Machine learning identifies genes linked to neurological disorders induced by equine encephalitis viruses, traumatic brain injuries, and organophosphorus nerve agents
Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted...
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Venezuelan, eastern, and western equine encephalitis viruses (collectively referred to as equine encephalitis viruses---EEV) cause serious neurological diseases and pose a significant threat to the civilian population and the warfighter. Likewise, organophosphorus nerve agents (OPNA) are highly toxic chemicals that pose serious health threats of neurological deficits to both military and civilian personnel around the world. Consequently, only a select few approved research groups are permitted...
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Interpretable machine learning for precision cognitive aging
INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a persistent gap exists between interpretability and performance, as black-box models, such as deep neural networks, often outperform white-box models, such as regression-based approaches. To bridge this...
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INTRODUCTION: Machine performance has surpassed human capabilities in various tasks, yet the opacity of complex models limits their adoption in critical fields such as healthcare. Explainable AI (XAI) has emerged to address this by enhancing transparency and trust in AI decision-making. However, a persistent gap exists between interpretability and performance, as black-box models, such as deep neural networks, often outperform white-box models, such as regression-based approaches. To bridge this...
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Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive
Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms would operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the...
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Mounting experimental evidence suggests the hypothesis that brain-state-specific neural mechanisms, supported by the connectome shaped by evolution, could play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms would operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the...
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Reinforced liquid state machines-new training strategies for spiking neural networks based on reinforcements
INTRODUCTION: Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.
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INTRODUCTION: Feedback and reinforcement signals in the brain act as natures sophisticated teaching tools, guiding neural circuits to self-organization, adaptation, and the encoding of complex patterns. This study investigates the impact of two feedback mechanisms within a deep liquid state machine architecture designed for spiking neural networks.
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Regulation of sharp wave-ripples by cholecystokinin-expressing interneurons and parvalbumin-expressing basket cells in the hippocampal CA3 region
To explore the individual and interactive effects of the interneurons cholecystokinin-expressing interneurons (CCKs) and parvalbumin-expressing basket cells (BCs) on sharp wave-ripples (SWR) and the underlying mechanisms, we constructed a mathematical model of the hippocampal CA3 network. By modulating the activity of CCKs and BCs, it was verified that CCKs inhibit the generation of SWR, while the activity of BCs affects the occurrence of SWR. Additionally, it was postulated that CCKs exert an...
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To explore the individual and interactive effects of the interneurons cholecystokinin-expressing interneurons (CCKs) and parvalbumin-expressing basket cells (BCs) on sharp wave-ripples (SWR) and the underlying mechanisms, we constructed a mathematical model of the hippocampal CA3 network. By modulating the activity of CCKs and BCs, it was verified that CCKs inhibit the generation of SWR, while the activity of BCs affects the occurrence of SWR. Additionally, it was postulated that CCKs exert an...
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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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