Resting state brain networks derived from spatial ICA - an individual case

Table of Contents: Architecture Overview ConvNet Layers Convolutional Layer Pooling Layer Normalization Layer Fully-Connected Layer Converting Fully-Connected Layers to Convolutional Layers ConvNet Architectures Layer Patterns Layer Sizing Patterns C

Organization, development and function of complex brain networks The Brain as a Complex System: Using Network Science as a Tool for Understanding the Brain Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations.

其实本设计模式可以实现一些像人工智能式的问答. 这个跟人回答问题差不多,根据不同的问题作出不同的回答,而且在不同的状态,也会作出不同的回答. 如果有大量的数据,那么就可以作出千变万化的问答效果来哦. #include <stdio.h> enum STATE { HUNGARY, EAT, FULL }; class State { STATE myState; public: State(STATE s = HUNGARY) : myState(s) { } void eat() { swi

原文粘贴,仅为查找方便 源地址:http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/DualRegression Research Overview A common need for analyses such as ICA for resting-state FMRI is to run a group-average ICA, and then, for each subject, estimate a "version" of each of the g

This article come from HEREARS-L1: Learning Tuesday 10:30–12:30; Oral Session; Room: Leonard de Vinci 10:30  ARS-L1.1—GROUP STRUCTURED DIRTY DICTIONARY LEARNING FOR CLASSIFICATION Yuanming Suo, Minh Dao, Trac Tran, Johns Hopkins University, USA; Hojj

第六章 基于fMRI生理噪声抑制方法知识的应用研究 6.1 引言 生理噪声抑制研究的应用可分为两个方向,纵向研究应用和横向研究应用.将生理噪声抑制操作融入fMRI信号分析的预处理环节中,属于纵向研究应用.比如,Kelley等人[74]利用python编程语言编写的生理噪声处理工具包PhysioNoise:牛津大学功能磁共振研究中心推出的FSL(FMRIB Software Library)软件包[75]融入了生理噪声抑制预处理组件PNM(Physiological Noise Modelling

第三章 基于CCA的fMRI信号生理噪声抑制方法 3.1 引言 典型相关分析作为一种多元变量相关分析方法,可以用来提取出自相关的信号子空间,因而被广泛地用来做激活信号的提取及噪声成分的估计[48][55].基于CCA,Churchill等人[10]提出了对fMRI残差数据做成分分解,进而估计出具有自相关特性的生理噪声成分,并在真实数据集上取得了较为显著的噪声抑制效果.但该方法需要先知道实验的刺激范式作为先验知识,然后去除fMRI信号中刺激范式相关的成分以得到残差数据.这里对功能信号与噪声信号进行

Applied Deep Learning Resources A collection of research articles, blog posts, slides and code snippets about deep learning in applied settings. Including trained models and simple methods that can be used out of the box. Mainly focusing on Convoluti

[ML] My Journal from Neural Network to Deep Learning: A Brief Introduction to Deep Learning. Part. Eight Dropout & Maxout This is the 8th post of a series of posts I planned about a journal of myself studying deep learning in Professor Bhiksha Raj's