Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank

نویسندگان

  • Tayfun Gürel
  • Luc De Raedt
  • Stefan Rotter
چکیده

1 Bernstein Center for Computational Neuroscience, Freiburg, Germany 2 Institute for Computer Science, Albert Ludwigs University of Freiburg, Germany 3 Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany 4 Department of Computer Science, Katholieke Universiteit Leuven, Belgium [email protected], [email protected], [email protected]

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Ranking neurons for mining structure-activity relations in biological neural networks: NeuronRank

It is difficult to relate the structure of a cortical neural network to its dynamic activity analytically. Therefore we employ machine learning and data mining algorithms to learn these relations from sample random recurrent cortical networks and corresponding simulations. Inspired by the PageRank and the Hubs&Authorities algorithms, we introduce the NeuronRank algorithm, which assigns a source...

متن کامل

Predicting peak particle velocity by artificial neural networks and multivariate regression analysis - Sarcheshmeh copper mine, Kerman, Iran

Ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. Controlling vibration caused by blasting can be achieved once peak particle velocity (PPV) is predicted. In this paper, the values of P...

متن کامل

Modeling of Texture and Color Froth Characteristics for Evaluation of Flotation Performance in Sarcheshmeh Copper Pilot Plant, Using Image Analysis and Neural Networks

Texture and color appearance of froth is a discreet qualitative tool for evaluating the performance of flotation process. The structure of a froth developed on the flotation cell has a significant effect on the grade and recovery of copper concentrate. In this work, image analysis and neural networks have been implemented to model and control the performance of such a system. The result reveals...

متن کامل

Yarn tenacity modeling using artificial neural networks and development of a decision support system based on genetic algorithms

Yarn tenacity is one of the most important properties in yarn production. This paper addresses modeling of yarn tenacity as well as optimally determining the amounts of the effective inputs to produce yarn with desired tenacity. The artificial neural network is used as a suitable structure for tenacity modeling of cotton yarn with 30 Ne. As the first step for modeling, the empirical data is col...

متن کامل

Evaluation of effects of operating parameters on combustible material recovery in coking coal flotation process using artificial neural networks

In this research work, the effects of flotation parameters on coking coal flotation combustible material recovery (CMR) were studied by the artificial neural networks (ANNs) method. The input parameters of the network were the pulp solid weight content, pH, collector dosage, frother dosage, conditioning time, flotation retention time, feed ash content, and rotor rotation speed. In order to sele...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007