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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Arun, Ishita Pandey, Pankaj Yadav, Goldy Miyapuram, Krishna Prasad Indian Institute of Technology Gandhinagar Centre for Cognitive and Brain Sciences Gandhinagar India Indian Institute of Technology Gandhinagar Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665401265

Learning a sequence of movements is akin to the acquisition of a motor skill. We investigated event-related potentials (ERPs) changes, particularly the error-related negativity (ERN) and P200 components, as participants learned a motor sequence using a serial reaction time task. Unlike previous studies that investigated error-related negativity for only incorrect motor responses, we tracked ERN changes for all responses. We found that ERN decreased significantly from early to later stages of learning. We also observed a significant change in the P200 component associated with increased selective attention to relevant stimuli as learning occurred. Scalp topography showed the differences in neural activity during motor sequence learning in the frontal, central, and parietal regions. We then employed machine learning to identify the best predictors of motor learning stages. Using random forest, we found the most discriminating pattern for early versus late learning phases. The combination of three electrodes, including 'C3-C4-P3', obtained the maximum accuracy of 82% while classifying EEG signals corresponding to early and late stages of learning. Our study demonstrates that ERN and P200 signals can serve as temporal neural markers for motor skill learning. © 2021 IEEE.

关键词： Errors

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Nonlinear EEG analysis of mindfulness training using interpretable machine learning

Nonlinear EEG analysis of mindfulness training using interpr...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Pandey, Pankaj Miyapuram, Krishna Prasad Indian Institute of Technology Gandhinagar Computer Science and Engineering Gandhinagar India Indian Institute of Technology Gandhinagar Centre for Cognitive and Brain Sciences Gandhinagar India

ISBN: (纸本)9781665401265

With an increasing effort from the scientific community to quantify the effects of meditation practices, various approaches have been used to establish neural correlates of experienced meditators. Numerous techniques have been studied to identify meditation-related changes in brain signals, including network analysis, synchronization metrics, and spectral features. Here, we employ nonlinear measures to explore the dynamical aspects of electroencephalography (EEG) signals in participants who had completed an 8-week Mindfulness Based Stress Reduction training program via pre-post intervention changes. Three nonlinear complexity measures comprising Detrended Fluctuation Analysis (DFA), Higuchi fractal dimension (FD), and Katz FD are implemented to measure the complexity of EEG signals. We examine theta and alpha frequency bands with a specific focus on four sub-bands, along with four anterior and posterior regions. Random Forest (RF) with SHAP explainability method is used to generate the results. RF models are trained for classification on features extracted from pre- and post-session. Our findings reveal that (a) Higuchi FD exhibits a decline post-training session and delivers the best classifying results;(b) the alpha wave contributes the most in the left-right frontal and parietal regions;(c) and overall, the right hemisphere has greater involvement. These findings add to the growing body of knowledge about the neural correlates of meditation. This research's implications for designing neurotechnology products that improve attention, awareness, and kindness have been discussed. © 2021 IEEE.

关键词： Electroencephalography

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Flexible context-driven sensory processing in dynamical vision models 24

Flexible context-driven sensory processing in dynamical visi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lakshmi Narasimhan Govindarajan Abhiram Iyer Valmiki Kothare Ila Fiete Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Electrical Engineering and Computer Science and McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA

ISBN: (纸本)9798331314385

Visual representations become progressively more abstract along the cortical hierarchy. These abstract representations define notions like objects and shapes, but at the cost of spatial specificity. By contrast, low-level regions represent spatially local but simple input features. How do spatially non-specific representations of abstract concepts in high-level areas flexibly modulate the low-level sensory representations in appropriate ways to guide context-driven and goal-directed behaviors across a range of tasks? We build a biologically motivated and trainable neural network model of dynamics in the visual pathway, incorporating local, lateral, and feedforward synaptic connections, excitatory and inhibitory neurons, and longrange top-down inputs conceptualized as low-rank modulations of the input-driven sensory responses by high-level areas. We study this Dynamical Cortical network (DCnet) in a visual cue-delay-search task and show that the model uses its own cue representations to adaptively modulate its perceptual responses to solve the task, outperforming state-of-the-art DNN vision and LLM models. The model's population states over time shed light on the nature of contextual modulatory dynamics, generating predictions for experiments. We fine-tune the same model on classic psychophysics attention tasks, and find that the model closely replicates known reaction time results. This work represents a promising new foundation for understanding and making predictions about perturbations to visual processing in the brain.

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DeAFusion: Detail-Aware Image Fusion for Whitematter Hyperintensity Segmentation

DeAFusion: Detail-Aware Image Fusion for Whitematter Hyperin...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gyeongdeok Jo Jonghun Kim Bo-Yong Park Hyunjin Park Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Republic of Korea Center for Neuroscience Imaging Research Institute for Basic Science Suwon Republic of Korea Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

White matter hyperintensities (WMHs) are critical indicators of cerebral small vessel disease and are linked to stroke and cognitive decline. Accurate WHM segmentation is challenging due to limited contrast and small size, particularly for deep WMHs where T1-weighted and FLAIR MRI are routinely used. Image fusion offers a method to combine two modalities into one fused modality useful for WHM segmentation. We propose DeAFusion: Detail-Aware Image Fusion, a novel image fusion framework. This framework introduces a Pixel-wise Information Preservation Degree map to enable detailed control of fusion at the pixel level and incorporates segmentation loss to embed semantic information. Our model outperforms existing fusion methods in enhancing lesion visibility and preserving structural details, as demonstrated on both public and in-house datasets. DeAFusion shows promise for improving clinical assessments of WMHs. Our code is available at https://***/Gyeongdeok-Jo/DeAFusion.

关键词： Training Image segmentation Wearable Health Monitoring Systems Codes Magnetic resonance imaging Semantics Lesions White matter Image fusion Diseases

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Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

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Proceedings of the ACM on Human-computer Interaction 2021年第CSCW1期5卷 1–42页

作者： Jahanbakhsh, Farnaz Zhang, Amy X. Berinsky, Adam J. Pennycook, Gordon Rand, David G. Karger, David R. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States University of Washington Allen School of Computer Science and Engineering SeattleWA United States Massachusetts Institute of Technology Political Science CambridgeMA United States University of Regina Hill/Levene Schools of Business Regina Canada Massachusetts Institute of Technology Sloan School of Management/Brain and Cognitive Sciences CambridgeMA United States

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true;this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges - -1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate - -on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response. © 2021 Owner/Author.

关键词： Taxonomies

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HSI: Human Saliency Imitator for Benchmarking Saliency-Based Model Explanations 10th

HSI: Human Saliency Imitator for Benchmarking Saliency-Based...

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10th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022

作者： Yang, Yi Zheng, Yueyuan Deng, Didan Zhang, Jindi Huang, Yongxiang Yang, Yumeng Hsiao, Janet H. Cao, Caleb Chen Huawei Research Hong Kong Hong Kong Department of Psychology University of Hong Kong Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Brain and Cognitive Sciences University of Hong Kong Hong Kong Institute of Data Science University of Hong Kong Hong Kong

ISBN: (纸本)9781577358787

Model explanations are generated by XAI (explainable AI)methods to help people understand and interpret machine learning models. To study XAI methods from the human perspective, we propose a human-based benchmark dataset, i.e., human saliency benchmark (HSB), for evaluating saliency based XAI methods. Different from existing human saliency annotations where class-related features are manually and subjectively labeled, this benchmark collects more objective human attention on vision information with a precise eyetracking device and a novel crowdsourcing experiment. Taking the labor cost of human experiment into consideration, we further explore the potential of utilizing a prediction model trained on HSB to mimic saliency annotating by humans. Hence, a dense prediction problem is formulated, and we propose an encoder-decoder architecture which combines multimodal and multi-scale features to produce the human saliency maps. Accordingly, a pretraining-finetuning method is designed to address the model training problem. Finally, we arrive at a model trained on HSB named human saliency imitator(HSI).We show, through an extensive evaluation, that HIS can successfully predict human saliency on our HSB dataset, and the HSI-generated human saliency dataset on Image Netshow cases the ability of benchmarking XAI methods both qualitatively and quantitatively. © 2022, Association for the Advancement of Artificial Intelligence.

关键词： Benchmarking

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Exp[licit] An Educational Robot Modeling Software based on Exponential Maps

Exp[licit] An Educational Robot Modeling Software based on E...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Johannes Lachner Moses C. Nah Stefano Stramigioli Neville Hogan Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA USA Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Deriving a robot’s equations of motion typically requires placing multiple coordinate frames, commonly using the Denavit-Hartenberg convention to express the kinematic and dynamic relationships between segments. This paper presents an alternative using the differential geometric method of Exponential Maps, which reduces the number of coordinate frame choices to two. The traditional and differential geometric methods are compared, and the conceptual and practical differences are detailed. The open-source software, $\operatorname{Exp}[\text { licit }]^{\mathrm{TM}}$, based on the differential geometric method, is introduced. It is intended for use by researchers and engineers with basic knowledge of geometry and robotics and aims to serve as a supportive resource during the study of differential geometric approaches. Code snippets and an example application are provided to demonstrate the benefits of the differential geometric method and assist users to get started with the software.

关键词： Knowledge engineering Geometry Mechatronics Educational robots Robot kinematics Motion segmentation Dynamics

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Towards a New Paradigm for brain-inspired computer Vision

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Machine Intelligence Research 2022年第5期19卷 412-424页

作者： Xiao-Long Zou Tie-Jun Huang Si Wu Beijing Academy of Artificial Intelligence Beijing 100084China School of Psychology and Cognitive Sciences IDG/McGovern Institute for Brain ResearchCenter for Quantitative BiologyPKU-Tsinghua Center for Life SciencesPeking UniversityBeijing 100084China National Engineering Research Center of Visual Technology School of Computer SciencePeking UniversityBeijing 100871China Institute for Artificial Intelligence Peking UniversityBeijing 100871China

brain-inspired computer vision aims to learn from biological systems to develop advanced image processing ***,its progress so far is not *** recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision,i.e.,the biological vision is targeted for processing spatio-temporal ***,a new paradigm for developing brain-inspired computer vision is emerging,which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of *** this paper,we review some recent primary works towards this new paradigm,including the development of spike cameras which acquire spiking signals directly from visual scenes,and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns,including models for object detection,tracking,and *** also discuss about the future directions to improve the paradigm.

关键词： brain-inspired computer vision spatio-temporal patterns object detection object tracking object recognition

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COBRA: A Continual Learning Approach to Vision-brain Understanding

arXiv

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arXiv 2024年

作者： Nguyen, Xuan-Bac Choudhary, Arabinda Kumar Sinha, Pawan Li, Xin Luu, Khoa Serna-Aguilera, Manuel Dept. of Electrical Engineering and Computer Science University of Arkansas AR United States Dept. of Radiology SUNY Upstate Medical University NY United States Dept. of Brain & Cognitive Sciences Massachusetts Institute of Technology MA United States Dept. of Computer Science SUNY Albany NY United States

Vision-brain Understanding (VBU) aims to extract visual information perceived by humans from brain activity recorded through functional Magnetic Resonance Imaging (fMRI). Despite notable advancements in recent years, existing studies in VBU continue to face the challenge of catastrophic forgetting, where models lose knowledge from prior subjects as they adapt to new ones. Addressing continual learning in this field is, therefore, essential. This paper introduces a novel framework called Continual Learning for Vision-brain (COBRA) to address continual learning in VBU. Our approach includes three novel modules: a Subject Commonality (SC) module, a Prompt-based Subject Specific (PSS) module, and a transformer-based module for fMRI, denoted as MRIFormer module. The SC module captures shared vision-brain patterns across subjects, preserving this knowledge as the model encounters new subjects, thereby reducing the impact of catastrophic forgetting. On the other hand, the PSS module learns unique vision-brain patterns specific to each subject. Finally, the MRIFormer module contains a transformer encoder and decoder that learns the fMRI features for VBU from common and specific patterns. In a continual learning setup, COBRA is trained in new PSS and MRIFormer modules for new subjects, leaving the modules of previous subjects unaffected. As a result, COBRA effectively addresses catastrophic forgetting and achieves state-of-the-art performance in both continual learning and vision-brain reconstruction tasks, surpassing previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A theoretical framework for inference learning 22

A theoretical framework for inference learning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Nick Alonso Beren Millidge Jeff Krichmar Emre Neftci Department of Cognitive Science UC Irvine MRC Brain Network Dynamics Unit University of Oxford Department of Cognitive Science UC Irvine and Department of Computer Science UC Irvine Department of Cognitive Science UC Irvine and Department of Computer Science UC Irvine and Electrical Engineering and Information Technology RWTH Aachen Germany and Peter Grünberg Institute Forschungszentrum Jülich Germany

ISBN: (纸本)9781713871088

Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiology. This difficulty has stimulated interest in more biologically plausible alternatives to BP. One such algorithm is the inference learning algorithm (IL). IL trains predictive coding models of neural circuits and has achieved equal performance to BP on supervised and auto-associative tasks. In contrast to BP, however, the mathematical foundations of IL are not well-understood. Here, we develop a novel theoretical framework for IL. Our main result is that IL closely approximates an optimization method known as implicit stochastic gradient descent (implicit SGD), which is distinct from the explicit SGD implemented by BP. Our results further show how the standard implementation of IL can be altered to better approximate implicit SGD. Our novel implementation considerably improves the stability of IL across learning rates, which is consistent with our theory, as a key property of implicit SGD is its stability. We provide extensive simulation results that further support our theoretical interpretations and find IL achieves quicker convergence when trained with mini-batch size one while performing competitively with BP for larger mini-batches when combined with Adam.

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