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The connectivity degree controls the difficulty in reservoir design of random boolean networks Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-03-14 Emmanuel Calvet, Bertrand Reulet, Jean Rouat
Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance b has been shown to be pivotal for driving the dynamics and performance of Echo State Networks (ESN) and, more recently
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Neurocomputational mechanisms underlying perception and sentience in the neocortex Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-03-05 Andrew S. Johnson, William Winlow
The basis for computation in the brain is the quantum threshold of “soliton,” which accompanies the ion changes of the action potential, and the refractory membrane at convergences. Here, we provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina. We also explain how the visual cortex operates through quantum-phase processing. In the small-world
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An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-26 Juliette Courson, Mathias Quoy, Yulia Timofeeva, Thanos Manos
Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic
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Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-23 Oleg Maslennikov, Matjaž Perc, Vladimir Nekorkin
In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor–Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically
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Noise-induced synchrony of two-neuron motifs with asymmetric noise and uneven coupling Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-23 Gurpreet Jagdev, Na Yu
Synchronous dynamics play a pivotal role in various cognitive processes. Previous studies extensively investigate noise-induced synchrony in coupled neural oscillators, with a focus on scenarios featuring uniform noise and equal coupling strengths between neurons. However, real-world or experimental settings frequently exhibit heterogeneity, including deviations from uniformity in coupling and noise
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End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-21 Licheng Zhao, Yi Zuo, Wenjun Zhang, Tieshan Li, C. L. Philip Chen
With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of prediction models remain critical bottlenecks. Therefore
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Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-21 Chunxiao Lin, Muhammad Farhan Azmine, Yibin Liang, Yang Yi
The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the
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Football referee gesture recognition algorithm based on YOLOv8s Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-19 Zhiyuan Yang, Yuanyuan Shen, Yanfei Shen
Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees’ gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task
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Artificial intelligence approaches for early detection of neurocognitive disorders among older adults Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-16 Khalid AlHarkan, Nahid Sultana, Noura Al Mulhim, Assim M. AlAbdulKader, Noor Alsafwani, Marwah Barnawi, Khulud Alasqah, Anhar Bazuhair, Zainab Alhalwah, Dina Bokhamseen, Sumayh S. Aljameel, Sultan Alamri, Yousef Alqurashi, Kholoud Al Ghamdi
IntroductionDementia is one of the major global health issues among the aging population, characterized clinically by a progressive decline in higher cognitive functions. This paper aims to apply various artificial intelligence (AI) approaches to detect patients with mild cognitive impairment (MCI) or dementia accurately.MethodsQuantitative research was conducted to address the objective of this study
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Dynamics of antiphase bursting modulated by the inhibitory synaptic and hyperpolarization-activated cation currents Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-09 Linan Guan, Huaguang Gu, Xinjing Zhang
Antiphase bursting related to the rhythmic motor behavior exhibits complex dynamics modulated by the inhibitory synaptic current (Isyn), especially in the presence of the hyperpolarization-activated cation current (Ih). In the present paper, the dynamics of antiphase bursting modulated by the Ih and Isyn is studied in three aspects with a theoretical model. Firstly, the Isyn and the slow Ih with strong
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Colorectal image analysis for polyp diagnosis Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-09 Peng-Cheng Zhu, Jing-Jing Wan, Wei Shao, Xian-Chun Meng, Bo-Lun Chen
Colorectal polyp is an important early manifestation of colorectal cancer, which is significant for the prevention of colorectal cancer. Despite timely detection and manual intervention of colorectal polyps can reduce their chances of becoming cancerous, most existing methods ignore the uncertainties and location problems of polyps, causing a degradation in detection performance. To address these problems
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Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-08 Brent M. Roeder, Xiwei She, Alexander S. Dakos, Bryan Moore, Robert T. Wicks, Mark R. Witcher, Daniel E. Couture, Adrian W. Laxton, Heidi Munger Clary, Gautam Popli, Charles Liu, Brian Lee, Christianne Heck, George Nune, Hui Gong, Susan Shaw, Vasilis Z. Marmarelis, Theodore W. Berger, Sam A. Deadwyler, Dong Song, Robert E. Hampson
ObjectiveHere, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject’s own hippocampal spatiotemporal neural codes for memory.ApproachWe constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles
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Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-02-07 Marina C. Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa
Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal
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Research on eight machine learning algorithms applicability on different characteristics data sets in medical classification tasks Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-31 Yiyan Zhang, Qin Li, Yi Xin
With the vigorous development of data mining field, more and more algorithms have been proposed or improved. How to quickly select a data mining algorithm that is suitable for data sets in medical field is a challenge for some medical workers. The purpose of this paper is to study the comparative characteristics of the general medical data set and the general data sets in other fields, and find the
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Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-29 Geyu Weng, Kelsey Clark, Amir Akbarian, Behrad Noudoost, Neda Nategh
To create a behaviorally relevant representation of the visual world, neurons in higher visual areas exhibit dynamic response changes to account for the time-varying interactions between external (e.g., visual input) and internal (e.g., reward value) factors. The resulting high-dimensional representational space poses challenges for precisely quantifying individual factors’ contributions to the representation
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Positional multi-length and mutual-attention network for epileptic seizure classification Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-25 Guokai Zhang, Aiming Zhang, Huan Liu, Jihao Luo, Jianqing Chen
The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length
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Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-24 M. Raheel Bhutta, Muhammad Umair Ali, Amad Zafar, Kwang Su Kim, Jong Hyuk Byun, Seung Won Lee
Deception is an inevitable occurrence in daily life. Various methods have been used to understand the mechanisms underlying brain deception. Moreover, numerous efforts have been undertaken to detect deception and truth-telling. Functional near-infrared spectroscopy (fNIRS) has great potential for neurological applications compared with other state-of-the-art methods. Therefore, an fNIRS-based spontaneous
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An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-18 Hsin-Ping Liu, Frederick Kin Hing Phoa, Yun-Heh Chen-Burger, Shau-Ping Lin
IntroductionThe Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the
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Information representation in an oscillating neural field model modulated by working memory signals Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-18 William H. Nesse, Kelsey L. Clark, Behrad Noudoost
We study how stimulus information can be represented in the dynamical signatures of an oscillatory model of neural activity—a model whose activity can be modulated by input akin to signals involved in working memory (WM). We developed a neural field model, tuned near an oscillatory instability, in which the WM-like input can modulate the frequency and amplitude of the oscillation. Our neural field
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Identifying distinctive brain regions related to consumer choice behaviors on branded foods using activation likelihood estimation and machine learning Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-17 Shinya Watanuki
IntroductionBrand equity plays a crucial role in a brand’s commercial success; however, research on the brain regions associated with brand equity has had mixed results. This study aimed to investigate key brain regions associated with the decision-making of branded and unbranded foods using quantitative neuroimaging meta-analysis and machine learning.MethodsQuantitative neuroimaging meta-analysis
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Exploring gene-drug interactions for personalized treatment of post-traumatic stress disorder Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-11 Konstantina Skolariki, Panagiotis Vlamos
IntroductionPost-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.MethodsThrough three methodological approaches, 122 genes and 184 Single Nucleotide Polymorphisms (SNPs) associated with PTSD were compiled into a single gene
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A lightweight mixup-based short texts clustering for contrastive learning Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-11 Qiang Xu, HaiBo Zan, ShengWei Ji
Traditional text clustering based on distance struggles to distinguish between overlapping representations in medical data. By incorporating contrastive learning, the feature space can be optimized and applies mixup implicitly during the data augmentation phase to reduce computational burden. Medical case text is prevalent in everyday life, and clustering is a fundamental method of identifying major
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Neuro-environmental interactions: a time sensitive matter Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-08 Azzurra Invernizzi, Stefano Renzetti, Elza Rechtman, Claudia Ambrosi, Lorella Mascaro, Daniele Corbo, Roberto Gasparotti, Cheuk Y. Tang, Donald R. Smith, Roberto G. Lucchini, Robert O. Wright, Donatella Placidi, Megan K. Horton, Paul Curtin
IntroductionThe assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environments to minimize variability and rule-out confounding sources of activation during testing conditions. Here, we investigated how temporally-distal environmental inputs, specifically metal exposures experienced up to several months prior to scanning, affect
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Text clustering based on pre-trained models and autoencoders Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-05 Qiang Xu, Hao Gu, ShengWei Ji
Text clustering is the task of grouping text data based on similarity, and it holds particular importance in the medical field. sIn healthcare, medical data clustering is a highly active and effective research area. It not only provides strong support for making correct medical decisions from medical datasets but also aids in patient record management and medical information retrieval. With the development
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Improving imbalance classification via ensemble learning based on two-stage learning Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-05 Na Liu, Jiaqi Wang, Yongtong Zhu, Lihong Wan, Qingdu Li
The excellent performance of deep neural networks on image classification tasks depends on a large-scale high-quality dataset. However, the datasets collected from the real world are typically biased in their distribution, which will lead to a sharp decline in model performance, mainly because an imbalanced distribution results in the prior shift and covariate shift. Recent studies have typically used
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Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS Front. Comput. Neurosci. (IF 3.2) Pub Date : 2024-01-04 Joseph Geraci, Ravi Bhargava, Bessi Qorri, Paul Leonchyk, Douglas Cook, Moses Cook, Fanny Sie, Luca Pani
IntroductionAdvances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are
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Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-19 Thanos Manos, Sandra Diaz-Pier, Igor Fortel, Ira Driscoll, Liang Zhan, Alex Leow
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer
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Causal functional connectivity in Alzheimer's disease computed from time series fMRI data Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-19 Rahul Biswas, SuryaNarayana Sripada
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC)
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Cortical field maps across human sensory cortex Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-15 Alyssa A. Brewer, Brian Barton
Cortical processing pathways for sensory information in the mammalian brain tend to be organized into topographical representations that encode various fundamental sensory dimensions. Numerous laboratories have now shown how these representations are organized into numerous cortical field maps (CMFs) across visual and auditory cortex, with each CFM supporting a specialized computation or set of computations
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Monitoring time domain characteristics of Parkinson’s disease using 3D memristive neuromorphic system Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-15 Md Abu Bakr Siddique, Yan Zhang, Hongyu An
IntroductionParkinson’s disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS
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A modelling study to dissect the potential role of voltage-gated ion channels in activity-dependent conduction velocity changes as identified in small fiber neuropathy patients Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-14 Anna Maxion, Ekaterina Kutafina, Maike F. Dohrn, Pierre Sacré, Angelika Lampert, Jenny Tigerholm, Barbara Namer
ObjectivePatients with small fiber neuropathy (SFN) suffer from neuropathic pain, which is still a therapeutic problem. Changed activation patterns of mechano-insensitive peripheral nerve fibers (CMi) could cause neuropathic pain. However, there is sparse knowledge about mechanisms leading to CMi dysfunction since it is difficult to dissect specific molecular mechanisms in humans. We used an in-silico
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Burst and Memory-aware Transformer: capturing temporal heterogeneity Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-12 Byounghwa Lee, Jung-Hoon Lee, Sungyup Lee, Cheol Ho Kim
Burst patterns, characterized by their temporal heterogeneity, have been observed across a wide range of domains, encompassing event sequences from neuronal firing to various facets of human activities. Recent research on predicting event sequences leveraged a Transformer based on the Hawkes process, incorporating a self-attention mechanism to capture long-term temporal dependencies. To effectively
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Altered synaptic plasticity at hippocampal CA1–CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-07 Justinas J. Dainauskas, Paola Vitale, Sebastien Moreno, Hélène Marie, Michele Migliore, Ausra Saudargiene
Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the
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A combination network of CNN and transformer for interference identification Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-06 Hu Zhang, Meng Zhao, Min Zhang, Sheng Lin, Youqiang Dong, Hai Wang
Communication interference identification is critical in electronic countermeasures. However, existed methods based on deep learning, such as convolutional neural networks (CNNs) and transformer, seldom take both local characteristics and global feature information of the signal into account. Motivated by the local convolution property of CNNs and the attention mechanism of transformer, we designed
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Simulation of neuroplasticity in a CNN-based in-silico model of neurodegeneration of the visual system Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-12-01 Jasmine A. Moore, Matthias Wilms, Alejandro Gutierrez, Zahinoor Ismail, Kayson Fakhar, Fatemeh Hadaeghi, Claus C. Hilgetag, Nils D. Forkert
The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by equipping it with a mechanism to simulate neuroplasticity. Therefore, deep convolutional networks of multiple sizes were trained for object recognition tasks and progressively lesioned to simulate neurodegeneration of the visual cortex
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Bio-inspired circular latent spaces to estimate objects' rotations Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-24 Alice Plebe, Mauro Da Lio
This paper proposes a neural network model that estimates the rotation angle of unknown objects from RGB images using an approach inspired by biological neural circuits. The proposed model embeds the understanding of rotational transformations into its architecture, in a way inspired by how rotation is represented in the ellipsoid body of Drosophila. To effectively capture the cyclic nature of rotation
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LGNN: a novel linear graph neural network algorithm Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-23 Shujuan Cao, Xiaoming Wang, Zhonglin Ye, Mingyuan Li, Haixing Zhao
The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of
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Epileptic focus localization using transfer learning on multi-modal EEG Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-23 Yong Yang, Feng Li, Jing Luo, Xiaolin Qin, Dong Huang
The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied
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Cellular computation and cognition Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-23 W. Tecumseh Fitch
Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of synaptic inputs computed by the “units” in standard neural network models. Neurons are
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Enhanced representation learning with temporal coding in sparsely spiking neural networks Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-23 Adrien Fois, Bernard Girau
Current representation learning methods in Spiking Neural Networks (SNNs) rely on rate-based encoding, resulting in high spike counts, increased energy consumption, and slower information transmission. In contrast, our proposed method, Weight-Temporally Coded Representation Learning (W-TCRL), utilizes temporally coded inputs, leading to lower spike counts and improved efficiency. To address the challenge
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Consciousness, 4E cognition and Aristotle: a few conceptual and historical aspects Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-22 Diana Stanciu
The new approach in cognitive science largely known as “4E cognition” (embodied/embedded/enactive/extended cognition), which sheds new light on the complex dynamics of human consciousness, seems to revive some of Aristotle's views. For instance, the concept of “nature” (phusis) and the discussion on “active intellect” (nous poiêtikos) may be particularly relevant in this respect. Out of the various
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Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-02 Huilin Zhao, Sungchil Yang, Chi Chung Alan Fung
IntroductionThe N-methyl-D-aspartate receptor (NMDAR) plays a critical role in synaptic transmission and is associated with various neurological and psychiatric disorders. Recently, a novel form of postsynaptic plasticity known as NMDAR-based short-term postsynaptic plasticity (STPP) has been identified. It has been suggested that long-lasting glutamate binding to NMDAR allows for the retention of
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Editorial: Complex network dynamics in consciousness. Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-11-01 Francisco J Esteban,Antonio Ibáñez-Molina,Sergio Iglesias-Parro,Juan Ruiz de Miras,Fernando Soler-Toscano
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Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-31 Ming Li, Jun Yang, Wenli Tian, Xiangyu Ju
An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive
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Spatial frequency channels depend on stimulus bandwidth in normal and amblyopic vision: an exploratory factor analysis Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-24 Alexandre Reynaud, Seung Hyun Min
The Contrast Sensitivity Function (CSF) is the measure of an observer’s contrast sensitivity as a function of spatial frequency. It is a sensitive measure to assess visual function in fundamental and clinical settings. Human contrast sensitivity is subserved by different spatial frequency channels. Also, it is known that amblyopes have deficits in contrast sensitivity, particularly at high spatial
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Lightweight semantic segmentation network with configurable context and small object attention Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-23 Chunyu Zhang, Fang Xu, Chengdong Wu, Jinzhao Li
The current semantic segmentation algorithms suffer from encoding feature distortion and small object feature loss. Context information exchange can effectively address the feature distortion problem, but it has the issue of fixed spatial range. Maintaining the input feature resolution can reduce the loss of small object information but would slow down the network’s operation speed. To tackle these
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Modelling decision-making biases Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-20 Ettore Cerracchio, Steven Miletić, Birte U. Forstmann
Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure
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Clustering and disease subtyping in Neuroscience, toward better methodological adaptations. Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-19 Konstantinos Poulakis,Eric Westman
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Atypical development of causal inference in autism inferred through a neurocomputational model Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-19 Melissa Monti, Sophie Molholm, Cristiano Cuppini
In everyday life, the brain processes a multitude of stimuli from the surrounding environment, requiring the integration of information from different sensory modalities to form a coherent perception. This process, known as multisensory integration, enhances the brain’s response to redundant congruent sensory cues. However, it is equally important for the brain to segregate sensory inputs from distinct
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Containment control of multiple unmanned surface vessels with NN control via reconfigurable hierarchical topology Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-19 Wei Liu, Fei Teng, Huiyu Xiao, Chen Wang
This paper investigates the containment control of multiple unmanned surface vessels with nonlinear dynamics. To solve the leader-follower synchronization problem in a containment control system, a hierarchical control framework with a topology reconfiguration mechanism is proposed, and the process of containment control is converted into the tracking of a reference signal for each vessel on its respective
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The storage capacity of a directed graph and nodewise autonomous, ubiquitous learning Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-19 Hui Wei, Fushun Li
The brain, an exceedingly intricate information processing system, poses a constant challenge to memory research, particularly in comprehending how it encodes, stores, and retrieves information. Cognitive psychology studies memory mechanism from behavioral experiment level and fMRI level, and neurobiology studies memory mechanism from anatomy and electrophysiology level. Current research findings are
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Computational assessment of visual coding across mouse brain areas and behavioural states Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-13 Yizhou Xie, Sadra Sadeh
IntroductionOur brain is bombarded by a diverse range of visual stimuli, which are converted into corresponding neuronal responses and processed throughout the visual system. The neural activity patterns that result from these external stimuli vary depending on the object or scene being observed, but they also change as a result of internal or behavioural states. This raises the question of to what
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Simple and complex cells revisited: toward a selectivity-invariance model of object recognition Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-13 Xin Li, Shuo Wang
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
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Grid cells, border cells, and discrete complex analysis Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-10 Yuri Dabaghian
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
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Analysis of abnormal posture in patients with Parkinson's disease using a computational model considering muscle tones Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-10-05 Yuichiro Omura, Hiroki Togo, Kohei Kaminishi, Tetsuya Hasegawa, Ryosuke Chiba, Arito Yozu, Kaoru Takakusaki, Mitsunari Abe, Yuji Takahashi, Takashi Hanakawa, Jun Ota
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
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Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-09-29 Shiming Zheng, Xiaopei Zhang, Panpan Song, Yue Hu, Xi Gong, Xiaoling Peng
IntroductionThe 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
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Editorial: Computational intelligence for signal and image processing. Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-09-27 Deepika Koundal,Baiyuan Ding
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Local minimization of prediction errors drives learning of invariant object representations in a generative network model of visual perception Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-09-26 Matthias Brucklacher, Sander M. Bohté, Jorge F. Mejias, Cyriel M. A. Pennartz
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
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Editorial: Perspectives in brain-network dynamics in computational psychiatry. Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-09-22 Sou Nobukawa,Tetsuya Takahashi
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Editorial: Advances in machine learning methods facilitating collaborative image-based decision making for neuroscience. Front. Comput. Neurosci. (IF 3.2) Pub Date : 2023-09-12 Chengjia Wang,Heye Zhang,Georgios Papanastasiou,Guang Yang