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1	A Biologically Plausible Learning Rule for the Infomax on Recurrent Neural Networks. / 生物学的に想定しうるリカレント結合神経回路上の情報量最大化学習則 Hayakawa, Takashi 23 March 2015 (has links) 京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18874号 / 医博第3985号 / 新制\|\|医\|\|1008(附属図書館) / 31825 / 京都大学大学院医学研究科医学専攻 / (主査)教授渡邉大, 教授山田亮, 教授福山秀直 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM Information Maximization Cortical Microcircuits Neuronal Plasticity Neuronal Avalanche Precise Firing Sequence Orientation Selectivity 490
2	General-purpose optimization through information maximization Lockett, Alan Justin 05 July 2012 (has links) The primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized problem-specific inputs. This dissertation suggests that such machines are realistic: If No Free Lunch theorems were to apply to all real-world problems, then the world would be utterly unpredictable. In response, the dissertation proposes the information-maximization principle, which claims that the optimal optimization methods make the best use of the information available to them. This principle results in a new algorithm, evolutionary annealing, which is shown to perform well especially in challenging problems with irregular structure. / text Optimization General-purpose learning Martingale optimization Artificial intelligence Evolutionary computation Genetic algorithms Simulated annealing Evolutionary annealing Neuroannealing Neural networks Neural network controllers Neuroevolution Differential evolution No Free Lunch theorems NFL Identification Theorem Population-based stochastic optimization Iterative optimization Optimal optimization Information-maximization principle Convex control Algorithm selection

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A Biologically Plausible Learning Rule for the Infomax on Recurrent Neural Networks. / 生物学的に想定しうるリカレント結合神経回路上の情報量最大化学習則

General-purpose optimization through information maximization