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2024 China Automation Congress, CAC 2024

作者： Qu, Youzhi Li, Wendu Xia, Junfeng Tang, Jiahao Peng, Kaining Liang, Zhichao Wu, Haiyan Liu, Quanying Department of Biomedical Engineering Southern University of Science and Technology Shenzhen China Centre for Cognitive and Brain Sciences Department of Psychology University of Macau China

ISBN: (纸本)9798350368604

The topology of structural brain networks is critical for information transmission and processing across brain regions, enabling the brain to flexibly perform diverse cognitive tasks through functional segregation and integration. While the architecture of brain networks continuously evolves and optimizes under the influence of factors such as wiring costs, it remains unclear whether cognitive tasks also impact this process. We formulate objective functions that integrate cognitive tasks with wiring cost factors and employ a genetic algorithm to iteratively optimize brain network structures. Our findings indicate that the optimized brain network structures driven by cognitive tasks closely resemble actual structural brain networks. Compared to optimizations considering individual cognitive tasks, the multitask optimized brain network demonstrates greater similarity and topological alignment with structural brain networks. These results suggest that the formation of brain structural networks may incorporate the influence of multiple cognitive tasks, offering new insights into the close relationship between cognitive functions and brain structure. © 2024 IEEE.

关键词： Genetic algorithms

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Elevating Robust ASR By Decoupling Multi-Channel Speaker Separation and Speech Recognition

Elevating Robust ASR By Decoupling Multi-Channel Speaker Sep...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yang, Yufeng Taherian, Hassan Kalkhorani, Vahid Ahmadi 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

ISBN: (纸本)9798350368741

Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker scenarios. Speaker separation excels in separating individual talkers but, as a frontend, it introduces processing artifacts that degrade the ASR backend trained on clean speech. As a result, mainstream robust ASR systems train on noisy speech to avoid processing artifacts. In this work, we propose to decouple the training of the multi-channel speaker separation frontend and the ASR backend, with the latter trained only on clean speech. On SMS-WSJ, the proposed approach achieves a word error rate (WER) of 5.74%, outperforming the previous best by 14.3%. Furthermore, on recorded LibriCSS, we achieve the speaker-attributed WER of 3.86%, outperforming the previous best system trained on the same data by 24.8%. These state-of-the-art results suggest that decoupling speech separation and recognition is a potentially effective approach to robust ASR. © 2025 IEEE.

关键词： LibriCSS multi-channel robust ASR SMS-WSJ speaker separation

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Development of Character Recognition Model Inspired by Visual Explanations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1362-1372页

作者： Ralekar, Chetan Choudhary, Shubham Gandhi, Tapan Kumar Chaudhury, Santanu Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Harvard John A. Paulson School of Engineering and Applied Sciences CambridgeMA02138 United States Department of Electrical Engineering Indian Institute of Technology New Delhi110016 India Department of Computer Science and Engineering Indian Institute of Technology Jodhpur342030 India

Deep neural networks (DNNs) currently constitute the best-performing artificial vision systems. However, humans are still better at recognizing many characters, especially distorted, ornamental, or calligraphic characters compared with the highly sophisticated recognitionmodels. Understanding themechanism of character recognition by humans may give some cues for building better recognition models. However, the appropriate methodological approach to using these cues has not been much explored for developing character recognition models. Therefore, this paper tries to understand the process of character recognition by humans and DNNs by generating visual explanations for their respective decisions. We have used eye tracking to assay the spatial distribution of information hotspots for humans via fixation maps. We have proposed a gradient-based method for visualizing the reasoning behind the model's decision through visualization maps and have proved that our method is better than the other class activation mapping methods. Qualitative comparison between visualization maps and fixation maps reveals that both model and humans focus on similar regions in character in the case of correctly classified characters. However, when the focused regions are different for humans and model, the characters are typically misclassified by the latter. Hence, we propose to use the fixation maps as a supervisory input to train the model that ultimately results in improved recognition performance and better generalization. As the proposedmodel gives some insights about the reasoning behind its decision, it can find applications in fields, such as surveillance and medical applications, where explainability helps to determine system fidelity. Impact Statement-Humans and DNNs rely on selective information uptake while classifying a character. This information selection strategy can be understood by visualizing the important, informative character regions that ultimately govern the decision o

关键词： Visualization

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Multi-Resolution Location-Based Training for Multi-Channel Continuous Speech Separation 48

Multi-Resolution Location-Based Training for Multi-Channel C...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-channel talker-independent speaker separation. Assuming fixed array geometry, LBT outperforms widely-used permutation-invariant training in fully overlapped utterances and matched reverberant conditions. This paper extends LBT to conversational multi-channel speaker separation. We introduce multi-resolution LBT to estimate the complex spectrograms from low to high time and frequency resolutions. With multi-resolution LBT, convolutional kernels are assigned consistently based on speaker locations in physical space. Evaluation results show that multi-resolution LBT consistently outperforms other competitive methods on the recorded LibriCSS corpus. © 2023 IEEE.

关键词： Source separation

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Cross-Domain Diffusion Based Speech Enhancement for Very Noisy Speech 48

Cross-Domain Diffusion Based Speech Enhancement for Very Noi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

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

ISBN: (纸本)9781728163277

Deep learning based speech enhancement has achieved remarkable success, but challenges remain in low signal-to-noise ratio (SNR) nonstationary noise scenarios. In this study, we propose to incorporate diffusion-based learning into an enhancement model and improve robustness in extremely noisy conditions. Specifically, a frequency-domain diffusion-based generative module is employed, and it accepts the enhanced signal obtained from a time-domain supervised enhancement module as an auxiliary input to learn to recover clean speech spectrograms. Experimental results on the TIMIT dataset demonstrate the advantage of this approach and show better enhancement performance over other strong baselines in both -5 and -10 dB SNR noisy conditions. © 2023 IEEE.

关键词： Speech enhancement

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Fault-tolerant neural networks from biological error correction codes

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Physical Review E 2024年第5期110卷 054303页

作者： Alexander Zlokapa Andrew K. Tan John M. Martyn Ila R. Fiete Max Tegmark Isaac L. Chuang Center for Theoretical Physics Department of Physics McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Department of Electrical Engineering and Computer Science

It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have been observed to protect states against neural spiking noise, but their role in information processing is unclear. Here, we use these biological error correction codes to develop a universal fault-tolerant neural network that achieves reliable computation if the faultiness of each neuron lies below a sharp threshold; remarkably, we find that noisy biological neurons fall below this threshold. The discovery of a phase transition from faulty to fault-tolerant neural computation suggests a mechanism for reliable computation in the cortex and opens a path towards understanding noisy analog systems relevant to artificial intelligence and neuromorphic computing.

关键词： Biological neural networks Artificial neural networks

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brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Flexible mapping of abstract domains by grid cells via self-supervised extraction and projection of generalized velocity signals 38

Flexible mapping of abstract domains by grid cells via self-...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Iyer, Abhiram Chandra, Sarthak Sharma, Sugandha Fiete, Ila Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Department of Brain and Cognitive Sciences MIT CambridgeMA United States McGovern Institute for Brain Research MIT CambridgeMA United States MIT CambridgeMA United States

Grid cells in the medial entorhinal cortex create remarkable periodic maps of explored space during navigation. Recent studies show that they form similar maps of abstract cognitive spaces. Examples of such abstract environments include auditory tone sequences in which the pitch is continuously varied or images in which abstract features are continuously deformed (e.g., a cartoon bird whose legs stretch and shrink). Here, we hypothesize that the brain generalizes how it maps spatial domains to mapping abstract spaces. To sidestep the computational cost of learning representations for each high-dimensional sensory input, the brain extracts self-consistent, low-dimensional descriptions of displacements across abstract spaces, leveraging the spatial velocity integration of grid cells to efficiently build maps of different domains. Our neural network model for abstract velocity extraction factorizes the content of these abstract domains from displacements within the domains to generate content-independent and self-consistent, low-dimensional velocity estimates. Crucially, it uses a self-supervised geometric consistency constraint that requires displacements along closed loop trajectories to sum to zero, an integration that is itself performed by the downstream grid cell circuit over learning. This process results in high fidelity estimates of velocities and allowed transitions in abstract domains, a crucial prerequisite for efficient map generation in these high-dimensional environments. We also show how our method outperforms traditional dimensionality reduction and deep-learning based motion extraction networks on the same set of tasks. This is the first neural network model to explain how grid cells can flexibly represent different abstract spaces and makes the novel prediction that they should do so while maintaining their population correlation and manifold structure across domains. Fundamentally, our model sheds light on the mechanistic origins of cognitive flexib

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Flexible Context-Driven Sensory Processing in Dynamical Vision Models 38

Flexible Context-Driven Sensory Processing in Dynamical Visi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Govindarajan, Lakshmi Narasimhan Iyer, Abhiram Kothare, Valmiki Fiete, Ila Department of Brain and Cognitive Sciences MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States McGovern Institute for Brain Research MIT CambridgeMA United States MIT CambridgeMA United States

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 long-range 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. © 2024 Neural information processing systems foundation. All rights reserved.

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Learning Individualized Automatic Content Magnification in Gaze-based Interaction

Learning Individualized Automatic Content Magnification in G...

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2023 IEEE International Symposium on Multimedia, ISM 2023

作者： Eggenkemper, Florian Kolker, Lars Valente, Mike Rothkopf, Constantin A. Mertens, Robert Technical University of Darmstadt Centre for Cognitive Science Institute of Psychology Germany Hsw University of Applied Sciences Dept. of Computer Science and Engineering Hamelin Germany Bhh University of Applied Sciences Hamburg Germany

ISBN: (纸本)9798350395761

The precision of modern commercial off-the-shelf eye trackers has reached a level sufficient for developing gaze-based applications. In many but not all applications, accuracy even allows for replacing a computer mouse with gaze-bazed pointing. The Multi-Modal Interaction Concept for Efficient input (M2ice) tackles accuracy problems with an on-demand hybrid fisheye magnifier. This paper introduces an image-analysis-based approach that identifies areas on the screen where to automatically activate magnification for improved interaction. It combines the separate actions magnifying and clicking into one seamless action. The approach works by combining OpenCV filters for detection of clickable elements on the screen with a local machine learning algorithm predicting whether magnification is needed based on size and position of screen elements. A user study (n = 28) showed a significant speed increase of 18.50 percent (t(27)=-3.95, p=.0002 at α = .05) with automatic magnification compared to separate shortcuts for clicking and magnification. © 2023 IEEE.

关键词： Eye tracking

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