Quantitative analysis of the optogenetic excitability of CA1 neurons
INTRODUCTION: Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols.
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INTRODUCTION: Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols.
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Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces
INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate...
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INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate...
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Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications
This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and...
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This article presents a comprehensive analysis of spiking neural networks (SNNs) and their mathematical models for simulating the behavior of neurons through the generation of spikes. The study explores various models, including LIF and NLIF, for constructing SNNs and investigates their potential applications in different domains. However, implementation poses several challenges, including identifying the most appropriate model for classification tasks that demand high accuracy and...
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Smoking behavior detection algorithm based on YOLOv8-MNC
INTRODUCTION: The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness.
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INTRODUCTION: The detection of smoking behavior is an emerging field faced with challenges in identifying small, frequently occluded objects like cigarette butts using existing deep learning technologies. Such challenges have led to unsatisfactory detection accuracy and poor model robustness.
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Editorial: Advanced deep learning approaches for medical neuroimaging data with limitation
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An improved fused feature residual network for 3D point cloud data
Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency. However, using lower order local estimate functions to close 3D objects,...
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Point clouds have evolved into one of the most important data formats for 3D representation. It is becoming more popular as a result of the increasing affordability of acquisition equipment and growing usage in a variety of fields. Volumetric grid-based approaches are among the most successful models for processing point clouds because they fully preserve data granularity while additionally making use of point dependency. However, using lower order local estimate functions to close 3D objects,...
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Synchronization in simplicial complexes of memristive Rulkov neurons
Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure...
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Simplicial complexes are mathematical constructions that describe higher-order interactions within the interconnecting elements of a network. Such higher-order interactions become increasingly significant in neuronal networks since biological backgrounds and previous outcomes back them. In light of this, the current research explores a higher-order network of the memristive Rulkov model. To that end, the master stability functions are used to evaluate the synchronization of a network with pure...
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Erratum: Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields
This corrects the article DOI: 10.3389/fncom.2023.1189949..
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This corrects the article DOI: 10.3389/fncom.2023.1189949..
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Soft error mitigation and recovery of SRAM-based FPGAs using brain-inspired hybrid-grained scrubbing mechanism
Soft error has increasingly become a critical concern for SRAM-based field programmable gate arrays (FPGAs), which could corrupt the configuration memory that stores configuration data describing the custom-designed circuit architecture. To mitigate this kind of error, this study proposes a brain-inspired hybrid-grained scrubbing mechanism consisting of fine-grained and coarse-grained scrubbing to mitigate and repair the errors as quickly as possible after an SEU occurrence. Inspired by the...
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Soft error has increasingly become a critical concern for SRAM-based field programmable gate arrays (FPGAs), which could corrupt the configuration memory that stores configuration data describing the custom-designed circuit architecture. To mitigate this kind of error, this study proposes a brain-inspired hybrid-grained scrubbing mechanism consisting of fine-grained and coarse-grained scrubbing to mitigate and repair the errors as quickly as possible after an SEU occurrence. Inspired by the...
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Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience
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Editorial: Perspectives in brain-network dynamics in computational psychiatry
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Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception
The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that allows, for instance, to fill in occluded information guided by visual experience. Here, we show how a multilayered predictive coding network can learn to recognize objects from the bottom up and to generate specific representations via a top-down...
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The ventral visual processing hierarchy of the cortex needs to fulfill at least two key functions: perceived objects must be mapped to high-level representations invariantly of the precise viewing conditions, and a generative model must be learned that allows, for instance, to fill in occluded information guided by visual experience. Here, we show how a multilayered predictive coding network can learn to recognize objects from the bottom up and to generate specific representations via a top-down...
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Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages
INTRODUCTION: The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.
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INTRODUCTION: The automatic precision detection technology based on electroencephalography (EEG) is essential in epilepsy studies. It can provide objective proof for epilepsy diagnosis, treatment, and evaluation, thus helping doctors improve treatment efficiency. At present, the normal and acute phases of epilepsy can be well identified through EEG analysis, but distinguishing between the normal and chronic phases is still tricky.
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Editorial: Temporal structure of neural processes coupling sensory, motor and cognitive functions of the brain, volume II
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Analysis of abnormal posture in patients with Parkinson's disease using a computational model considering muscle tones
Patients with Parkinson's disease (PD) exhibit distinct abnormal postures, including neck-down, stooped postures, and Pisa syndrome, collectively termed "abnormal posture" henceforth. In the previous study, when assuming an upright stance, patients with PD exhibit heightened instability in contrast to healthy individuals with disturbance, implying that abnormal postures serve as compensatory mechanisms to mitigate sway during static standing. However, limited studies have explored the...
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Patients with Parkinson's disease (PD) exhibit distinct abnormal postures, including neck-down, stooped postures, and Pisa syndrome, collectively termed "abnormal posture" henceforth. In the previous study, when assuming an upright stance, patients with PD exhibit heightened instability in contrast to healthy individuals with disturbance, implying that abnormal postures serve as compensatory mechanisms to mitigate sway during static standing. However, limited studies have explored the...
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Grid cells, border cells, and discrete complex analysis
We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the...
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We propose a mechanism enabling the appearance of border cells-neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the...
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Computational assessment of visual coding across mouse brain areas and behavioural states
CONCLUSION: Our analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states.
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CONCLUSION: Our analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states.
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Simple and complex cells revisited: toward a selectivity-invariance model of object recognition
This paper presents a theoretical perspective on modeling ventral stream processing by revisiting the computational abstraction of simple and complex cells. In parallel to David Marr's vision theory, we organize the new perspective into three levels. At the computational level, we abstract simple and complex cells into space partitioning and composition in a topological space based on the redundancy exploitation hypothesis of Horace Barlow. At the algorithmic level, we present a hierarchical...
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This paper presents a theoretical perspective on modeling ventral stream processing by revisiting the computational abstraction of simple and complex cells. In parallel to David Marr's vision theory, we organize the new perspective into three levels. At the computational level, we abstract simple and complex cells into space partitioning and composition in a topological space based on the redundancy exploitation hypothesis of Horace Barlow. At the algorithmic level, we present a hierarchical...
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