurrent Engineering: parallel distributed Engineering for More Adaptability and Less Energy Consumption

Beyond Concurrent Engineering: Parallel Distributed Engineer...

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20th ISPE International Conference on Concurrent Engineering

作者： Fukuda, Shuichi Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781614993025;9781614993018

A parallel distributed processing (PDP) or Neural Network model is proposed to re-organize industries so that they can share knowledge and experience among them. This re-organized industry framework, which is called parallel distributed Engineering here, develops interchangeable components at its intermediate level and combines them into final products to meet the requirements of customers. Thus, it brings forth greater flexibility to adapt to very frequently and extensively changing situations and what is more important, it reduces time, cost and energy consumption and increase productivity considerably. And it will satisfy customers more because their diverse requirements can be more precisely met.

关键词： parallel distributed processing Neural Network interchangeable common components reduction of time cost and energy consumption increase of productivity

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Response to Helen Grebow

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International Journal of Psychoanalytic Self Psychology 2014年第4期9卷 373-382页

作者： Shane, Estelle Institute of Contemporary Psychoanalysis United States

My discussion focuses on the excellence and complexity of Helen Grebow’s presentation on the therapist’s experience of the uncanny, unsettling, even surreal nature of the verbal and non-verbal resonance that emerges in the analytic dyad. She explores how the analyst is able to grasp and translate the patient’s embodied and enacted experience, using data drawn from such sources as infant developmental studies, cognitive theory, neurological research, brain study, and systems understanding to conceptualize this mysterious experience of seemingly unnatural knowing. Yet despite her explication of these resources, we remain in awe of the process, in a state of uncertainty about how it all works. I turn to alternative resources for understanding this process of subsymbolic communication, including brief forays into the writings of Claudio Arnetoli on connectionism, neural networking, and parallel distributed representations;the contributions of Elizabeth Lloyd Meyer on the anomalous, on uncanny unconscious communication, and the bounded mind versus the radically connected mind;of the discussion of Bruce Reis on recognition and mutual recognition, and finally, on the work of William Coburn on psychoanalytic complexity. © The International Association for Psychoanalytic Self Psychology.

关键词： connectionism enactment mutual recognition parallel distributed processing psychoanalytic complexity recognition subsymbolic communication

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A connectionist model of category learning by individuals with high-functioning autism spectrum disorder

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COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2013年第2期13卷 371-389页

作者： Dovgopoly, Alexander Mercado, Eduardo, III SUNY Buffalo Dept Psychol Buffalo NY 14260 USA

Individuals with autism spectrum disorder (ASD) show atypical patterns of learning and generalization. We explored the possible impacts of autism-related neural abnormalities on perceptual category learning using a neural network model of visual cortical processing. When applied to experiments in which children or adults were trained to classify complex two-dimensional images, the model can account for atypical patterns of perceptual generalization. This is only possible, however, when individual differences in learning are taken into account. In particular, analyses performed with a self-organizing map suggested that individuals with high-functioning ASD show two distinct generalization patterns: one that is comparable to typical patterns, and a second in which there is almost no generalization. The model leads to novel predictions about how individuals will generalize when trained with simplified input sets and can explain why some researchers have failed to detect learning or generalization deficits in prior studies of category learning by individuals with autism. On the basis of these simulations, we propose that deficits in basic neural plasticity mechanisms may be sufficient to account for the atypical patterns of perceptual category learning and generalization associated with autism, but they do not account for why only a subset of individuals with autism would show such deficits. If variations in performance across subgroups reflect heterogeneous neural abnormalities, then future behavioral and neuroimaging studies of individuals with ASD will need to account for such disparities.

关键词： Autism Perceptual categorization Perceptual learning Computational model Neural network Connectionism parallel distributed processing

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