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Basal Ganglia and Thalamic Contributions to Language Function: Insights from A parallel distributed processing Perspective

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NEUROPSYCHOLOGY REVIEW 2021年第3期31卷 495-515页

作者： Nadeau, Stephen E. Malcom Randall VA Med Ctr Res Serv 1601 SW Archer Rd Gainesville FL 32608 USA Malcom Randall VA Med Ctr Brain Rehabil Res Ctr 1601 SW Archer Rd Gainesville FL 32608 USA Univ Florida Dept Neurol Coll Med 1601 SW Archer Rd Gainesville FL 32608 USA

Cerebral representations are encoded as patterns of activity involving billions of neurons. parallel distributed processing (PDP) across these neuronal populations provides the basis for a number of emergent properties: 1) processing occurs and knowledge (long term memories) is stored (as synaptic connection strengths) in exactly the same networks;2) networks have the capacity for setting into stable attractor states corresponding to concepts, symbols, implicit rules, or data transformations;3) networks provide the scaffold for the acquisition of knowledge but knowledge is acquired through experience;4) PDP networks are adept at incorporating the statistical regularities of experience as well as frequency and age of acquisition effects;5) networks enable content-addressable memory;6) because knowledge is distributed throughout networks, they exhibit the property of graceful degradation;7) networks intrinsically provide the capacity for inference. This paper details the features of the basal ganglia and thalamic systems (recurrent and distributed connectivity) that support PDP. The PDP lens and an understanding of the attractor trench dynamics of the basal ganglia provide a natural explanation for the peculiar dysfunctions of Parkinson's disease and the mechanisms by which dopamine deficiency is causal. The PDP lens, coupled with the fact that the basal ganglia of humans bears strong homology to the basal ganglia of lampreys and the central complex of arthropods, reveals that the fundamental function of the basal ganglia is computational and involves the reduction of the vast dimensionality of a complex multi-dimensional array of sensorimotor input into the optimal choice from a small repertoire of behavioral options - the essence of reactive intention (automatic responses to sensory input). There is strong evidence that the sensorimotor basal ganglia make no contributions to cognitive or motor function in humans but can cause serious dysfunction when pathological. I

关键词： parallel distributed processing Basal ganglia Thalamus Language

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Seismic Hazard Visualization from Big Simulation Data: Construction of a parallel distributed processing System for Ground Motion Simulation Data

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JOURNAL OF DISASTER RESEARCH 2016年第2期11卷 265-271页

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

We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.

关键词： seismic hazard visualization simulation parallel distributed processing

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An Extension of a parallel-distributed processing Framework of Reading Aloud in Japanese: Human Nonword Reading Accuracy Does Not Require a Sequential Mechanism

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COGNITIVE SCIENCE 2017年第Suppl 6期41卷 1288-1317页

作者： Ikeda, Kenji Ueno, Taiji Ito, Yuichi Kitagami, Shinji Kawaguchi, Jun Nagoya Univ Dept Psychol Nagoya Aichi Japan Univ York Dept Psychol York N Yorkshire England Japan Soc Promot Sci Tokyo Japan Doshisha Univ Kyoto Japan

Humans can pronounce a nonword (e.g., rint). Some researchers have interpreted this behavior as requiring a sequential mechanism by which a grapheme-phoneme correspondence rule is applied to each grapheme in turn. However, several parallel-distributed processing (PDP) models in English have simulated human nonword reading accuracy without a sequential mechanism. Interestingly, the Japanese psycholinguistic literature went partly in the same direction, but it has since concluded that a sequential parsing mechanism is required to reproduce human nonword reading accuracy. In this study, by manipulating the list composition (i.e., pure word/nonword list vs. mixed list), we demonstrated that past psycholinguistic studies in Japanese have overestimated human nonword reading accuracy. When the more fairly reevaluated human performance was targeted, a newly implemented Japanese PDP model simulated the target accuracy as well as the error patterns. These findings suggest that PDP models are a more parsimonious way of explaining reading across various languages.

关键词： Reading parallel distributed processing Serial processing Nonword reading

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Interlocking Backpropagation: Improving depthwise model-parallelism

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-28页

作者： Gomez, Aidan N. Key, Oscar Perlin, Kuba Gou, Stephen Frosst, Nick Dean, Jeff Gal, Yarin Univ Oxford Oxford England Cohere Toronto ON Canada Google Mountain View CA 94043 USA

The number of parameters in state of the art neural networks has drastically increased in recent years. This surge of interest in large scale neural networks has motivated the development of new distributed training strategies enabling such models. One such strategy is model-parallel distributed training. Unfortunately, model-parallelism can suffer from poor resource utilisation, which leads to wasted resources. In this work, we improve upon recent developments in an idealised model-parallel optimisation setting: local learning. Motivated by poor resource utilisation in the global setting and poor task performance in the local setting, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation. These strategies preserve many of the compute-efficiency advantages of local optimisation, while recovering much of the task performance achieved by global optimisation. We assess our strategies on both image classification ResNets and Transformer language models, finding that our strategy consistently out-performs local learning in terms of task performance, and out-performs global learning in training efficiency.

关键词： Model parallelism distributed Optimisation Large-scale Modelling parallel distributed processing Efficient Training

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What is an Emotion? A Connectionist Perspective

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EMOTION REVIEW 2022年第2期14卷 99-110页

作者： Suri, Gaurav Gross, James J. San Francisco State Univ Dept Psychol San Francisco CA 94132 USA Stanford Univ Dept Psychol Stanford CA USA

Researchers often disagree as to whether emotions are largely consistent across people and over time, or whether they are variable. They also disagree as to whether emotions are initiated by appraisals, or whether they may be initiated in diverse ways. We draw upon parallel-distributed-processing to offer an algorithmic account in which features of an emotion instance are bi-directionally connected to each other via conjunction units. We propose that such indirect connections may be innate as well as learned. These ideas lead to the development of the Interactive Activation and Competition framework for Emotion (IAC-E) which allows us to specify when emotions are consistent and when they are variable, as well as when they are appraisal-led and when they are input-agnostic.

关键词： computational models emotion theories parallel distributed processing neural networks

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Why a Connectionist Perspective on Emotion is Helpful COMMENT

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EMOTION REVIEW 2022年第2期14卷 116-120页

作者： Suri, Gaurav Gross, James J. San Francisco State Univ Dept Psychol San Francisco CA 94132 USA Stanford Univ Dept Psychol Stanford CA 94305 USA

To make progress related to long-standing questions related to the nature of emotion, we offer the Interactive Activation and Competition framework for Emotion (IAC-E). The IAC-E is not another conventional theory of emotion. Rather, it offers a neural-network-based, algorithmic account of how emotion instances and categories arise. Our approach suggests that there need not be a contradiction between instances of the same emotion being sometimes consistent and sometimes variable. Similarly, there need not be a contradiction between observations of homogeneity (common in the basic emotion approach) and heterogeneity (common in the constructed emotion approach) within emotion categories

关键词： emotion emotion categories neural networks connectionism parallel distributed processing

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Interlocking backpropagation: improving depthwise model-parallelism

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 7714-7741页

作者： Aidan N. Gomez Oscar Key Kuba Perlin Stephen Gou Nick Frosst Jeff Dean Yarin Gal University of Oxford & Cohere University of Oxford Cohere Google

The number of parameters in state of the art neural networks has drastically increased in recent years. This surge of interest in large scale neural networks has motivated the development of new distributed training strategies enabling such models. One such strategy is model-parallel distributed training. Unfortunately, model-parallelism can suffer from poor resource utilisation, which leads to wasted resources. In this work, we improve upon recent developments in an idealised model-parallel optimisation setting: local learning. Motivated by poor resource utilisation in the global setting and poor task performance in the local setting, we introduce a class of intermediary strategies between local and global learning referred to as interlocking backpropagation. These strategies preserve many of the compute-efficiency advantages of local optimisation, while recovering much of the task performance achieved by global optimisation. We assess our strategies on both image classification ResNets and Transformer language models, finding that our strategy consistently outperforms local learning in terms of task performance, and out-performs global learning in training efficiency.

关键词： model parallelism distributed optimisation large-scale modelling parallel distributed processing efficient training

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A neurally plausible parallel distributed processing model of Event-Related Potential word reading data

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BRAIN AND LANGUAGE 2012年第3期120卷 271-281页

作者： Laszlo, Sarah Plaut, David C. SUNY Binghamton Dept Psychol Binghamton NY 13902 USA Carnegie Mellon Univ Dept Psychol Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA

The parallel distributed processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision-a benchmark task for reading models. Simulations reveal that the model's success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Computational modeling parallel distributed processing Event-Related Potentials N400 Visual word recognition

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Cognition and emotion: on paradigms and metaphors

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COGNITION & EMOTION 2019年第1期33卷 85-93页

作者： Wentura, Dirk Saarland Univ Dept Psychol Fac Behav Sci Saarbrucken Germany

The field of cognition and emotion is characterised as the cognitive psychology of evaluative and affective processes. The most important development in this field is the fruitful adoption of cognitive psychology paradigms to study automatic evaluation processes, for example. This has led to a plethora of findings and theories. Two points are emphasised: First, the (often metaphorical) theoretical way of thinking has changed over the decades. Theorising with symbolic models (e.g. semantic networks), which was prevalent in earlier years, has been replaced more recently by subsymbolic models (i.e. PDP models). It is argued that - despite their still metaphorical character - the latter are better suited to capturing characteristics of emotional processes. Second, the field has adopted the methods of experimental cognitive psychology to develop and refine paradigms as windows to the mind.

关键词： Cognition emotion automatic evaluation parallel distributed processing

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LogDrive: a proactive data collection and analysis framework for time-traveling forensic investigation in IaaS cloud environments

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2018年第1期7卷 1-25页

作者： Hirano, Manabu Tsuzuki, Natsuki Ikeda, Seishiro Kobayashi, Ryotaro Toyota Coll Natl Inst Technol 2-1 Eisei Toyota 4718525 Japan Kogakuin Univ Shinjuku Ku 1-24-2 Nishi Shinjuku Tokyo 1638677 Japan

This paper presents the LogDrive framework for mitigating the following problems of storage forensics in Infrastructure-as-a-Service (IaaS) cloud environments: volatility, increasing volume of forensic data, and anti-forensic attacks that hide traces of incidents in virtual machines. The proposed proactive data collection function of virtual block devices mitigates the problem of volatility within the cloud environments and enables a time-traveling investigation to reveal overwritten or deleted evidence files. We employ a sector-hash-based file detection method with random sampling to search for an evidence file in the record of the write logs of the virtual storage. The problem formulation, the investigation context, and the design with five algorithms are presented. We explore the performance of LogDrive through a detailed evaluation. Finally, security analysis of LogDrive is presented based on the STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privilege) threats model and related work. We posted the source code of LogDrive on GitHub.

关键词： Cloud forensics Surveillance Anti-forensics Hypervisor Virtual machine monitor parallel distributed processing

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