A Hybrid Electro - Diffusion Model for Neural Signaling

نویسندگان

  • C. L. Lopreore
  • T. M. Bartol
  • T. J. Sejnowski
چکیده

View A HYBRID ELECTRO-DIFFUSION MODEL FOR NEURAL SIGNALING. A new method is introduced for modeling the three-dimensional movement of ions in neurons. Using the Nernst-Planck equation, concentration gradients and electric fields were evaluated using a weighted moving least-squares algorithm. We incorporate this method into MCell, a Monte-Carlo cell simulator, and present preliminary validation under several testing scenarios. We apply the method to a reactive-diffusive simulation of an action potential propagating through an unmyelinated axon, with discrete sodium and potassium channels modeled by a voltage-dependent Markov random process. For large diameter axons, the spatio-temporal dynamics of the membrane potential averaged over several runs converges to results obtained from the Hodgkin and Huxley model implemented in NEURON. The results also corroborate previous stochastic simulations by other workers demonstrating that at thin diameters channel noise is sufficient to induce action potentials*.

برای دانلود رایگان 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

A hybrid model for estimating the probability of default of corporate customers

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verific...

متن کامل

Model for Thermal Conductivity of Nanofluids Using a General Hybrid GMDH Neural Network Technique

In this study, a model for estimating the NFs thermal conductivity by using a GMDH-PNN has been investigated. NFs thermal conductivity was modeled as a function of the nanoparticle size, temperature, nanoparticle volume fraction and the thermal conductivity of the base fluid and nanoparticles. For this purpose, the developed network contains 8 layers with 2 inputs in each layer and also tra...

متن کامل

AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

متن کامل

Hybrid Neural Network-Based Fault Diagnosis and Fault-Tolerance De- sign with Application in Electro-Hydraulic Servovalve

In this study, to cope with the needs of the predictive maintenance for complex systems, a hybrid dynamic Artificial Neural Network (ANN) based fault and degradation diagnosis and tolerance method is designed. The multi-layer feed forward ANN and recurrent ANN are combined, so as to be able to form a dynamic identification model for the nonlinear time-varying system. It has three work modes, an...

متن کامل

Evaluation of the Neuro-Fuzzy and Hybrid Wavelet-Neural Models Efficiency in River Flow Forecasting (Case Study: Mohmmad Abad Watershed)

  One of the most important issues in watersheds management is rainfall-runoff hydrological process forecasting. Using new models in this field can contribute to proper management and planning. In addition, river flow forecasting, especially in flood conditions, will allow authorities to reduce the risk of flood damage. Considering the importance of river flow forecasting in water resources ma...

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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2002