Neuromorphic energy economics: toward biologically inspired and sustainable power market design
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iSeizdiag: toward the framework development of epileptic seizure detection for healthcare
INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records.
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INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acquisition is the real-time module that records the electrical discharges produced by the brain cells. The electrical discharges are amplified and appear as a graph on electroencephalogram systems. Different neurological disorders are represented as different waves on EEG records.
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Corrigendum: An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique
This corrects the article DOI: 10.3389/fncom.2024.1418280..
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This corrects the article DOI: 10.3389/fncom.2024.1418280..
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Neural oscillation in low-rank SNNs: bridging network dynamics and cognitive function
Neural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity, suggesting a direct link between oscillatory dynamics and cognition. However, there remains a lack of computational models that can systematically simulate and investigate this effect. To address this, we construct a low-rank spiking neural network...
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Neural oscillation, particularly gamma oscillation, are fundamental to cognitive processes such as attention, perception, and decision-making. Experimental studies have shown that the phase of gamma oscillation modulates neuronal response selectivity, suggesting a direct link between oscillatory dynamics and cognition. However, there remains a lack of computational models that can systematically simulate and investigate this effect. To address this, we construct a low-rank spiking neural network...
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A new method for community-based intelligent screening of early Alzheimer's disease populations based on digital biomarkers of the writing process
CONCLUSION: Therefore, digital biomarkers based on the writing process can characterize and quantify the cognitive function of MCI due to AD populations at a fine-grained level, which is expected to be a new method for intelligent screening and early warning of early AD populations in a community-based physician-free setting.
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CONCLUSION: Therefore, digital biomarkers based on the writing process can characterize and quantify the cognitive function of MCI due to AD populations at a fine-grained level, which is expected to be a new method for intelligent screening and early warning of early AD populations in a community-based physician-free setting.
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Constraint-based modeling of bioenergetic differences between synaptic and non-synaptic components of dopaminergic neurons in Parkinson's disease
INTRODUCTION: Emerging evidence suggests that different metabolic characteristics, particularly bioenergetic differences, between the synaptic terminal and soma may contribute to the selective vulnerability of dopaminergic neurons in patients with Parkinson's disease (PD).
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INTRODUCTION: Emerging evidence suggests that different metabolic characteristics, particularly bioenergetic differences, between the synaptic terminal and soma may contribute to the selective vulnerability of dopaminergic neurons in patients with Parkinson's disease (PD).
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Enhancing medical image privacy in IoT with bit-plane level encryption using chaotic map
CONCLUSION: Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.
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CONCLUSION: Extensive evaluations have proven that the designed scheme exhibits a high degree of resilience to attacks, making it particularly suitable for small IoT devices with limited processing power and memory.
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Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data
Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse underlying causes and pathophysiological factors contributing to depression. Therefore, scientific...
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Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse underlying causes and pathophysiological factors contributing to depression. Therefore, scientific...
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Reductionist modeling of calcium-dependent dynamics in recurrent neural networks
Mathematical analysis of biological neural networks, specifically inhibitory networks with all-to-all connections, is challenging due to their complexity and non-linearity. In examining the dynamics of individual neurons, many fast currents are involved solely in spike generation, while slower currents play a significant role in shaping a neuron's behavior. We propose a discrete map approach to analyze the behavior of inhibitory neurons that exhibit bursting modulated by slow calcium currents,...
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Mathematical analysis of biological neural networks, specifically inhibitory networks with all-to-all connections, is challenging due to their complexity and non-linearity. In examining the dynamics of individual neurons, many fast currents are involved solely in spike generation, while slower currents play a significant role in shaping a neuron's behavior. We propose a discrete map approach to analyze the behavior of inhibitory neurons that exhibit bursting modulated by slow calcium currents,...
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Analysis of argument structure constructions in a deep recurrent language model
Understanding how language and linguistic constructions are processed in the brain is a fundamental question in cognitive computational neuroscience. This study builds directly on our previous work analyzing Argument Structure Constructions (ASCs) in the BERT language model, extending the investigation to a simpler, brain-constrained architecture: a recurrent neural language model. Specifically, we explore the representation and processing of four ASCs-transitive, ditransitive, caused-motion,...
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Understanding how language and linguistic constructions are processed in the brain is a fundamental question in cognitive computational neuroscience. This study builds directly on our previous work analyzing Argument Structure Constructions (ASCs) in the BERT language model, extending the investigation to a simpler, brain-constrained architecture: a recurrent neural language model. Specifically, we explore the representation and processing of four ASCs-transitive, ditransitive, caused-motion,...
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CNN-BiLSTM and DC-IGN fusion model and piecewise exponential attenuation optimization: an innovative approach to improve EEG emotion recognition performance
EEG emotion recognition has important applications in human-computer interaction and mental health assessment, but existing models have limitations in capturing the complex spatial and temporal features of EEG signals. To overcome this problem, we propose an innovative model that combines CNN-BiLSTM and DC-IGN and fused both outputs for sentiment classification via a fully connected layer. In addition, we use a piecewise exponential decay strategy to optimize the training process. We conducted a...
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EEG emotion recognition has important applications in human-computer interaction and mental health assessment, but existing models have limitations in capturing the complex spatial and temporal features of EEG signals. To overcome this problem, we propose an innovative model that combines CNN-BiLSTM and DC-IGN and fused both outputs for sentiment classification via a fully connected layer. In addition, we use a piecewise exponential decay strategy to optimize the training process. We conducted a...
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Generalizing location-centric variations to enhance contactless human activity recognition
Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The effectiveness of non-invasive HAR is often hindered by location-centric variations in Channel State Information (CSI). These variations limit the ability of HAR models to generalize across new unseen cross-domain environments, for instance, a model trained in one location might not perform well in...
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Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The effectiveness of non-invasive HAR is often hindered by location-centric variations in Channel State Information (CSI). These variations limit the ability of HAR models to generalize across new unseen cross-domain environments, for instance, a model trained in one location might not perform well in...
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Modeling autonomous shifts between focus state and mind-wandering using a predictive-coding-inspired variational recurrent neural network
Mind-wandering reflects a dynamic interplay between focused attention and off-task mental states. Despite its relevance in understanding fundamental cognitive processes, such as attention regulation, decision-making, and creativity, previous models have not yet provided an account of the neural mechanisms for autonomous shifts between focus state (FS) and mind-wandering (MW). To address this, we conduct model simulation experiments employing predictive coding as a theoretical framework of...
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Mind-wandering reflects a dynamic interplay between focused attention and off-task mental states. Despite its relevance in understanding fundamental cognitive processes, such as attention regulation, decision-making, and creativity, previous models have not yet provided an account of the neural mechanisms for autonomous shifts between focus state (FS) and mind-wandering (MW). To address this, we conduct model simulation experiments employing predictive coding as a theoretical framework of...
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Survey of temporal coding of sensory information
Here we present evidence for the ubiquity of fine spike timing and temporal coding broadly observed across sensory systems and widely conserved across diverse phyla, spanning invertebrates and vertebrates. A taxonomy of basic neural coding types includes channel activation patterns, temporal patterns of spikes, and patterns of spike latencies. Various examples and types of combination temporal-channel codes are discussed, including firing sequence codes. Multiplexing of temporal codes and mixed...
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Here we present evidence for the ubiquity of fine spike timing and temporal coding broadly observed across sensory systems and widely conserved across diverse phyla, spanning invertebrates and vertebrates. A taxonomy of basic neural coding types includes channel activation patterns, temporal patterns of spikes, and patterns of spike latencies. Various examples and types of combination temporal-channel codes are discussed, including firing sequence codes. Multiplexing of temporal codes and mixed...
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DTCNet: finger flexion decoding with three-dimensional ECoG data
ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately...
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ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately...
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Analytical computation for segmentation and classification of lumbar vertebral fractures
Spinal health forms the cornerstone of the overall human body functionality with the lumbar spine playing a critical role and prone to various types of injuries due to inflammation and diseases, including lumbar vertebral fractures. This paper proposes automated method for segmentation of lumbar vertebral body (VB) using image processing techniques such as shape features and morphological operations. This entails an initial phase of image preprocessing, followed by detection and localizing of...
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Spinal health forms the cornerstone of the overall human body functionality with the lumbar spine playing a critical role and prone to various types of injuries due to inflammation and diseases, including lumbar vertebral fractures. This paper proposes automated method for segmentation of lumbar vertebral body (VB) using image processing techniques such as shape features and morphological operations. This entails an initial phase of image preprocessing, followed by detection and localizing of...
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ModFus-PD: synergizing cross-modal attention and contrastive learning for enhanced multimodal diagnosis of Parkinson's disease
Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by a high rate of misdiagnosis, underscoring the critical importance of early and accurate diagnosis. Although existing computer-aided diagnostic systems integrate clinical assessment scales with neuroimaging data, they typically rely on superficial feature concatenation, which fails to capture the deep inter-modal dependencies essential for effective multimodal fusion. To address this limitation, we propose...
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Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by a high rate of misdiagnosis, underscoring the critical importance of early and accurate diagnosis. Although existing computer-aided diagnostic systems integrate clinical assessment scales with neuroimaging data, they typically rely on superficial feature concatenation, which fails to capture the deep inter-modal dependencies essential for effective multimodal fusion. To address this limitation, we propose...
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System-level brain modeling
System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing...
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System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing...
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The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis
INTRODUCTION: The increasing integration of large language models (LLMs) into human-AI collaboration necessitates a deeper understanding of their cognitive impacts on users. Traditional evaluation methods have primarily focused on task performance, overlooking the underlying neural dynamics during interaction.
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INTRODUCTION: The increasing integration of large language models (LLMs) into human-AI collaboration necessitates a deeper understanding of their cognitive impacts on users. Traditional evaluation methods have primarily focused on task performance, overlooking the underlying neural dynamics during interaction.
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Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay
Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human...
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Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human...
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The role of IP<sub>3</sub> receptors and SERCA pumps in restoring working memory under amyloid β induced Alzheimer's disease: a modeling study
Memory impairment is a prevalent symptom in patients with Alzheimer's disease (AD), with working memory loss being the most prominent deficit. Recent experimental evidence suggests that abnormal calcium levels in the Endoplasmic Reticulum (ER) may disrupt synaptic transmission, leading to memory loss in AD patients. However, the specific mechanisms by which intracellular calcium homeostasis influences memory formation, storage, and recall in the context of AD remain unclear. In this study, we...
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Memory impairment is a prevalent symptom in patients with Alzheimer's disease (AD), with working memory loss being the most prominent deficit. Recent experimental evidence suggests that abnormal calcium levels in the Endoplasmic Reticulum (ER) may disrupt synaptic transmission, leading to memory loss in AD patients. However, the specific mechanisms by which intracellular calcium homeostasis influences memory formation, storage, and recall in the context of AD remain unclear. In this study, we...
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