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 propertie...
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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
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 indi...
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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 parallelprocessing 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.
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. How...
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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-distributedprocessing (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.
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 s...
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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-paralleldistributed 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.
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 the...
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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.
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 ...
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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
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 s...
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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-paralleldistributed 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.
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...
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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.
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 para...
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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.
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-...
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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.
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