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Signal transcoding by nonlinear sensory neurons: Information-entropy maximization, optimal transfer function, and anti-Hebbian adaptation

F. CHAPEAU-BLONDEAU, F. RAGUIN
1997 Mathematical Medicine and Biology  
Secondly, we derive simple laws which adaptively adjust modifiable parameters of a neuron toward maximum entropy.  ...  The present results highlight the usefulness of general information-theoretic principles in contributing to the understanding of neural systems and their remarkable performances for information processing  ...  = -0.015 almost realizes the maximum entropy. (• • •) The theoretical realization of the exact maximum entropy H(Y) = -0.013 achievable with an arbitrary Gaussian input X and a logistic sigmoidal transfer  ... 
doi:10.1093/imammb/14.3.227 fatcat:dzw2v6ytbnco3bpsdtfgvhbdq4

Signal transcoding by nonlinear sensory neurons: information-entropy maximization, optimal transfer function, and anti-Hebbian adaptation

F Chapeau-Blondeau
1997 Mathematical Medicine and Biology  
Secondly, we derive simple laws which adaptively adjust modifiable parameters of a neuron toward maximum entropy.  ...  The present results highlight the usefulness of general information-theoretic principles in contributing to the understanding of neural systems and their remarkable performances for information processing  ...  = -0.015 almost realizes the maximum entropy. (• • •) The theoretical realization of the exact maximum entropy H(Y) = -0.013 achievable with an arbitrary Gaussian input X and a logistic sigmoidal transfer  ... 
doi:10.1093/imammb14.3.227 fatcat:e6lqn3up2rcmzombpsrocojgyy

Page 626 of Hydrological Processes Vol. 11, Issue 6 [page]

1997 Hydrological Processes  
The choice of extremal models by Akaike’s information criterion’, J. Hydrol., 82, 307-315. Ulrych, T. and Clayton, R. W. 1976. ‘Time series modeling and maximum entropy’, Phys. Earth Planet.  ...  ‘Parameter estimation for 3-parameter generalized Pareto distribution by the principle of maximum entropy (POME)’, Hydrol. Sci. J., 4, 165-181. Singh, V. P. and Guo, H. 1995c.  ... 

Maximisation Principles and Daisyworld [article]

G.J.Ackland
2003 arXiv   pre-print
We find that this is sufficient to invalidate the MEP principle, finding instead a different principle, that the system self-organises to a state which maximises the amount of life.  ...  Unlike physical systems where a principle of maximum energy production has been observed, Daisyworld follows evolutionary dynamics rather than Hamiltonian dynamics.  ...  GAIA AND MEP IN THE LOGISTIC MAP The logistic map is another widely used model for population growth (Hassell, Lawton and May, 1976) .  ... 
arXiv:cond-mat/0307567v1 fatcat:czcod753sbfsrogr7kbztbshxi

Semi-supervised Learning by Entropy Minimization

Yves Grandvalet, Yoshua Bengio
2004 Neural Information Processing Systems  
The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the "cluster  ...  The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model.  ...  Test errors of minimum entropy logistic regression (•) and mixture models (+).  ... 
dblp:conf/nips/GrandvaletB04 fatcat:bsycp7up6vafndkfbgrjyrvv7y

An improved logistic probability prediction model for water shortage risk in situations with insufficient data

Longxia Qian, Ren Zhang, Chengzu Bai, Yangjun Wang, Hongrui Wang
2018 NHESSD  
Then, the logistic regression model was used to describe the relation between water shortage risk and its factors, and an alternative method of parameter estimation (maximum entropy estimation) was proposed  ...  This study developed an improved logistic probability prediction model for water shortage risk in situations when there is insufficient data.  ...  CC BY 4.0 License. entropy principle. The new method is named after maximum entropy estimation.  ... 
doi:10.5194/nhess-2018-56 fatcat:n2upyhm2rfatjordhguvqpsqoq

Maximum entropy principle in recurrence plot analysis on stochastic and chaotic systems

T. L. Prado, G. Corso, G. Z. dos Santos Lima, R. C. Budzinski, B. R. R. Boaretto, F. A. S. Ferrari, E. E. N. Macau, S. R. Lopes
2020 Chaos  
In this work, we study the recently developed entropy of recurrence microstates. We propose a new quantifier, the maximum entropy (Smax).  ...  We apply Smax and the optimum ϵ to deterministic and stochastic systems.  ...  Corso also gratefully acknowledges support from Shell Brasil through the project "New methods for Full Wave Inversion" at Programa de Pós Graduação em Ciências e Engenharia do Petróleo and the strategic  ... 
doi:10.1063/1.5125921 pmid:32357677 fatcat:gjslqqqusjgblharfx2lz2jksi

Maximum entropy, logistic regression, and species abundance

Fangliang He
2010 Oikos  
Their identical formalisms facilitate the interpretation of logistic regression models with statistical mechanics, and reveal several limitations of the maximum entropy formalism.  ...  Although the Boltzmann distribution and the logistic regression models represent two fundamentally different approaches, the two models have an identical mathematical form.  ...  Acknowledgements -I thank Tommaso Zillio and Salvador Pueyo for discussions that helped me understand the maximum entropy formalism.  ... 
doi:10.1111/j.1600-0706.2009.17113.x fatcat:hstwatuhdbfj5ansiyzukzpnsu

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

Ge Cheng, Zhenyu Zhang, Moses Kyebambe, Nasser Kimbugwe
2016 Entropy  
In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits  ...  to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e18120450 fatcat:ohrshxeyivf5ng6l3tcynhc65u

A Maximum Entropy Procedure to Solve Likelihood Equations [article]

Antonio Calcagnì, Livio Finos, Gianmarco Altoè, Massimiliano Pastore
2019 arXiv   pre-print
Similarly, when maximum-likelihood estimation is difficult, as for the case of logistic regression under separation, the maximum entropy proposal achieved results (numerically) comparable to those obtained  ...  We assessed our proposal by means of empirical case studies and a simulation study, this latter involving the most critical case of logistic regression under data separation.  ...  Maximum entropy based methods have a long history in statistical modeling and inference (e.g., for a recent review see [20] ).  ... 
arXiv:1904.09734v2 fatcat:76vngrdi3vbddndm67mnspaghm

An Equivalent of XOR Operator and Its Application in One-Dimensional CA Model

Youssef Khmou
2018 International Journal of Control and Automation  
the logarithmic logistic map which is a non linear model of discrete dynamical system, numerical results illustrate the equivalence of the variations between both functions.  ...  In the second part, we study the variation of the proposed metric and the information entropy with respect to the repartition of the binary sequence that is generated using an optimal transformation of  ...  At this point, the functions reach their maximum values   쟫 og 2 and 1for h and  respectively.  ... 
doi:10.14257/ijca.2018.11.2.15 fatcat:7xbvyilngbbf5olq6ypye6u6rq

Tikhonov Kullback Leibler Vuong Logistic Machine Learning Classifier for Early Disease Diagnosis Over Big Data

2022 TAGA Journal  
Second, the feature extraction model is based on Entropy Tikhonov Regularization and is applied to the pre-processed features to identify the features pertinent to seizures.  ...  To address these issues, in this work, a method using machine learning technique called, Polynomial Tikhonov Entropy and Kullback Vuong Logistic Classifier (PTE-KVLC) is presented.  ...  As mentioned, the non-extensive entropy includes as a particular case the extensive entropy (q = 1): where the maximum entropy principle can be used as a regularization method.  ... 
doi:10.37896/pd91.4/91443 fatcat:vofslbrc2ne7bhstr3phuuxqde

Understanding Deep Learning Generalization by Maximum Entropy [article]

Guanhua Zheng, Jitao Sang, Changsheng Xu
2017 arXiv   pre-print
The connection between DNN and maximum entropy well explains why typical designs such as shortcut and regularization improves model generalization, and provides instructions for future model development  ...  DNN is then regarded as approximating the feature conditions with multilayer feature learning, and proved to be a recursive solution towards maximum entropy principle.  ...  Imposing extra data hypothesis actually violates the ME principle and degrades the model to non-ME (Maximum Entropy) model.  ... 
arXiv:1711.07758v1 fatcat:zchz4vgjyvaubi4fi4vsjyp27e

Route entropy based capacity reliability assessment and application in multi-objective satisfactory optimization of logistics network
English

Miao Xin, Xi Bao, Guan Ming, Tang Yan hong
2011 Scientific Research and Essays  
The probability space and calculative model of route entropy were introduced as a new method with regard to the field of capacity reliability research in transportation science.  ...  This paper built assessment model of capacity reliability of road network from the perspective of route entropy by referring to relevant concepts and methods applied in water distribution system.  ...  ACKNOWLEDGEMENTS This work is supported by Humanities and Social Science  ... 
doi:10.5897/sre10.1013 fatcat:y42qolfd6vcuhm36ob5744elmu

Application of a Maximum Entropy Model for Mineral Prospectivity Maps

Binbin Li, Bingli Liu, Ke Guo, Cheng Li, Bin Wang
2019 Minerals  
In the present study, based on the maximum entropy principle, a maximum entropy model (MaxEnt model) was established to predict the potential distribution of copper deposits by integrating 43 ore-controlling  ...  Kappa = 0.5 and maximum TSS = 0.6).  ...  This standard is known as the maximum entropy principle. The MaxEnt model based on the principle of maximum entropy originates from statistical mechanics and is a log-linear model [71] .  ... 
doi:10.3390/min9090556 fatcat:oikvfeybpzgvzkvbem373yx4yu
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