Nonlinear System Identification of Neural Systems from Neurophysiological Signals

نویسندگان
چکیده

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

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nonlinear multivariate analysis of neurophysiological signals.

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used i...

متن کامل

Information Retrieval from Neurophysiological Signals

One of the ultimate goals of neuroscience is decoding someone’s intentions directly from his/her brain activities. In this thesis, we aim at pursuing this goal in different scenarios. Firstly, we show the possibility of creating a user-centric music/movie recommender system by employing neurophysiological signals. Regarding this, we employed a brain decoding paradigm in order to classify the fe...

متن کامل

Nonlinear System Identification Using Neural Network

Magneto-rheological damper is a nonlinear system. In this case study, system has been identified using Neural Network tool. Optimization between number of neurons in the hidden layer and number of epochs has been achieved and discussed by using multilayer perceptron Neural Network.

متن کامل

Nonlinear System Identification Using Spatiotemporal Neural Networks

The so-called spatiotemporal neural network is considered. This is a neural network where the conventional weight multiplication operation is replaced by a linear filtering operation [l]. A training algorithm is derived for such networks. The problem of nonlinear system identification is considered as an application for spatiotemporal networks. Nonlinear system identification is one of the chal...

متن کامل

Nonlinear Characteristics of Neural Signals

The study is devoted to definition of generalized metrical and topological (informational entropy) characteristics of neural signals via their well-known theoretical models. We have shown that time dependence of action potential of neurons is scale invariant. Information and entropy of neural signals have constant values in case of self-similarity and self-affinity.

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

ژورنال

عنوان ژورنال: Neuroscience

سال: 2021

ISSN: 0306-4522

DOI: 10.1016/j.neuroscience.2020.12.001