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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Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration
Many computational models that incorporate spike-timing-dependent plasticity (STDP) have shown the ability to learn from stimuli, supporting theories that STDP is a sufficient basis for learning and memory. However, to prevent runaway activity and potentiation, particularly within recurrent networks, additional global mechanisms are commonly necessary. A STDP-based learning rule, which involves local resource-dependent potentiation and heterosynaptic depression, is shown to enable stable...
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Many computational models that incorporate spike-timing-dependent plasticity (STDP) have shown the ability to learn from stimuli, supporting theories that STDP is a sufficient basis for learning and memory. However, to prevent runaway activity and potentiation, particularly within recurrent networks, additional global mechanisms are commonly necessary. A STDP-based learning rule, which involves local resource-dependent potentiation and heterosynaptic depression, is shown to enable stable...
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Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns
Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion...
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Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion...
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Dentate gyrus granule cell activation following extracellular electrical stimulation: a multi-scale computational model to guide hippocampal neurostimulation strategies
CONCLUSION: Stimulation amplitudes above 750 μA consistently activate DG granule cells. Lower stimulation amplitudes are required for axonal activation and downstream synaptic transmission when electrodes are placed in the molecular layer, infra-pyramidal region, and DG crest.
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CONCLUSION: Stimulation amplitudes above 750 μA consistently activate DG granule cells. Lower stimulation amplitudes are required for axonal activation and downstream synaptic transmission when electrodes are placed in the molecular layer, infra-pyramidal region, and DG crest.
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Exploring subthreshold processing for next-generation TinyAI
The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy efficiency, achieving complex computations while consuming orders of magnitude less power. A key mechanism enabling this efficiency is subthreshold processing, where neurons perform computations through...
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The energy demands of modern AI systems have reached unprecedented levels, driven by the rapid scaling of deep learning models, including large language models, and the inefficiencies of current computational architectures. In contrast, biological neural systems operate with remarkable energy efficiency, achieving complex computations while consuming orders of magnitude less power. A key mechanism enabling this efficiency is subthreshold processing, where neurons perform computations through...
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Editorial: Interdisciplinary synergies in neuroinformatics, cognitive computing, and computational neuroscience
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Editorial: Cardio-respiratory-brain integrative physiology: interactions, mechanisms, and methods for assessment
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Super special relativity
This paper proposes a new theoretical framework for understanding time perception centered on information processing in the brain. We introduce the concept of "perceptual time" as distinct from inertial clock time and develop a model relating perceptual time experience to the brain's computational capacity and information processing rate. This framework explains phenomena like time dilation and compression during intense experiences in terms of neural information processing, bridging perceptual...
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This paper proposes a new theoretical framework for understanding time perception centered on information processing in the brain. We introduce the concept of "perceptual time" as distinct from inertial clock time and develop a model relating perceptual time experience to the brain's computational capacity and information processing rate. This framework explains phenomena like time dilation and compression during intense experiences in terms of neural information processing, bridging perceptual...
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The improved thalamo-cortical spiking network model of deep brain stimulation
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Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons
Understanding how functional connectivity between cortical neurons varies with spatial distance is crucial for characterizing large-scale neural dynamics. However, inferring these spatial patterns is challenging when spike trains are collected from large populations of neurons. Here, we present a maximum likelihood estimation (MLE) framework to quantify distance-dependent functional interactions directly from observed spiking activity. We validate this method using both synthetic spike trains...
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Understanding how functional connectivity between cortical neurons varies with spatial distance is crucial for characterizing large-scale neural dynamics. However, inferring these spatial patterns is challenging when spike trains are collected from large populations of neurons. Here, we present a maximum likelihood estimation (MLE) framework to quantify distance-dependent functional interactions directly from observed spiking activity. We validate this method using both synthetic spike trains...
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Transformer-based multimodal precision intervention model for enhancing diaphragm function in elderly patients
Diaphragm dysfunction represents a significant complication in elderly patients undergoing mechanical ventilation, often resulting in extended intensive care stays, unsuccessful weaning attempts, and increased healthcare expenditures. To address the deficiency of precise, real-time decision support in this context, a novel artificial intelligence framework is proposed, integrating imaging, physiological signals, and ventilator parameters. Initially, a hierarchical Transformer encoder is employed...
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Diaphragm dysfunction represents a significant complication in elderly patients undergoing mechanical ventilation, often resulting in extended intensive care stays, unsuccessful weaning attempts, and increased healthcare expenditures. To address the deficiency of precise, real-time decision support in this context, a novel artificial intelligence framework is proposed, integrating imaging, physiological signals, and ventilator parameters. Initially, a hierarchical Transformer encoder is employed...
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Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning
As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning...
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As global life expectancy rises, a growing proportion of the population is affected by dementia, particularly Alzheimer's disease (AD) and Frontotemporal dementia (FTD). Electroencephalography (EEG) based diagnosis presents a non-invasive, cost effective alternative for early detection, yet existing methods are challenged by data scarcity, inter-subject variability, and privacy concerns. This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning...
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Correction: Multi-label remote sensing classification with self-supervised gated multi-modal transformers
This corrects the article DOI: 10.3389/fncom.2024.1404623..
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This corrects the article DOI: 10.3389/fncom.2024.1404623..
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Maximizing theoretical and practical storage capacity in single-layer feedforward neural networks
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks...
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Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks...
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Closed-loop coupling of both physiological spindle model and spinal pathways for sensorimotor control of human center-out reaching
The development of new studies that consider different structures of the hierarchical sensorimotor control system is essential to enable a more holistic understanding about movement. The incorporation of more biological proprioceptive and neuronal circuit models to muscles can turn neuromusculoskeletal systems more appropriate to investigate and elucidate motor control. Specifically, further studies that consider the closed-loop between proprioception and central nervous system may allow to...
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The development of new studies that consider different structures of the hierarchical sensorimotor control system is essential to enable a more holistic understanding about movement. The incorporation of more biological proprioceptive and neuronal circuit models to muscles can turn neuromusculoskeletal systems more appropriate to investigate and elucidate motor control. Specifically, further studies that consider the closed-loop between proprioception and central nervous system may allow to...
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Autonomous retrieval for continuous learning in associative memory networks
The brain's faculty to assimilate and retain information, continually updating its memory while limiting the loss of valuable past knowledge, remains largely a mystery. We address this challenge related to continuous learning in the context of associative memory networks, where the sequential storage of correlated patterns typically requires non-local learning rules or external memory systems. Our work demonstrates how incorporating biologically inspired inhibitory plasticity enables networks to...
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The brain's faculty to assimilate and retain information, continually updating its memory while limiting the loss of valuable past knowledge, remains largely a mystery. We address this challenge related to continuous learning in the context of associative memory networks, where the sequential storage of correlated patterns typically requires non-local learning rules or external memory systems. Our work demonstrates how incorporating biologically inspired inhibitory plasticity enables networks to...
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Editorial: Neuro-detection: advancements in pattern detection and segmentation techniques in neuroscience
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Individualized connectomic tACS immediately improves oscillatory network with language facilitation in post-stroke aphasia: a feasibility study of a dysfunctome-based targeting approach
INTRODUCTION: People with post-stroke aphasia (PSA) exhibit significant interindividual variability attributed to distinctive network disruption patterns across individuals. This complexity limits the effectiveness of conventional one-size-fits-all brain stimulation approaches, but to date no individualized tACS targeting on functional network was studied in PSA. This two-phase study aimed to investigate the immediate network-modulation and language-facilitation effects of dual-site in-phase...
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INTRODUCTION: People with post-stroke aphasia (PSA) exhibit significant interindividual variability attributed to distinctive network disruption patterns across individuals. This complexity limits the effectiveness of conventional one-size-fits-all brain stimulation approaches, but to date no individualized tACS targeting on functional network was studied in PSA. This two-phase study aimed to investigate the immediate network-modulation and language-facilitation effects of dual-site in-phase...
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Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit
Fitting models to experimental intracellular data is challenging. While detailed conductance-based models are difficult to train, phenomenological statistical models often fail to capture the rich intrinsic dynamics of circuits such as central pattern generators (CPGs). A recent trend has been to employ tools from deep learning to obtain data-driven models that can quantitatively learn intracellular dynamics from experimental data. This paper addresses the general questions of modeling,...
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Fitting models to experimental intracellular data is challenging. While detailed conductance-based models are difficult to train, phenomenological statistical models often fail to capture the rich intrinsic dynamics of circuits such as central pattern generators (CPGs). A recent trend has been to employ tools from deep learning to obtain data-driven models that can quantitatively learn intracellular dynamics from experimental data. This paper addresses the general questions of modeling,...
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