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arXiv 2023年

作者： Nayebi, Aran Rajalingham, Rishi Jazayeri, Mehrdad Yang, Guangyu Robert McGovern Institute for Brain Research MIT CambridgeMA02139 United States Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States Reality Labs Meta 390 9th Ave New YorkNY10001 United States

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms a

关键词： Forecasting

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Characterizing Focal and Generalized Epileptic Networks Using Interictal Functional Connectivity

Characterizing Focal and Generalized Epileptic Networks Usin...

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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Zarafshan, Elaheh Rajaei, Hoda Forouzannezhad, Parisa Morar, Ulyana Cabrerizo, Mercedes Adjouadi, Malek Florida International University Department of Electrical and Computer Engineering FL United States Massachusetts Institute of Technology Department of Brain and Cognitive Science MA United States

ISBN: (纸本)9781728176246

Using electroencephalography (EEG) data from epileptic patients1, we investigated and compared functional connectivity networks of three various types of epileptiform discharges (ED;single, complex repetitive spikes) in 4 regions of the brain. Our results showed different connectivity patterns among three ED types within-and between-brain regions. The one-way ANOVA test indicated significant differences between the mean of the average connectivity matrices (ACMs) of the single spike, which characterize focal epilepsy, and the other two ED types (complex repetitive) which characterize generalized epilepsy. The interictal EEG segments, through the connectivity patterns they yield, could be considered as one of the key indicators for the diagnosis of focal or generalized epilepsy. © 2020 IEEE.

关键词： Graph theory

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Noisy dynamical systems evolve error correcting codes and modularity

arXiv

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arXiv 2023年

作者： McCourt, Trevor Fiete, Ila R. Chuang, Isaac L. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA02139 United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States

Noise is a ubiquitous feature of the physical world. As a result, the first prerequisite of life is fault tolerance: maintaining integrity of state despite external bombardment. Recent experimental advances have revealed that biological systems achieve fault tolerance by implementing mathematically intricate error-correcting codes and by organizing in a modular fashion that physically separates functionally distinct subsystems. These elaborate structures represent a vanishing volume in the massive genetic configuration space. How is it possible that the primitive process of evolution, by which all biological systems evolved, achieved such unusual results? In this work, through experiments in Boolean networks, we show that the simultaneous presence of error correction and modularity in biological systems is no coincidence. Rather, it is a typical co-occurrence in noisy dynamic systems undergoing evolution. From this, we deduce the principle of error correction enhanced evolvability: systems possessing error-correcting codes are more effectively improved by evolution than those without. Copyright © 2023, The Authors. All rights reserved.

关键词： Error correction

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Location-based training for multi-channel talker-independent speaker separation

arXiv

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arXiv 2021年

作者： Taherian, Hassan Tan, Ke Wang, DeLiang Department of Computer Science and Engineering The Ohio State University United States Center for Cognitive and Brain Sciences The Ohio State University United States

Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial information afforded by microphone arrays, we propose a new training approach to resolving permutation ambiguities for multi-channel speaker separation. The proposed approach, named location-based training (LBT), assigns speakers on the basis of their spatial locations. This training strategy is easy to apply, and organizes speakers according to their positions in physical space. Specifically, this study investigates azimuth angles and source distances for location-based training. Evaluation results on separating two- and three-speaker mixtures show that azimuth-based training consistently outperforms PIT, and distance-based training further improves the separation performance when speaker azimuths are close. Furthermore, we dynamically select azimuth-based or distance-based training by estimating the azimuths of separated speakers, which further improves separation performance. LBT has a linear training complexity with respect to the number of speakers, as opposed to the factorial complexity of PIT. We further demonstrate the effectiveness of LBT for the separation of four and five concurrent speakers. Copyright © 2021, The Authors. All rights reserved.

关键词： Location

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Multi-channel and multi-microphone acoustic echo cancellation using a deep learning based approach

arXiv

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arXiv 2021年

作者： Zhang, Hao Wang, DeLiang Department of Computer Science and Engineering The Ohio State University United States Center for Cognitive and Brain Sciences The Ohio State University United States

Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We train a deep neural network (DNN) to predict the near-end speech from microphone signals with far-end signals used as additional information. We find that the deep learning approach avoids the non-uniqueness problem in traditional MCAEC algorithms. For the AEC setup with multiple microphones, rather than employing AEC for each microphone, a single DNN is trained to achieve echo removal for all microphones. Also, combining deep learning based AEC with deep learning based beamforming further improves the system performance. Experimental results show the effectiveness of both bidirectional long short-term memory (BLSTM) and convolutional recurrent network (CRN) based methods for MCAEC and MMAEC. Furthermore, deep learning based methods are capable of removing echo and noise simultaneously and work well in the presence of nonlinear distortions. Copyright © 2021, The Authors. All rights reserved.

关键词： Microphones

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Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation

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Journal of affective disorders 2023年 343卷 86-95页

作者： Cheng-Ta Li Chi-Sheng Chen Chih-Ming Cheng Chung-Ping Chen Jen-Ping Chen Mu-Hong Chen Ya-Mei Bai Shih-Jen Tsai Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan Division of Psychiatry School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan Institute of Brain Science and Brain Research Center School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan Institute of Cognitive Neuroscience National Central University Jhongli Taiwan. Electronic address: ctil2@vghtpe.gov.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan. Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan. Division of Psychiatry School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan. Institute of Brain Science and Brain Research Center School of Medicine National Yang-Ming Chiao-Tung University Taipei Taiwan.

BACKGROUND:10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS:Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS:Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION:The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for

关键词： Artificial intelligence Depression EEG Machine learning Repetitive transcranial magnetic stimulation theta burst stimulation

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Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling

arXiv

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arXiv 2022年

作者： Li, Zhaoning Jiang, Qiaoli Wu, Zhengming Liu, Anqi Wu, Haiyan Huang, Miner Huang, Kai Ku, Yixuan The Guangdong Provincial Key Laboratory of Brain Function and Disease Centre for Brain and Mental Well-being Department of Psychology Sun Yat-sen University Guangzhou China The Centre for Cognitive and Brain Sciences Department of Psychology University of Macau Taipa China The Guangzhou Intelligent Connected Vehicle Pilot Zone Operations Centre Guangzhou China The Department of Computer Science Whiting School of Engineering Johns Hopkins University Baltimore United States The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger’s seat perspective to examine the humanness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants’ feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers’ judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin’s field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers’ humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers’ ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers’ ascription of humanness, which might become a future direction for autonomous driving. © 2022, CC BY.

关键词： Autonomous vehicles

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Physion: Evaluating Physical Prediction from Vision in Humans and Machines 35

Physion: Evaluating Physical Prediction from Vision in Human...

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35th Conference on Neural Information Processing Systems - Track on Datasets and Benchmarks, NeurIPS Datasets and Benchmarks 2021

作者： Bear, Daniel M. Wang, Elias Mrowca, Damian Binder, Felix Tung, Hsiao-Yu Fish Pramod, R.T. Holdaway, Cameron Tao, Sirui Smith, Kevin Sun, Fan-Yun Fei-Fei, Li Kanwisher, Nancy Tenenbaum, Joshua B. Yamins, Daniel L.K. Fan, Judith Department of Psychology StanfordCA94305 United States Department of Electrical Engineering StanfordCA94305 United States Department of Computer Science StanfordCA94305 United States Wu Tsai Neurosciences Institute StanfordCA94305 United States Department of Cognitive Science UC San DiegoCA92093 United States Department of Psychology UC San DiegoCA92093 United States Department of Brain and Cognitive Sciences CBMM MIT CambridgeMA02139 United States

While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time. Our dataset features realistic simulations of a wide range of physical phenomena, including rigid and soft-body collisions, stable multi-object configurations, rolling, sliding, and projectile motion, thus providing a more comprehensive challenge than previous benchmarks. We used Physion to benchmark a suite of models varying in their architecture, learning objective, input-output structure, and training data. In parallel, we obtained precise measurements of human prediction behavior on the same set of scenarios, allowing us to directly evaluate how well any model could approximate human behavior. We found that vision algorithms that learn object-centric representations generally outperform those that do not, yet still fall far short of human performance. On the other hand, graph neural networks with direct access to physical state information both perform substantially better and make predictions that are more similar to those made by humans. These results suggest that extracting physical representations of scenes is the main bottleneck to achieving human-level and human-like physical understanding in vision algorithms. We have publicly released all data and code to facilitate the use of Physion to benchmark additional models in a fully reproducible manner, enabling systematic evaluation of progress towards vision algorithms that understand physical environments as robustly as people do. © 2021 Neural information processing systems foundation. All rights reserved.

关键词： Graph neural networks

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Detection-and-Excitation Neural Network Achieves Accurate Nasopharyngeal Carcinoma Segmentation in Multi-modality MR Images

Detection-and-Excitation Neural Network Achieves Accurate Na...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Tao, Guihua Li, Haojiang Liu, Lizhi Cai, Hongmin South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University Cancer Center Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Collaborative Innovation Center for Cancer Medicine Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (纸本)9781665401265

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance image (MRI) is important for treatment planning and follow-up evaluation. However, it is still challenging because NPC is infiltrative with a vague border and has a tiny volume with varying sizes and shapes. The above problems easily cause the NPC tumors' features to submerge in the process of feature extraction. Standard segmentation methods do not cope with the 'feature submergence' and performed unsatisfactorily in NPC segmentation. To address the problem, a dual-supervised method equipped with the detection-and-excitation module (DEM) was proposed. DEM strengthens the region of interest (ROI) and weakens complex and immense background through incorporating detection feature maps (outline maps) with intermediated segmentation features. To be specific, the DEM explored the outline of the ROI and output a detection feature map by using a supervised layer guided by a tailored loss function. Then, the DEM uses the results to recalibrates the intermediated segmentation. Finally, a neural network equipped with the proposed DEM was elaborately designed to achieve accurate segmentation. We applied the proposed network termed as DENet on a real nasopharyngeal carcinoma dataset to realize automatic NPC tumors segmentation in multi-modality MR images. The proposed DEM enables the neural network to segment NPC tumors within the immense and complex background. Experimental results have demonstrated the effectiveness of the proposed method by comparing it with benchmark models. © 2021 IEEE.

关键词： Tumors

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