Sparse measures with swarm-based pliable hidden Markov model and deep learning for EEG classification
No abstract
Read more...
No abstract
Read more...
Production of adaptive movement patterns <em>via</em> an insect inspired spiking neural network central pattern generator
No abstract
Read more...
No abstract
Read more...
Perineuronal nets restrict transport near the neuron surface: A coarse-grained molecular dynamics study
No abstract
Read more...
No abstract
Read more...
Integrating ankle and hip strategies for the stabilization of upright standing: An intermittent control model
No abstract
Read more...
No abstract
Read more...
Integrated world modeling theory expanded: Implications for the future of consciousness
No abstract
Read more...
No abstract
Read more...
🤔1
An interpretable approach for automatic aesthetic assessment of remote sensing images
No abstract
Read more...
No abstract
Read more...
Biases in BCI experiments: Do we really need to balance stimulus properties across categories?
No abstract
Read more...
No abstract
Read more...
👍1
An efficient computer vision-based approach for acute lymphoblastic leukemia prediction
No abstract
Read more...
No abstract
Read more...
Mathematical processing of trading strategy based on long short-term memory neural network model
No abstract
Read more...
No abstract
Read more...
Disrupted visual input unveils the computational details of artificial neural networks for face perception
No abstract
Read more...
No abstract
Read more...
Quasicriticality explains variability of human neural dynamics across life span
No abstract
Read more...
No abstract
Read more...
Classification of dry and wet macular degeneration based on the ConvNeXT model
CONCLUSION: The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.
Read more...
CONCLUSION: The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.
Read more...
Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning
Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth...
Read more...
Extensive studies in rodents show that place cells in the hippocampus have firing patterns that are highly correlated with the animal's location in the environment and are organized in layers of increasing field sizes or scales along its dorsoventral axis. In this study, we use a spatial cognition model to show that different field sizes could be exploited to adapt the place cell representation to different environments according to their size and complexity. Specifically, we provide an in-depth...
Read more...
Emergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network
We establish a simple mechanism by which radially oriented simple cells can emerge in the primary visual cortex. In 1986, R. Linsker. proposed a means by which radially symmetric, spatial opponent cells can evolve, driven entirely by noise, from structure in the initial synaptic connectivity distribution. We provide an analytical derivation of Linsker's results, and further show that radial eigenfunctions can be expressed as a weighted sum of degenerate Cartesian eigenfunctions, and vice-versa....
Read more...
We establish a simple mechanism by which radially oriented simple cells can emerge in the primary visual cortex. In 1986, R. Linsker. proposed a means by which radially symmetric, spatial opponent cells can evolve, driven entirely by noise, from structure in the initial synaptic connectivity distribution. We provide an analytical derivation of Linsker's results, and further show that radial eigenfunctions can be expressed as a weighted sum of degenerate Cartesian eigenfunctions, and vice-versa....
Read more...
Analysis of instantaneous brain interactions contribution to a motor imagery classification task
The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in...
Read more...
The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in...
Read more...
Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram classification
CONCLUSION: These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.
Read more...
CONCLUSION: These superior experimental results demonstrate that the optimal frequency band and time interval selected by the INGHS algorithm could significantly improve the decoding accuracy compared with the traditional CSP method. This method has a potential to improve the performance of MI-based BCI systems.
Read more...
Importance of prefrontal meta control in human-like reinforcement learning
Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy tasks for humans. To reconcile the discrepancy, our paper is focused on the computational benefits of the brain's RL. We examine the brain's ability to combine complementary learning strategies to resolve the trade-off between prediction performance, computational costs, and time constraints. The...
Read more...
Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy tasks for humans. To reconcile the discrepancy, our paper is focused on the computational benefits of the brain's RL. We examine the brain's ability to combine complementary learning strategies to resolve the trade-off between prediction performance, computational costs, and time constraints. The...
Read more...
On the similarities of representations in artificial and brain neural networks for speech recognition
INTRODUCTION: In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system.
Read more...
INTRODUCTION: In recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance, despite their huge differences in implementation, and so deep learning models can-in principle-serve as candidates for mechanistic models of the human auditory system.
Read more...