The Experts below are selected from a list of 445878 Experts worldwide ranked by ideXlab platform
Robert Legenstein - One of the best experts on this subject based on the ideXlab platform.
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a model for Structured Information representation in neural networks of the brain
eNeuro, 2020Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans can reason at an abstract level and structure Information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural Information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain. Significance Statement High-level cognition in the human brain necessitates dynamically changing Structured representations of Information. There exists experimental evidence that in cortex, sensory content is enriched with structural Information using a factorized code. We introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic neural networks. Assembly projections provide the basis for explaining a number of experimental findings. In addition, the model is capable of performing elementary cognitive processing operations, thus extending the computational capabilities of neural network models in the direction of cognitive symbolic computations.
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a model for Structured Information representation in neural networks of the brain
eNeuro, 2020Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans can reason at an abstract level and structure Information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural Information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain.
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a model for Structured Information representation in neural networks
arXiv: Neurons and Cognition, 2016Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans possess the capability to reason at an abstract level and to structure Information into abstract categories, but the underlying neural processes have remained unknown. Experimental evidence has recently emerged for the organization of an important aspect of abstract reasoning: for assigning words to semantic roles in a sentence, such as agent (or subject) and patient (or object). Using minimal assumptions, we show how such a binding of words to semantic roles emerges in a generic spiking neural network through Hebbian plasticity. The resulting model is consistent with the experimental data and enables new computational functionalities such as Structured Information retrieval, copying data, and comparisons. It thus provides a basis for the implementation of more demanding cognitive computations by networks of spiking neurons.
Michael Brundage - One of the best experts on this subject based on the ideXlab platform.
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the impact of explicit values clarification exercises in a patient decision aid emerges after the decision is actually made evidence from a randomized controlled trial
Medical Decision Making, 2012Co-Authors: Deb Feldmanstewart, Christine Tong, Rob Siemens, Shabbir M H Alibhai, Tom Pickles, John W Robinson, Michael BrundageAbstract:Purpose. To determine if particular values clarification exercises included in a patient decision aid had discernible impact on postdecisional regret in patients with early-stage prostate cancer. Methods. A multicenter randomized controlled trial compared 2 versions of a computerized patient decision aid: only Structured Information compared to the Structured Information plus values clarification exercises. Assessments were conducted during the decision aid visit; telephone follow-up interviews were conducted when patients made their decisions with their physician, 3 months after completing treatment, and .1 year later (per a mailing). Outcome measures included the Decisional Conflict Scale, the Preparation for Decision Making Scale, and the Decision Regret Scale. Results. A total of 156 patients participated, 75 provided Information only and 81 provided Information plus values clarification exercises. The groups did not differ significantly on any outcome evaluated at the decision aid visit; in both groups, decisional conflict decreased immediately after using the decision aid. Between-group differences emerged after the decision was actually made. The values clarification exercises group reported higher Preparation for Decision Making Scale scores at the decision follow-up and at the .1-year follow-up. Regret did not differ significantly between groups at the 3-month follow-up but was lower for the values clarification exercises group than for the Information group at the .1year follow-up. Conclusion. The results suggest that the values clarification exercises led to better preparation for decision making and to less regret. The impact, however, only emerged after the decision was made. Key words: patient decision aid; judgment; decision making. (Med Decis Making 2012;32:616–626)
Michael G Muller - One of the best experts on this subject based on the ideXlab platform.
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a model for Structured Information representation in neural networks of the brain
eNeuro, 2020Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans can reason at an abstract level and structure Information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural Information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain. Significance Statement High-level cognition in the human brain necessitates dynamically changing Structured representations of Information. There exists experimental evidence that in cortex, sensory content is enriched with structural Information using a factorized code. We introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic neural networks. Assembly projections provide the basis for explaining a number of experimental findings. In addition, the model is capable of performing elementary cognitive processing operations, thus extending the computational capabilities of neural network models in the direction of cognitive symbolic computations.
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a model for Structured Information representation in neural networks of the brain
eNeuro, 2020Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans can reason at an abstract level and structure Information into abstract categories, but the underlying neural processes have remained unknown. Recent experimental data provide the hint that this is likely to involve specific subareas of the brain from which structural Information can be decoded. Based on this data, we introduce the concept of assembly projections, a general principle for attaching structural Information to content in generic networks of spiking neurons. According to the assembly projections principle, structure-encoding assemblies emerge and are dynamically attached to content representations through Hebbian plasticity mechanisms. This model provides the basis for explaining a number of experimental data and provides a basis for modeling abstract computational operations of the brain.
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a model for Structured Information representation in neural networks
arXiv: Neurons and Cognition, 2016Co-Authors: Michael G Muller, Christos H Papadimitriou, Wolfgang Maass, Robert LegensteinAbstract:Humans possess the capability to reason at an abstract level and to structure Information into abstract categories, but the underlying neural processes have remained unknown. Experimental evidence has recently emerged for the organization of an important aspect of abstract reasoning: for assigning words to semantic roles in a sentence, such as agent (or subject) and patient (or object). Using minimal assumptions, we show how such a binding of words to semantic roles emerges in a generic spiking neural network through Hebbian plasticity. The resulting model is consistent with the experimental data and enables new computational functionalities such as Structured Information retrieval, copying data, and comparisons. It thus provides a basis for the implementation of more demanding cognitive computations by networks of spiking neurons.
Clement J Mcdonald - One of the best experts on this subject based on the ideXlab platform.
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extracting Structured Information from free text pathology reports
American Medical Informatics Association Annual Symposium, 2003Co-Authors: Gunther Schadow, Clement J McdonaldAbstract:We have developed a method that extracts Structured Information about specimens and their related findings in free-text surgical pathology reports. Our method uses regular expressions that drive a state-automaton on top of XSLT and Java. Text fragments identified are coded against the UMLS®. This paper describes the technical approach and reports on a preliminary evaluation study, designed to guide further development. We found that of 275 reviewed reports, 91% were coded at least so that all specimens and their critical pathologic findings were represented in codes.
Daphne Koller - One of the best experts on this subject based on the ideXlab platform.
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combining Structured and free text data for automatic coding of patient outcomes
American Medical Informatics Association Annual Symposium, 2010Co-Authors: Suchi Saria, Gayle Mcelvain, Anand K Rajani, Anna A Penn, Daphne KollerAbstract:: Integrating easy-to-extract Structured Information such as medication and treatments into current natural language processing based systems can significantly boost coding performance; in this paper, we present a system that rigorously attempts to validate this intuitive idea. Based on recent i2b2 challenge winners, we derive a strong language model baseline that extracts patient outcomes from discharge summaries. Upon incorporating additional clinical cues into this language model, we see a significant boost in performance to F1 of 88.3 and a corresponding reduction in error of 23.52%.
