I will regularly update this section contents [Last updated: Aug, 11 2022]. My current research generally focuses on computational neuroscience, computational neuroimaging, deep learning (NeuroAI), information theory, and image and video processing. Please visit the publication window to check more of my recent works.

The part of fundamental knowledge or background of neuroimage and computational neuroscience.

• Interpreting the BOLD signal
• Computational approaches to fMRI analysis
• Is neuroimaging measuring information in the brain
• Computational Neuroscience: Mathematical and Statistical Perspectives
• Computational Neuroscience
• Interpreting encoding and decoding models
• Perception and Cognition
• History of Neuroscience
• Biological Intelligence and Artificial Intelligence

This sense of Sameness is the very keel and backbone of our thinking ——William James (1890)

Attention is…the taking into possession of the mind, in clear and vivid form, of one out of what seem several simultaneously possible objects or trains of thought ——William James (1890)

We talk of man* being the rational animal; and the traditional intellectualist philosophy has always made a great point of treating the brutes as wholly irrational creatures. Nevertheless, it is by no means easy to decide just what is meant by reason ——William James (1890)

Neuron that fire together wire together ——Donald Olding Hebb (1949)

Codes [Github]

• Nest: is a repository for collecting software of (f)MRI data analysis.  [HTML,CSS,Javascript]

• BioMulti-L-NL Computational Vision Model  [Python,Matlab]

• Our Lab(Computational Vision Neuroscience) Toolbox [Matlab]

• COVID19 Tracker: Real Time Tracker COVID19 Information [HTML,CSS,Javascript]

• Neuroscience-Information-Theory-Python-Toolbox [Python] [Under construction]

• cuRBIG: CUDAMat-Rotation-Based Iterative Gaussianization [Python]

• PairsVideo2tfrecord [Python] [Under construction]

• A Psychophysiologically Oriented Saliency Map Prediction Model [Python,Matlab]

• CSFpy: Human Contrast Sensivity Functions  [Python]

• CSFs in Autoencoder: Contrast Sensitivity Functions in Autoencoders [Python,Matlab]

• Fuzzy Recurrence Eigenvalues of Aging with fMRI [Python,Matlab]

• Functional Connectivity in Visual Areas from Total Correlation [Matlab]

• Functional Connectome of the Human Brain with Total Correlation [Matlab]

Databases

• Images[under construction]

• Videos[under construction]

• Computational Neuroscience Databases[under construction]

• DreamNet:Cross-platform Human Dream Recording Database[under construction]
[The backend will be in C++ (PC) or Java (PC) and Android (Mobile app)]. The project will fuse computational engineering, neuroscience, and AI (NLP, GAN, and so on).
[OLD:DreamBank]

Journals [Google Scholar] [ORCID] [ResearchGate]

• Canonical Retina-to-Cortex Vision Model ready for Automatic Differentiation, Qiang Li, Jesus Malo, Brain Informatics, 2020,329-337. doi:10.1007/978-3-030-59277-6_30.
  [Published] [Abstract] [BibTeX] [URL][Slides] [Code] [Experiment Log] [GoogleColab] [Automatic differentiation] [Companion info] [Companion abstract 1] [Companion abstract 2]
  Abstract: Canonical vision models of the retina-to-V1 cortex pathway consist of cascades of several Linear+Nonlinear layers. In this setting, parameter tuning is the key to obtain a sensible behavior when putting all these multiple layers to work together. Conventional tuning of these neural models very much depends on the explicit computation of the derivatives of the response with regard to the parameters. And, in general, this is not an easy task. Automatic differentiation is a tool developed by the deep learning community to solve similar problems without the need of explicit computation of the analytic derivatives. Therefore, implementations of canonical visual neuroscience models that are ready to be used in an automatic differentiation environment are extremely needed nowadays. In this work we introduce a Python implementation of a standard multi-layer model for the retina-to-V1 pathway. Results show that the proposed default parameters reproduce image distortion psychophysics. More interestingly, given the python implementation, the parameters of this visual model are ready to be optimized with automatic differentiation tools for alternative goals.
  BibTeX:

                                  @InProceedings{springer20,
                                                  author="Li, Qiang and Malo, Jesus",
                                                  title="Canonical Retina-to-Cortex Vision Model Ready for Automatic Differentiation",
                                                  booktitle="Brain Informatics",
                                                  year="2020",
                                                  publisher="Springer International Publishing",
                                                  address="Cham",
                                                  pages="329--337"}
                                  

• Saliency Prediction Based on Multi-Channel Models of Visual Processing, Qiang Li, Machine Vision and Applications 34, 47 (2023). doi:10.1007/s00138-023-01405-2.
  [Published] [Abstract] [BibTeX] [URL] [ArXiv] [Code&Data] [Project Page] [Companion paper]
  Abstract: Visual attention is one of the most significant characteristics for selecting and understanding the outside redundancy world. The human vision system cannot process all information simultaneously due to the visual information bottleneck. In order to reduce the redundant input of visual information, the human visual system mainly focuses on dominant parts of scenes. This is commonly known as visual saliency map prediction. This paper proposed a new psychophysical saliency prediction architecture, WECSF, inspired by multi-channel model of visual cortex functioning in humans. The model consists of opponent color channels, wavelet transform, wavelet energy map, and contrast sensitivity function for extracting low-level image features and providing a maximum approximation to the human visual system. The proposed model is evaluated using several datasets, including the MIT1003, MIT300, TORONTO, SID4VAM, and UCF Sports datasets. We also quantitatively and qualitatively compare the saliency prediction performance with that of other state-of-the-art models. Our model achieved strongly stable and better performance with different metrics on natural images, psychophysical synthetic images and dynamic videos. Additionally, we confirmed that Fourier and spectral-inspired saliency prediction models outperformed other state-of-the-art non- neural network and even deep neural network models on psychophysical synthetic images. It can be explained and supported by the Fourier Vision Hypothesis. In the meantime, we suggest that deep neural networks need specific architectures and goals to be able to predict salient performance on psychophysical synthetic images better and more reliably. Finally, the proposed model could be used as a computational model of primate vision system and help us understand mechanism of primate vision system.
  BibTeX:

                                  @article{nn20,
                      		year = {2023},
                      		month = {05},
                      		title = {Saliency prediction based on multi-channel models of visual processing},
                      		volume = {34},
                      		journal = {Machine Vision and Applications},
                      		doi = {10.1007/s00138-023-01405-2}
                      		}
                                  

• Contrast Sensitivity Functions in Autoencoders, Qiang Li, Alex Gomez-Villa, Marcelo Bertalmio, Jesus Malo, Journal of Vision, 2022, 22(6):8. doi:10.1167/jov.22.6.8.
  [Published] [Abstract] [BibTeX] [ArXiv] [URL] [Local PDF] [Code&Data] [Project Page] [Offline experiment Log notebook] [Online experiment Log notebook] [Companion papers:Deep Neural Networks and Biological Vision]
  Abstract: Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the current trend of using artificial neural networks for studying vision, and (2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatio-temporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic adaptation) or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with an RMSE error of 11% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher-level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science since the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.
  BibTeX:

  				@article{Qiang22,
  				    author = {Li, Qiang and Gomez-Villa, Alex and Bertalmío, Marcelo and Malo, Jesús},
  				    title = "{Contrast sensitivity functions in autoencoders}",
  				    journal = {Journal of Vision},
  				    volume = {22},
  				    number = {6},
  				    pages = {8-8},
  				    year = {2022},
  				    month = {05},
  				    issn = {1534-7362},
  				    doi = {10.1167/jov.22.6.8},
  				    url = {https://doi.org/10.1167/jov.22.6.8}
  				}
                                  

• Bidirected Information Flow in the High-Level Visual Cortex, Qiang Li, Brain Informatics, 2021,57-66. doi:10.1007/978-3-030-86993-9_6.
  [Published] [Abstract] [BibTeX] [URL][Slides] [Certificate] [Context-Tree Weighting Project]
  Abstract: Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.
  BibTeX:

                                  @InProceedings{springer21,
  						author="Li, Qiang",
  						editor="Mahmud, Mufti
  						and Kaiser, M. Shamim
  						and Vassanelli, Stefano
  						and Dai, Qionghai
  						and Zhong, Ning",
  						title="Bidirected Information Flow in the High-Level Visual Cortex",
  						booktitle="Brain Informatics",
  						year="2021",
  						publisher="Springer International Publishing",
  						address="Cham",
  						pages="57--66",
  						isbn="978-3-030-86993-9"}
                                  

• Functional Connectivity Inference from fMRI Data Using Multivariate Information Measures, Qiang Li, Neural Networks, 2022,146:85-97. doi:10.1016/j.neunet.2021.11.016.
  [Published][Abstract] [BibTeX] [URL] [Code] [Companion paper1] [Companion paper2] [Companion paper3]
  Abstract: Shannon's entropy or an extension of Shannon's entropy can be used to quantify information transmission between or among variables. Mutual information is the pair-wise information that captures nonlinear relationships between variables. It is more robust than linear correlation methods. Beyond mutual information, two generalizations are defined for multivariate distributions: interaction information or co-information and total correlation or multi-mutual information. In comparison to mutual information, interaction information and total correlation are underutilized and poorly studied in applied neuroscience research. Quantifying information flow between brain regions is not explicitly explained in neuroscience by interaction information and total correlation. This article aims to clarify the distinctions between the neuroscience concepts of mutual information, interaction information, and total correlation. Additionally, we proposed a novel method for determining the interaction information between three variables using total correlation and conditional mutual information. On the other hand, how to apply it properly in practical situations. We supplied both simulation experiments and real neural studies to estimate functional connectivity in the brain with the above three higher-order information-theoretic approaches. In order to capture redundancy information for multivariate variables, we discovered that interaction information and total correlation were both robust, and it could be able to capture both well-known and yet-to-be-discovered functional brain connections.
  BibTeX:

                                  @article{LI202285,
  				title = {Functional connectivity inference from fMRI data using multivariate information measures},
  				journal = {Neural Networks},
  				volume = {146},
  				pages = {85-97},
  				year = {2022},
  				issn = {0893-6080},
  				doi = {https://doi.org/10.1016/j.neunet.2021.11.016},
  				url = {https://www.sciencedirect.com/science/article/pii/S0893608021004445},
  				author = {Qiang Li}
  				}

• Functional Connectivity via Total Correlation: Analytical results in Visual Areas, Qiang Li, Greg Ver Steeg, Jesus Malo, Neurocomputing, 2023, 571. 127143. 10.1016/j.neucom.2023.127143.
  [Published][Abstract] [BibTeX] [URL] [ArXiv] [Code&Data] [Companion paper1] [Companion paper2] [Companion paper3]
  Abstract: A recent study invoked the superiority of the Total Correlation concept over the conventional pairwise measures of functional connectivity in neuroscience. That seminal work was restricted to show that empirical measures of Total Correlation lead to connectivity patterns that differ from what is obtained using linear correlation and Mutual Information. However, beyond the obvious multivariate versus bivariate definitions, no theoretical insight on the benefits of Total Correlation was given. The accuracy of the empirical estimators could not be addressed because no controlled scenario with known analytical result was considered either. In this work we analytically illustrate the advantages of Total Correlation to describe the functional connectivity in the visual pathway. Our neural model includes three layers (retina, LGN, and V1 cortex) and one can control the connectivity among the nodes, within the cortex, and the eventual top-down feedback. In this multivariate setting (three nodes with multidimensional signals), we derive analytical results for the three-way Total Correlation and for all possible pairwise Mutual Information measures. These analytical results show that pairwise Mutual Information cannot capture the effect of different intra-cortical inhibitory connections while the three-way Total Correlation can. The presented analytical setting is also useful to check empirical estimators of Total Correlation. Therefore, once certain estimator can be trusted, one can explore the behavior with natural signals where the analytical results (that assume Gaussian signals) are no longer valid. In this regard (a) we explore the effect of connectivity and feedback in the analytical retina-cortex network with natural images, and (b) we assess the functional connectivity in V1-V2-V3-V4 from actual fMRI recordings.
  BibTeX:

  					@article{QL22TC,
  						author = {Li, Qiang and Ver Steeg, Greg and Malo, Jesús},
  						year = {2023},
  						month = {12},
  						pages = {127143},
  						title = {Functional connectivity via total correlation: Analytical results in visual areas},
  						journal = {Neurocomputing},
  						doi = {10.1016/j.neucom.2023.127143}
  						}				
  				

• Investigate Bidirectional Functional Brain Networks Using Directed Information, Qiang Li, Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2022).
  [Published] [Context-Tree Weighting Project] [Asia-Pacific Signal and Information Processing Association (APSIPA)] [URL] [Local PDF] [Certificate1] [Certificate2]

• Noise Perturbation for Saliency Prediction with Psychophysical Synthetic Images, Qiang Li, ArXiv, 2022. doi:10.48550/arXiv.2204.06071.
  [Abstract] [BibTeX] [Code&Data] [ArXiv] [Companion paper]
  Abstract: Convolutional neural networks (CNNs) have achieved great success in natural image saliency prediction. The primary goal of this study is to investigate the performance of saliency prediction in CNN and classic models with psychophysical synthetic images under noise perturbation. Is it still as decent as natural images in terms of performance? In the meantime, it can be used to investigate the relationship between CNNs and human vision, mainly low-level vision functions. On the other hand, are CNNs exact replicas of human visual function? This study used CNNs, Fourier, and spectral models inspired by low-level vision systems to investigate saliency prediction on psychophysical synthetic images rather than natural images. According to our findings, saliency prediction models inspired by Fourier and spectral theory outperformed current pre-trained deep neural networks on psychophysical images with noise perturbation. However, psychophysical models were more unstable in noise than pre-trained deep neural networks. Meanwhile, we suggested that investigating CNNs with psychophysical methods could benefit visual neuroscience and artificial neural network studies.
  BibTeX:

                                  @article{ieeeicip22,
                                                  title={Noise Perturbation for Saliency Prediction with Psychophysical Synthetic Images},
    						author={Li, Qiang},
    						journal={arXiv preprint arXiv:2204.06071},
    						year={2022}
  					}
                                  

• Functional Connectome of the Human Brain with Total Correlation, Qiang Li, Greg Ver Steeg, Shujian Yu, Jesus Malo, Entropy. 2022; 24(12):1725. doi:10.3390/e24121725.
  [Published][Abstract] [BibTeX] [ArXiv] [URL-Feature Paper] [Cover Article in Entropy] [Local PDF] [Feature Paper Certificate] [Code&Data] [Companion paper1] [Companion paper2] [Companion paper3]
  Abstract: Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pair-wise measures such as correlation or mutual information. In this work we build on this idea to infer a large scale (whole brain) connectivity network based on Total Correlation and show the possibility of using this kind of networks as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of total correlation and clustering results are trustable compared to ground truth values. Second, the inferred large scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies but, interestingly, can estimate additional relations beyond pair-wise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.
  BibTeX:

                                  @misc{TCFCC22,
                                                	AUTHOR = {Li, Qiang and Steeg, Greg Ver and Yu, Shujian and Malo, Jesus},
  						TITLE = {Functional Connectome of the Human Brain with Total Correlation},
  						JOURNAL = {Entropy},
  						VOLUME = {24},
  						YEAR = {2022},
  						NUMBER = {12},
  						ARTICLE-NUMBER = {1725},
  						URL = {https://www.mdpi.com/1099-4300/24/12/1725},
  						ISSN = {1099-4300},
  						ABSTRACT = {Recent studies proposed the use of Total Correlation to describe functional connectivity among brain regions as a multivariate alternative to conventional pairwise measures such as correlation or mutual information. In this work, we build on this idea to infer a large-scale (whole-brain) connectivity network based on Total Correlation and show the possibility of using this kind of network as biomarkers of brain alterations. In particular, this work uses Correlation Explanation (CorEx) to estimate Total Correlation. First, we prove that CorEx estimates of Total Correlation and clustering results are trustable compared to ground truth values. Second, the inferred large-scale connectivity network extracted from the more extensive open fMRI datasets is consistent with existing neuroscience studies, but, interestingly, can estimate additional relations beyond pairwise regions. And finally, we show how the connectivity graphs based on Total Correlation can also be an effective tool to aid in the discovery of brain diseases.},
  						DOI = {10.3390/e24121725}
  					}
                                  

• Higher-order Organization in the Human Brain from Matrix-Based Rényi's Entropy, Qiang Li, Shujian Yu, Kristoffer H Madsen, Vince D Calhoun, Armin Iraji, Data Science and Learning Workshop: Unraveling the Brain. A satellite workshop of ICASSP 2023.
  [Published] [Data Science and Learning Workshop: Unraveling the Brain. A satellite workshop of ICASSP 2023] [ArXiv] [URL] [Poster] [Companion paper1] [Companion paper2] [Companion paper3]

• Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks, Qiang Li, Vince D Calhoun, Adithya Ram Ballem, Armin Iraji, Shujian Yu, Jesus Malo, Information-Theoretic Principles in Cognitive Systems, NeurIPS 2023.
  [Published] [Information-Theoretic Principles in Cognitive Systems, NeurIPS 2023] [ArXiv] [URL] [Companion paper1] [Companion paper2] [Companion paper3] [Companion paper4]

• Visual Image Reconstructed Without Semantics from Human Brain Activity Using Linear Image Decoders and Nonlinear Noise Suppression, Qiang Li.
  [bioRxiv]

• Revealing Complex Functional Topology Brain Network Correspondences Between Humans and Marmosets, Qiang Li, Vince D. Calhoun, Armin Iraji, Neuroscience Letters. 2024; 137624.10.1016/j.neulet.2024.137624.
  [Published] [bioRxiv] [URL]

• Exploring nonlinear dynamics in brain functionality through phase portraits and fuzzy recurrence plots, Qiang Li, Vince D. Calhoun, Tuan D. Pham, Armin Iraji, Chaos 1 October 2024; 34 (10): 103123. https://doi.org/10.1063/5.0203926.
  [Published] [URL] [bioRxiv] [Companion paper1]

• Complexity Measures Of Psychotic Brain Activity In The fMRI Signal, Qiang Li, Masoud Seraji, Vince D. Calhoun, Armin Iraji., 2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, USA, 2024, pp.9-12, doi:10.1109/SSIAI59505.2024.10508702.
  [Published] [IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI 2024)] [URL] [bioRxiv] [Companion paper1]

• From Sight to Insight: A Multi-task Approach with the Visual Language Decoding Model, Wei Huang, Pengfei Yang, Ying Tang, Fan Qin, Hengjiang Li, Diwei Wu, Wei Ren, Sizhuo Wang, Yuhao Zhao, Jing Wang, Haoxiang Liu, Jingpeng Li, Yucheng Zhu, Bo Zhou, Jingyuan Sun, Qiang Li, Kaiwen Cheng, Hongmei Yan, Huafu Chen, Information Fusion. 2024; 102573. 10.1016/j.inffus.2024.102573.
  [Published] [bioRxiv] [Companion paper1] [URL] [Local PDF]

• Spatial Development of Brain Networks During The First Six Postnatal Months, Masoud Seraji, Sarah Shultz, Qiang Li, Zening Fu, Vince D Calhoun, Armin Iraji, 10.1101/2024.09.19.613960.
  [bioRxiv]

• Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity, Qiang Li, Wei Huang, Chen Qiao, Huafu Chen, 10.1101/2024.10.03.616506.
  [bioRxiv]

• Hybrid Dynamic High-Order Functional Correlations and Divisive Normalization for Improved Classification of Schizophrenia and Bipolar Disorder, Qiang Li, Vince D Calhoun, UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models, NeurIPS 2024.
  [Published] [UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models, NeurIPS 2024] [URL]

• Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks, Qiang Li, Jingyu Liu, Vince D Calhoun, 10.48550/arXiv.2411.00992.
  [ArXiv]

• The Dynamics of Triple Interactions in Resting fMRI: Insights into Psychotic Disorders, Qiang Li, Vince D Calhoun, Armin Iraji, 10.48550/arXiv.2411.00982.
  [ArXiv]

Archival Repositories (No plan for offical submission)

The Pipeline of Processing fMRI Data with Python Based on the Ecosystem NeuroDebian, Preprints 2019, 2019040027 (doi:10.20944/preprints201904.0027.v2).
[URL]

Research Ebook[Start from COVID19]
[Private]

The Story of My Family/北方李家人[Essay]
[Writing in Chinese. Someday, I'll make it public]

Preventing Harassment and Discrimination in the Workplace
[Certificate]

Abstracts and E-Posters

2020


• Computing Variations of Entropy and Redundancy under Nonlinear Mappings not Preserving the Signal Dimension: Quantifying the Efficiency of V1 Cortex, Entropy 2020: The Scientific Tool of the 21st Century, Porto, Portugal. doi:10.3390/Entropy2021-09813.
  [Published][URL][Local PDF] [Poster Best Poster Certificate]
  
  
• Measuring Functional Connectivity of Human Intra-Cortex Regions with Total Correlation, Entropy 2020: The Scientific Tool of the 21st Century, Porto, Portugal. doi:10.3390/Entropy2021-09797.
  [Published] [URL] [Local PDF] [Poster]
  
  
• A Gaussian Noise Neural Channel Modulated the Functional Connectivity, NeuroMatch3.0., Virtual.
  [Published][URL]

2021


• Visual Information Fidelity with better Vision Models and better Mutual Information Estimates, VSS2021, Virtual. doi:10.1167/jov.21.9.2351.
  [Published][URL] [Local PDF] [Poster]
  
  

2022


• Fuzzy Recurrence Eigenvalues of Aging with fMRI, The Organization for Human Brain Mapping (OHBM 2022), Virtual/In-person, Glasgow, Scotland.
  [Published] [Recurrence plots] [Slides] [Poster] [Code&Data]
  
  
• Basic Psychophysics of Deep Networks Trained via Maximum Differentiation, The 9th Iberian Conference on Perception (CIP 2022), In-person, Barcelona, Spain.
  [Published] [Local PDF] [Companion Info] [Certificate] [Poster]
  
  
• Basic Psychophysics of Deep Networks Trained for Subjective Image Distortion, The 9th Iberian Conference on Perception (CIP 2022), In-person, Barcelona, Spain.
  [Published] [Local PDF] [Certificate]
  
  
• Basic Psychophysics of Deep Networks Trained for Image Segmentation, The 9th Iberian Conference on Perception (CIP 2022), In-person, Barcelona, Spain.
  [Published] [Local PDF] [Poster-made by the first author] [Certificate]
  
  

2023


• Revealing Complex Neurodynamics with Low-dimensional Attractors, International Symposium on Biomedical Imaging (IEEE-ISBI 2023), Virtual/In-person, Cartagena de Indias, Colombia.
  [Published] [Local PDF] [Poster]
  
  

• Complexity Measures Of Psychotic Brain Activity In The fMRI Signal, Fall 2023 Research Day, In-person, TReNDS, Atlanta, GA, USA.
  [Poster] [Related Paper]
  
  

• Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks, Information-Theoretic Principles in Cognitive Systems, NeurIPS 2023, In-person, New Orleans Convention Center, USA.
  [Poster] [Related Paper]
  
  

2024


• Multi-way Multiscale Brain Network Interaction: Insights from Birth to 6 Months, 2024 Fetal, Infant, & Toddler Neuroimaging Group Conference, Baltimore, MD, USA.
  [Accepted] [Local PDF] [Poster]
  
  

• Non-Linear Trajectories of Brain Network Development in Infants: Insights from Resting-State fMRI, ISMRM 2025, Honolulu, Hawaiʻi, USA.
  
  
  

Oral Presentations

2019


• Hokkaido University Summer School, Sapporo, Japan.

2020


• Ph.D Welcome Day, Valencia, Spain.
  [Slides]
• BI2020, Virtual Conference.
  [Slides]
  

2021


• NeuroMatch3.0, Virtual Conference.
  [Slides]
• BI2021, Virtual Conference.
  [Slides]

2022


• Talk to Nandy Lab at Yale, Virtual.
  [Slides]

2023


• Talk to Lab Meeting at TReNDS, In person.
  [Handwritten Manuscript]

2024


• SSIAI2024
  [Slides]

1. The 4th IEEE EMBS Summer School of Neural Engineering in Beijing, P.R.China.

2. First Nepal AI Winter School 2018. Application for participation is open. Apply here to secure your seat.

3. Today-30 Oct.2018, I defend my Denmark master thesis, it's a different and great day for me, but it's not ending for my study. Thanks my supervisors(Prof.Kristoffer H.M. and Prof.Rong X.) again.

4. I would like to recommend some very fantastic books for people whose are interested in fMRI data analysis or just start to study analysis neuroimaging data.

a. Human brain function edited John Ashburner, Karl Friston, Will Penny (the second edition is freely available on-line).

b. Handbook of Functional MRI Data Analysis by Russell A. Poldrack, Jeanette A. Mumford and Thomas E. Nichols.But also you can find some video related with this book from youtube channel, where Jeanette A. Mumford has a fantastic series of videos on neuroimaging analysis: I highly recommend it.

c. Statistical Analysis of fMRI Data by F. Gregory Ashby.

In summary, I hope these information can help U.

5. Brain Networks tutorial from Prof.Russell Poldrack.

6. Pymc-learn built on top of Scikit-learn and PyMC3, It is a Practical Probabilistic Machine Learning in Python, more example link here.

7. Nilearn: machine learning for Neuro-imaging in Python, more tutorial link here.

8. Scikit-learn's Transform tutorial from Pyparis conference.

9. Introduction to Python! One of the best tutorials to help you quickly understand Python.

10. Dirty_Cat: machine learning on dirty categories.

11. A list of tutorials and other resources useful for open science and neuroimaging.

12. The information ocean of machine learning with Python.

13. CodePen is a social development environment for front-end designers and developers. Here is an example for helping you quickly understand and handle codePen and D3.js.

14. Rmarkdown is an R syntax that helps make documents in many different formats (R Markdown supports dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more.).

15. Overleaf is a online real time edit software with Latex syntex.

16. The book of An Introduction to Statistical Learning (with R). This book provides an introduction to statistical learning methods.

17. The book of Statistical Thinking for the 21st Century written by Prof.Russell A. Poldrack(Psychology department of Stanford University).

18. A podcast about neuroscience, artificial intelligence and science more broadly.

19. Bayesian Statistics Using Stan.

20. Scripts for running experiments or analyzing (intracranial) EEG data, and other shared material.

21. International Conference on Basic and Clinical Multimodal Imaging, September 10-14, 2019, Chengdu, P.R.China.

22. Teaching basic lab skills for research computing. More tutorial link here.

23. Berkeley Practical Neuroimaging course in 2015.

24. Unofficial Windows Binaries for Python Extension Packages.

25. PractiCal fMRI: the nuts & bolts. More very useful blog are listed in the right side. At here, I am also strongly recommend andysbrainblog. Go to explore it.

26. 135 Visual Phenomena & Optical Illusions.

27. MATLAB and Octave Functions for Computer Vision and Image Processing.

28. Journals and Conferences I read.

1. Nature Reviews Neuroscience
2. Nature Neuroscience
3. Nature
4. Nature Machine Intelligence
5. Science
6. Trends in Cognitive Sciences
7. Trends in Neurosciences
8. Neural Networks
9. Neural Computation
10. Network: Computation in Neural Systems
11. Journal of Neuroscience
12. Journal of Neurophysiology
13. Journal of Computational Neuroscience
14. Frontiers
15. Cerebral Cortex
16. Human Brain Mapping
17. NeuroImage
18. Current Biology
19. Journal of Vision
20. Vision Research
21. Entropy
22. Brain Informatics
23. Physical Review Letters & E
24. PLOS Computational Biology
25. PLOS One
26. Network Neuroscience
27. Computer Vision and Image Understanding
28. IEEE Transactions on Image Processing
29. IEEE Transactions on Information Theory
30. IEEE Transactions on Pattern Analysis and Machine Intelligence
31. IEEE Transactions on Neural Networks and Learning Systems
32. NeurIPS, ICLR, ICML, CVPR, AAAI, ICCV, ECCV, IJCAI, ICIP, COSYNE.

29. Hand draft for help me understand IT and Divisive Normalization(DN). (tips:zoom to 120)

30. Hand draft for help me understand Huffman, Arithmetic, and Lempel Ziv encoding algorithms. (tips:zoom to 120)

31. Three nice articles for introducing directed information.

1. An overview of directed information and its relationship to Grange causality. [Paper1]
2. A straightforward and traditional method for estimating directed information. [Paper2]
3. The numerical experiment showed that directed information is better than mutual information and also gives a new way to estimate it. [Paper3]

32. Digital image/video processing resources from OLIVES.

33. There are a number of important and intriguing research labs (updating ...).

1. Schwartz Lab:computational neuroscience; natural scene statistics; machine learning.
2. Yang Lab:complex system.
3. Yu Lab:neural population activity.
4. Eero Simoncelli Lab:computational vision.
5. David Heeger Lab:computational neuroimaging.
6. Wilson S Geisler III Lab:natural scene statistics in vision science.
7. Alan Bovik Lab:Laboratory for Image & Video Engineering.
8. CiNet:Center for Information and Neural Networks.
9. Hideaki Shimazaki:Theoretical Neuroscience.

34. There are a number of important and intriguing AI blogs (blue) and human vision perception blogs (green).

1. Google AI Blog
2. DeepMind Blog
3. Distill
4. Colah's Blog
5. Machine Learning Blog-Carnegie Mellon University
6. RStudio AI Blog
7. Cambridge MLG Blog
8. The Berkeley Artificial Intelligence Research Blog
9. Stanford AI Lab Blog
10. WEBVISION:The Organization of the Retina and Visual System
11. Vision Science Library:An Internet Resource for Research in Human and Animal Vision

35. Kyoto calibrated color natural image dataset from Doi et al.

36. Computational Neuroscience:This site provides an annotated index for computational neurobiology, focusing on compartmental modeling and realistic simulations of biological neural systems.

37. Neuroscience News:This is an online science magazine. We offer free to read research articles covering neuroscience, neurology, psychology, artificial intelligence, neurotechnology, robotics, deep learning, neurosurgery, mental health and more.

1. Neurostars is a community for whose of looking help of all kind of neuroscience problems. 15,Oct.2018

2. Documenting python code: A complete guide. This manuscript can help you more write standard style Python code(including script, comment etc). 27,Oct.2018

3. PySlackers is an open community for Python programming enthusiasts. It has a lot of Python resources. The one of best places for talking Python with some talent people. 27,Oct.2018

4. Julia is a dynamic language, it has many good performance compared to others language. In the neuroscience data analysis, the very popular language is Matlab, Python(it used more than Matlab in the future), R. Is it possible to transfer Python to Julia in the neuroscience field? 27,Oct.2018

5. NeuroSpin includes a lot of information for neuroimaging analysis and machine learning library with Python. 31 Oct.2018

6. I would like to transfer my develop environment to cloud in the future, actually I already start doing it, by the way, I also highly recommend to you about it personally. 1 Nov.2018

7. It's a very interesting research Separate brain areas for processing human and dog faces as revealed by awake fMRI in dogs (Canis familiaris) from Jeffrey S. Katz. The one of conclusion from paper is "The findings revealed adjacent but separate brain areas in the left temporal cortex for processing human and dog faces in the dog brain."

That's a very nice find and can help us understand the evolution of face processing. One more thing, in this paper, author also talked some famous model of Face processing in humans. Based on my experience, fantastic, amazing, beautiful research. 2 Nov.2018

8. Pop!_OS system is a totally new and different compute system, the features of compute system more simple from command-line format and it also infusion some new techniques different than traditional system. I would like to change and update my compute system to Pop!_OS, and next document includes how to install it into your system. By the way, system76 also a good choice for your. 3 Nov.2018

9. Nice Essay "The importance of stupidity in scientific research" .6 Nov.2018

10. PretermSurfaces .6 Nov.2018

11. Small tips for programming with Python. Because I am always forget to how to use the function [if __name__=="__main__":]. 16 Nov.2018

12. The tutorial of Git study in Chinese. 18 Nov.2018

13. Nice posts, it includes much useful information for studying Python. 20 Nov.2018

14. D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation. 28 Nov.2018

15. Rodent whole body neuron imaging with vDISCO techniques. More details link above paper. This is real real START OF THE ART(BLACK TECHNOLOGY). 2 Dec.2018

16. 2045 AVATAR PROJECT was founded by Russian entrepreneur Dmitry Itskov in February 2011 with the participation of leading Russian specialists in the field of neural interfaces, robotics, artificial organs and systems. The main goals of the 2045 Initiative: the creation and realization of a new strategy for the development of humanity which meets global civilization challenges; the creation of optimale conditions promoting the spiritual enlightenment of humanity; and the realization of a new futuristic reality based on 5 principles: high spirituality, high culture, high ethics, high science and high technologies. More info link here. 6 Dec.2018

17. The tutorial for workshop of diffusion MRI data analysis with DSI Studio, More information link here. 6 Dec.2018

18. The physical world already exists some classical principle or law governing its behavior, but for the brain, this universe lacks some classical law to explain or guide its behavioral. The post talked about the past, present, and future of biological intelligence(BI) and AI. The whole post link here. 9 Dec.2018

19. Deeping understand how dose the CPU works. Link online simulate model of CPU. 9 Dec.2018

20. How to configure putty and xming server in the Cloud Services(my cloud service is AliCloud), link here and more detail information check here. 9 Dec.2018

21. Some small tips for Linux users. 9 Dec.2018

$ eog ../image dir/ # Check you directory structure

$ sudo apt-get install tree #(Ubuntu)
$ brew install tree #(Mac)
$ yum install tree #(Centos)

$ tree --dirsfirst --filelimit 10
# tree: show file use tree structure
# --dirsfirst: first show directory
# --filelimit: how much files will be show in the screen
# 10: the number of files of show


22. What is Artificial Intelligence? ——Alan Turing

The complete manuscript link here. 10 Dec.2018

23. When your program contains more than one statement, the statements are executed as if they are a story, from top to bottom.
I love this statement. 2 Jan. 2019

24. The first JavaScript code follows the book of ELOQUENT JAVASCRIPT(third edition) by Marijin Haverbeke was done. More information about how to run Js code that link this post. The next imge is produced by Js. 3 Jan. 2019

25. Dr. Miguel Nicolelis led a brain-computer interface lab in Duke University. If you are interested in the brain-computer interface related research, you definitely worth going to visit his lab website, he absolutely did some fantastic research, eg. first brain-to-brain interface allows transmission of tactile and motor information between rats; brain controlled kick to open 2018 year's world cup soccer etc. He also publish two famous book, they separately are "Beyond Boundaries" and "THE RELATIVISTIC BRAIN", you can read at here. 11 Jan. 2019

26. A brain–spine interface alleviating gait deficits after spinal cord injury in primates. The famous and clssical brain-spine interface experiment by Prof.Jocelyne Bloch & Grégoire Courtine. 1 Mar,2019

27. First meeting with my Ph.D. supervisor prof. Jesús Malo at Joaquin Sorolla Station, Valencia, Spain on Sep 28, 2019. What a precious moment for me!

28. Hebbian Learning Rule. Neurons communicate by transmitting impulses over a network of synapses.

29. Mammals Functional Connectivity. "We discovered that brain connectivity -- namely the efficiency of information transfer through the neural network -- does not depend on either the size or structure of any specific brain". "Our study revealed a universal law: Conservation of Brain Connectivity." The original paper link here.

30. 3T Philips MRI machine in Lafe hospital, Valencia, Spain.

31. My workbase at the Lab.

32. My workbase at the home during COVID19.

33. Some eye-blinking statistics are intriguing.

34. The layout of graph visualizations.

35. Human brain vs Artificial neural networks.

36. Predictive coding, variational autoencoders and biological connections (Joseph, 2021). The paper covered a wide range of fancy concepts and probabilistic models.

37. Visual attention in human brain. The full story link here.

38. DNA matching, Iris recognition, Fingerprint detection and Brainprints (maybe in future). The iris and fingerprint images from IITD_database and DB1_B. The below entire figure adapted from my OHBM 2022 Poster.

39. Secondhand computing and portable external hard drive HDD 10TB.

40. The effects of heavy smoking on contrast sensitivity functional (CSF). The figure adapted from Thiago et al,.

41. Color representation in CNNs (top left: RGB color space, top right: Opponent color space, bottom left: YCbCr color space, bottom right: Lab color space). The figure adapted from Anwer et al,.

42. I'm off to Barcelona for the CIP2022 conference, and my poster is here.You can also see some lovely pictures of the Sagrada Familia and other lovely images in the episodic memory window under living in Spain.

43. My first draw with Blender.