Simulation of neuroplasticity in a CNN-based <em>in-silico</em> model of neurodegeneration of the visual system
The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how...
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The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex. More specifically, the injured parts of the network remained injured while we investigated how...
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A combination network of CNN and transformer for interference identification
Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global...
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Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed a novel network that combines both architectures, which make better use of both local and global...
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Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models
Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at...
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Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at...
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Burst and Memory-aware Transformer: capturing temporal heterogeneity
Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model,...
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Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively handle bursty temporal patterns, we propose a Burst and Memory-aware Transformer (BMT) model,...
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A modelling study to dissect the potential role of voltage-gated ion channels in activity-dependent conduction velocity changes as identified in small fiber neuropathy patients
OBJECTIVE: Patients with small fiber neuropathy (SFN) suffer from neuropathic pain, which is still a therapeutic problem. Changed activation patterns of mechano-insensitive peripheral nerve fibers (CMi) could cause neuropathic pain. However, there is sparse knowledge about mechanisms leading to CMi dysfunction since it is difficult to dissect specific molecular mechanisms in humans. We used an in-silico model to elucidate molecular causes of CMi dysfunction as observed in single nerve fiber...
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OBJECTIVE: Patients with small fiber neuropathy (SFN) suffer from neuropathic pain, which is still a therapeutic problem. Changed activation patterns of mechano-insensitive peripheral nerve fibers (CMi) could cause neuropathic pain. However, there is sparse knowledge about mechanisms leading to CMi dysfunction since it is difficult to dissect specific molecular mechanisms in humans. We used an in-silico model to elucidate molecular causes of CMi dysfunction as observed in single nerve fiber...
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Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system
INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms....
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INTRODUCTION: Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms....
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Cortical field maps across human sensory cortex
Cortical processing pathways for sensory information in the mammalian brain tend to be organized into topographical representations that encode various fundamental sensory dimensions. Numerous laboratories have now shown how these representations are organized into numerous cortical field maps (CMFs) across visual and auditory cortex, with each CFM supporting a specialized computation or set of computations that underlie the associated perceptual behaviors. An individual CFM is defined by two...
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Cortical processing pathways for sensory information in the mammalian brain tend to be organized into topographical representations that encode various fundamental sensory dimensions. Numerous laboratories have now shown how these representations are organized into numerous cortical field maps (CMFs) across visual and auditory cortex, with each CFM supporting a specialized computation or set of computations that underlie the associated perceptual behaviors. An individual CFM is defined by two...
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Causal functional connectivity in Alzheimer's disease computed from time series fMRI data
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between...
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Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between...
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Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the...
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The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the...
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Retraction: Cerebral microbleed detection via convolutional neural network and extreme learning machine
This retracts the article DOI: 10.3389/fncom.2021.738885..
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This retracts the article DOI: 10.3389/fncom.2021.738885..
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Editorial: Advances in Shannon-based communications and computations approaches to understanding information processing in the brain
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Improving imbalance classification via ensemble learning based on two-stage learning
The excellent performance of deep neural networks on image classification tasks depends on a large-scale high-quality dataset. However, the datasets collected from the real world are typically biased in their distribution, which will lead to a sharp decline in model performance, mainly because an imbalanced distribution results in the prior shift and covariate shift. Recent studies have typically used a two-stage learning method consisting of two rebalancing strategies to solve these problems,...
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The excellent performance of deep neural networks on image classification tasks depends on a large-scale high-quality dataset. However, the datasets collected from the real world are typically biased in their distribution, which will lead to a sharp decline in model performance, mainly because an imbalanced distribution results in the prior shift and covariate shift. Recent studies have typically used a two-stage learning method consisting of two rebalancing strategies to solve these problems,...
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Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases
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Text clustering based on pre-trained models and autoencoders
Text clustering is the task of grouping text data based on similarity, and it holds particular importance in the medical field. sIn healthcare, medical data clustering is a highly active and effective research area. It not only provides strong support for making correct medical decisions from medical datasets but also aids in patient record management and medical information retrieval. With the development of the healthcare industry, a large amount of medical data is being generated, and...
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Text clustering is the task of grouping text data based on similarity, and it holds particular importance in the medical field. sIn healthcare, medical data clustering is a highly active and effective research area. It not only provides strong support for making correct medical decisions from medical datasets but also aids in patient record management and medical information retrieval. With the development of the healthcare industry, a large amount of medical data is being generated, and...
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Neuro-environmental interactions: a time sensitive matter
INTRODUCTION: The assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environments to minimize variability and rule-out confounding sources of activation during testing conditions. Here, we investigated how temporally-distal environmental inputs, specifically metal exposures experienced up to several months prior to scanning, affect functional dynamics measured using rs functional magnetic resonance imaging (rs-fMRI).
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INTRODUCTION: The assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environments to minimize variability and rule-out confounding sources of activation during testing conditions. Here, we investigated how temporally-distal environmental inputs, specifically metal exposures experienced up to several months prior to scanning, affect functional dynamics measured using rs functional magnetic resonance imaging (rs-fMRI).
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Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS
CONCLUSION: In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.
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CONCLUSION: In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.
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Exploring gene-drug interactions for personalized treatment of post-traumatic stress disorder
INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.
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INTRODUCTION: Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.
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Information representation in an oscillating neural field model modulated by working memory signals
We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus...
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We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity-a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field model has a spatial-like domain in which an input that preferentially targets a point-a stimulus...
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An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management
INTRODUCTION: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints.
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INTRODUCTION: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints.
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Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario
Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to...
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Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous lie detection model was used in the present study. We interviewed 10 healthy subjects to...
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