Structural Constraints on Information Content in Human Brain States

Overview

Structural Constraints on Information Content in Human Brain States

Code accompanying the paper "The information content of brain states is explained by structural constraints on state energetic"

Necessary files

  • Diffusion connectivity matrices
  • Preprocessed and parcellated fMRI timeseries

Software requirements

  • python 3.7 (not tested with other versions)
  • packages: numpy, nibabel, matplotlib, seaborn, scipy, nilearn, statsmodels, sklearn, pandas, neurosynth
  • MATLAB R2020b (not tested with other versions)
  • MATLAB Engine API for Python

Settings

In the file settings.py, the paths to find the necessary data and where to write the results need to be specified. All other settings (e.g. time horizon, normalization factor used in the network control theory framework) can also be changed here.

With the following files, information content, minimum control energy, and average controllability is calculated for all subjects/tasks/states

  • run_ic.py (for Information content)
  • run_ce.py (for Minimum control energy)
  • run_ctrbty.py (for Average controllability)

After calculation the necessary results, the Figures in the manuscript can be reproduced using the following files

  • fig1.py
  • analyzeSTATEdist.py (Fig. 2)
  • visualize_I_parcel.py (Fig. 3)
  • analysis_energetics.py (Fig. 4)

Results without figures were obtained with the following scripts

  • ctrbty_pred_err.py (Result section D)
  • relation_MCE_IC_SED.py (Appendix)
Owner
Leon Weninger
Leon Weninger
PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

Sangchun Ha 24 Nov 24, 2022
Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

NeuralTextures This is repository with inference code for paper "StylePeople: A Generative Model of Fullbody Human Avatars" (CVPR21). This code is for

Visual Understanding Lab @ Samsung AI Center Moscow 18 Oct 06, 2022
Official implementation of CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21

CATs: Cost Aggregation Transformers for Visual Correspondence NeurIPS'21 For more information, check out the paper on [arXiv]. Training with different

Sunghwan Hong 120 Jan 04, 2023
ViDT: An Efficient and Effective Fully Transformer-based Object Detector

ViDT: An Efficient and Effective Fully Transformer-based Object Detector by Hwanjun Song1, Deqing Sun2, Sanghyuk Chun1, Varun Jampani2, Dongyoon Han1,

NAVER AI 262 Dec 27, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
An open-access benchmark and toolbox for electricity price forecasting

epftoolbox The epftoolbox is the first open-access library for driving research in electricity price forecasting. Its main goal is to make available a

97 Dec 05, 2022
Potato Disease Classification - Training, Rest APIs, and Frontend to test.

Potato Disease Classification Setup for Python: Install Python (Setup instructions) Install Python packages pip3 install -r training/requirements.txt

codebasics 95 Dec 21, 2022
The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation.

Feedback Convolutional Neural Network for Visual Localization and Segmentation The code is an implementation of Feedback Convolutional Neural Network

19 Dec 04, 2022
Implementation of ConvMixer-Patches Are All You Need? in TensorFlow and Keras

Patches Are All You Need? - ConvMixer ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in t

Sayan Nath 8 Oct 03, 2022
TensorFlow implementation of ENet

TensorFlow-ENet TensorFlow implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. This model was tested on th

Kwotsin 255 Oct 17, 2022
Banglore House Prediction Using Flask Server (Python)

Banglore House Prediction Using Flask Server (Python) 🌐 Links 🌐 📂 Repo In this repository, I've implemented a Machine Learning-based Bangalore Hous

Dhyan Shah 1 Jan 24, 2022
Implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

YOLOv4-large This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork. YOLOv4-CSP YOLOv4-tiny YOLOv4-

Kin-Yiu, Wong 2k Jan 02, 2023
Companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsura et al.

META-RS This is the companion code for the paper "Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks" by Yatsu

Bosch Research 7 Dec 09, 2022
gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks.

gym-anm is a framework for designing reinforcement learning (RL) environments that model Active Network Management (ANM) tasks in electricity distribution networks. It is built on top of the OpenAI G

Robin Henry 99 Dec 12, 2022
Official implementation for Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos

Multi-modal Interaction Graph Convolutioal Network for Temporal Language Localization in Videos Official implementation for Multi-Modal Interaction Gr

Zongmeng Zhang 15 Oct 18, 2022
This repo contains the official code of our work SAM-SLR which won the CVPR 2021 Challenge on Large Scale Signer Independent Isolated Sign Language Recognition.

Skeleton Aware Multi-modal Sign Language Recognition By Songyao Jiang, Bin Sun, Lichen Wang, Yue Bai, Kunpeng Li and Yun Fu. Smile Lab @ Northeastern

Isen (Songyao Jiang) 128 Dec 08, 2022
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022
TensorFlow implementation of "Learning from Simulated and Unsupervised Images through Adversarial Training"

Simulated+Unsupervised (S+U) Learning in TensorFlow TensorFlow implementation of Learning from Simulated and Unsupervised Images through Adversarial T

Taehoon Kim 569 Dec 29, 2022
Neurolab is a simple and powerful Neural Network Library for Python

Neurolab Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework

152 Dec 06, 2022
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

Xinyi Wang 21 May 18, 2022