Editorial: 15 years of frontiers in computational neuroscience - computational perception and cognition
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Memory consolidation from a reinforcement learning perspective
Memory consolidation refers to the process of converting temporary memories into long-lasting ones. It is widely accepted that new experiences are initially stored in the hippocampus as rapid associative memories, which then undergo a consolidation process to establish more permanent traces in other regions of the brain. Over the past two decades, studies in humans and animals have demonstrated that the hippocampus is crucial not only for memory but also for imagination and future planning, with...
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Memory consolidation refers to the process of converting temporary memories into long-lasting ones. It is widely accepted that new experiences are initially stored in the hippocampus as rapid associative memories, which then undergo a consolidation process to establish more permanent traces in other regions of the brain. Over the past two decades, studies in humans and animals have demonstrated that the hippocampus is crucial not only for memory but also for imagination and future planning, with...
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Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model
INTRODUCTION: The hippocampal formation exhibits complex and context-dependent activity patterns and dynamics, e.g., place cell activity during spatial navigation in rodents or remapping of place fields when the animal switches between contexts. Furthermore, rodents show context-dependent renewal of extinguished behavior. However, the link between context-dependent neural codes and context-dependent renewal is not fully understood.
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INTRODUCTION: The hippocampal formation exhibits complex and context-dependent activity patterns and dynamics, e.g., place cell activity during spatial navigation in rodents or remapping of place fields when the animal switches between contexts. Furthermore, rodents show context-dependent renewal of extinguished behavior. However, the link between context-dependent neural codes and context-dependent renewal is not fully understood.
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Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach
Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the standard procedure for diagnosing genetic disorders. Identifying anomalies is often costly, time-consuming, heavily reliant on expert interpretation, and requires considerable manual effort. Efforts are being made to automate karyogram analysis. However, the unavailability of large datasets, particularly those including samples with chromosomal...
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Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the standard procedure for diagnosing genetic disorders. Identifying anomalies is often costly, time-consuming, heavily reliant on expert interpretation, and requires considerable manual effort. Efforts are being made to automate karyogram analysis. However, the unavailability of large datasets, particularly those including samples with chromosomal...
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Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection
Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial-temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can...
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Traditional object detection methods usually underperform when locating tiny or small drones against complex backgrounds, since the appearance features of the targets and the backgrounds are highly similar. To address this, inspired by the magnocellular motion processing mechanisms, we proposed to utilize the spatial-temporal characteristics of the flying drones based on spiking neural networks, thereby developing the Magno-Spiking Neural Network (MG-SNN) for drone detection. The MG-SNN can...
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MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks
Brain tumors are one of the major health threats to humans, and their complex pathological features and anatomical structures make accurate segmentation and detection crucial. However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. On the other hand, CNNs...
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Brain tumors are one of the major health threats to humans, and their complex pathological features and anatomical structures make accurate segmentation and detection crucial. However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. On the other hand, CNNs...
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EEG electrode setup optimization using feature extraction techniques for neonatal sleep state classification
An optimal arrangement of electrodes during data collection is essential for gaining a deeper understanding of neonatal sleep and assessing cognitive health in order to reduce technical complexity and reduce skin irritation risks. Using electroencephalography (EEG) data, a long-short-term memory (LSTM) classifier categorizes neonatal sleep states. An 16,803 30-second segment was collected from 64 infants between 36 and 43 weeks of age at Fudan University Children's Hospital to train and test the...
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An optimal arrangement of electrodes during data collection is essential for gaining a deeper understanding of neonatal sleep and assessing cognitive health in order to reduce technical complexity and reduce skin irritation risks. Using electroencephalography (EEG) data, a long-short-term memory (LSTM) classifier categorizes neonatal sleep states. An 16,803 30-second segment was collected from 64 infants between 36 and 43 weeks of age at Fudan University Children's Hospital to train and test the...
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Artificial intelligence in stroke risk assessment and management via retinal imaging
Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular...
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Retinal imaging, used for assessing stroke-related retinal changes, is a non-invasive and cost-effective method that can be enhanced by machine learning and deep learning algorithms, showing promise in early disease detection, severity grading, and prognostic evaluation in stroke patients. This review explores the role of artificial intelligence (AI) in stroke patient care, focusing on retinal imaging integration into clinical workflows. Retinal imaging has revealed several microvascular...
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences
Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It adopts a signal-centric perspective in which neural assemblies produce circulating and propagating characteristic temporally patterned signals for each attribute (feature). Temporal...
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Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It adopts a signal-centric perspective in which neural assemblies produce circulating and propagating characteristic temporally patterned signals for each attribute (feature). Temporal...
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A new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing: an exploratory study
CONCLUSION: This new method rapidly characterizes and quantifies cognitive impairment in young people with depressive disorders. It provides a new way for organizations, such as schools, to quickly identify and evaluate the population of young people with depressive disorders based on human-computer interaction.
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CONCLUSION: This new method rapidly characterizes and quantifies cognitive impairment in young people with depressive disorders. It provides a new way for organizations, such as schools, to quickly identify and evaluate the population of young people with depressive disorders based on human-computer interaction.
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Editorial: Brain-inspired intelligence: the deep integration of brain science and artificial intelligence
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Estimation of ionic currents and compensation mechanisms from recursive piecewise assimilation of electrophysiological data
The identification of ion channels expressed in neuronal function and neuronal dynamics is critical to understanding neurological disease. This program calls for advanced parameter estimation methods that infer ion channel properties from the electrical oscillations they induce across the cell membrane. Characterization of the expressed ion channels would allow detecting channelopathies and help devise more effective therapies for neurological and cardiac disease. Here, we describe Recursive...
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The identification of ion channels expressed in neuronal function and neuronal dynamics is critical to understanding neurological disease. This program calls for advanced parameter estimation methods that infer ion channel properties from the electrical oscillations they induce across the cell membrane. Characterization of the expressed ion channels would allow detecting channelopathies and help devise more effective therapies for neurological and cardiac disease. Here, we describe Recursive...
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Editorial: Computational intelligence for signal and image processing, volume II
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Exploring the neural basis of creativity: EEG analysis of power spectrum and functional connectivity during creative tasks in school-aged children
Creativity is a fundamental aspect of human cognition, particularly during childhood. Exploring creativity through electroencephalography (EEG) provides valuable insights into the brain mechanisms underlying this vital cognitive process. This study analyzed the power spectrum and functional connectivity of interhemispheric and intrahemispheric brain activity during creative tasks in 15 Argentine children aged 9 to 12, using a 14-channel EEG system. The Torrance test of creative thinking (TTCT)...
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Creativity is a fundamental aspect of human cognition, particularly during childhood. Exploring creativity through electroencephalography (EEG) provides valuable insights into the brain mechanisms underlying this vital cognitive process. This study analyzed the power spectrum and functional connectivity of interhemispheric and intrahemispheric brain activity during creative tasks in 15 Argentine children aged 9 to 12, using a 14-channel EEG system. The Torrance test of creative thinking (TTCT)...
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AD-Diff: enhancing Alzheimer's disease prediction accuracy through multimodal fusion
Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration and the high cost of PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD prediction accuracy by integrating PET images generated...
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Early prediction of Alzheimer's disease (AD) is crucial to improving patient quality of life and treatment outcomes. However, current predictive methods face challenges such as insufficient multimodal information integration and the high cost of PET image acquisition, which limit their effectiveness in practical applications. To address these issues, this paper proposes an innovative model, AD-Diff. This model significantly improves AD prediction accuracy by integrating PET images generated...
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NeuroFusionNet: cross-modal modeling from brain activity to visual understanding
In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, designed to enhance the understanding of visual information by integrating fMRI signals with image features. Specifically, images are processed by a visual model to extract region-of-interest (ROI) features and contextual information, which are then encoded through fully connected...
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In recent years, the integration of machine vision and neuroscience has provided a new perspective for deeply understanding visual information. This paper proposes an innovative deep learning model, NeuroFusionNet, designed to enhance the understanding of visual information by integrating fMRI signals with image features. Specifically, images are processed by a visual model to extract region-of-interest (ROI) features and contextual information, which are then encoded through fully connected...
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Prefrontal meta-control incorporating mental simulation enhances the adaptivity of reinforcement learning agents in dynamic environments
INTRODUCTION: Recent advances in computational neuroscience highlight the significance of prefrontal cortical meta-control mechanisms in facilitating flexible and adaptive human behavior. In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. Rooted from these neuroscientific insights, we present Meta-Dyna, a novel neuroscience-inspired reinforcement learning architecture that demonstrates rapid adaptation to environmental dynamics...
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INTRODUCTION: Recent advances in computational neuroscience highlight the significance of prefrontal cortical meta-control mechanisms in facilitating flexible and adaptive human behavior. In addition, hippocampal function, particularly mental simulation capacity, proves essential in this adaptive process. Rooted from these neuroscientific insights, we present Meta-Dyna, a novel neuroscience-inspired reinforcement learning architecture that demonstrates rapid adaptation to environmental dynamics...
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Further <em>N</em>-Frame networking dynamics of conscious observer-self agents via a functional contextual interface: predictive coding, double-slit quantum mechanical experiment, and decision-making fallacy modeling as applied to the measurement problem in humans and AI
Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this...
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Artificial intelligence (AI) has made some remarkable advances in recent years, particularly within the area of large language models (LLMs) that produce human-like conversational abilities via utilizing transformer-based architecture. These advancements have sparked growing calls to develop tests not only for intelligence but also for consciousness. However, existing benchmarks assess reasoning abilities across various domains but fail to directly address consciousness. To bridge this gap, this...
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