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Biophysically Interpretable Recurren...

Wang, Yuan.

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Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis./
作者:	Wang, Yuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	108 p.
附註:	Source: Masters Abstracts International, Volume: 79-12.
Contained By:	Masters Abstracts International79-12.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10816127
ISBN:	9780355992205

Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis.
Wang, Yuan.

Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 108 p.

Source: Masters Abstracts International, Volume: 79-12.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2018.

This item must not be sold to any third party vendors.

Recently, state-of-the-art Recurrent Neural Networks (RNNs) have been used to expand people's knowledge of neuroscience. However, these generic RNNs lack biophysical meaning, making the interpretation of results in a neuroscience context difficult. In this study, we propose a new biophysically plausible RNN built on Dynamic Causal Modelling (DCM). DCM is a nonlinear generative model explicitly describing the entire process from stimulus to functional magnetic resonance image (fMRI) blood oxygen level dependent (BOLD) signal via neural activity and cerebral hemodynamics, and thus is considered by many to be the most biologically plausible as well as the most technically advanced fMRI modeling method. We propose a generalization of RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. We demonstrate that the maximum a posteriori (MAP) estimation of DCM parameters can be found with back propagation, a novel form of DCM model inversion. DCM-RNN combines biophysical interpretability and neural network compatibility, making it a versatile tool for neuroscience. A combination of DCM-RNN and other deep neural networks enables one to study human brains while subjects perform complex tasks such as reading and watching movies. DCM is traditionally used to estimate the brain effective connectivity, the coupling strength between brain regions, and the brain causal architecture. In experiments of inferring the effective connectivity, back propagation used in DCM-RNN was shown to be more noise robust than the variational Gauss-Newton search traditionally used for DCM parameter estimation. In experiments of model discovering, DCM-RNN, equipped with a l1 prior, was shown to be better at identifying parsimonious causal architecture than the traditional DCM parameter estimation method. We also evaluated the incorporation of a neuron firing model in the DCM-RNN framework. Studies with complex tasks are in our further plan.

ISBN: 9780355992205Subjects--Topical Terms:

649834
Electrical engineering.

Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis.
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520 $a Recently, state-of-the-art Recurrent Neural Networks (RNNs) have been used to expand people's knowledge of neuroscience. However, these generic RNNs lack biophysical meaning, making the interpretation of results in a neuroscience context difficult. In this study, we propose a new biophysically plausible RNN built on Dynamic Causal Modelling (DCM). DCM is a nonlinear generative model explicitly describing the entire process from stimulus to functional magnetic resonance image (fMRI) blood oxygen level dependent (BOLD) signal via neural activity and cerebral hemodynamics, and thus is considered by many to be the most biologically plausible as well as the most technically advanced fMRI modeling method. We propose a generalization of RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. We demonstrate that the maximum a posteriori (MAP) estimation of DCM parameters can be found with back propagation, a novel form of DCM model inversion. DCM-RNN combines biophysical interpretability and neural network compatibility, making it a versatile tool for neuroscience. A combination of DCM-RNN and other deep neural networks enables one to study human brains while subjects perform complex tasks such as reading and watching movies. DCM is traditionally used to estimate the brain effective connectivity, the coupling strength between brain regions, and the brain causal architecture. In experiments of inferring the effective connectivity, back propagation used in DCM-RNN was shown to be more noise robust than the variational Gauss-Newton search traditionally used for DCM parameter estimation. In experiments of model discovering, DCM-RNN, equipped with a l1 prior, was shown to be better at identifying parsimonious causal architecture than the traditional DCM parameter estimation method. We also evaluated the incorporation of a neuron firing model in the DCM-RNN framework. Studies with complex tasks are in our further plan.
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