Frontiers in Computational Neuroscience最新文献

{"title":"Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories","authors":"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","doi":"10.3389/fncom.2024.1263311","DOIUrl":"https://doi.org/10.3389/fncom.2024.1263311","url":null,"abstract":"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 that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task.Main resultsMDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation.SignificanceThese results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level","authors":"Marina C. Ruppert-Junck, Gunter Kräling, Andrea Greuel, Marc Tittgemeyer, Lars Timmermann, Alexander Drzezga, Carsten Eggers, David Pedrosa","doi":"10.3389/fncom.2024.1328699","DOIUrl":"https://doi.org/10.3389/fncom.2024.1328699","url":null,"abstract":"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 [<jats:sup>18</jats:sup>F]-fluorodeoxyglucose ([<jats:sup>18</jats:sup>F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [<jats:sup>18</jats:sup>F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [<jats:sup>18</jats:sup>F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [<jats:sup>18</jats:sup>F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139762640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Football referee gesture recognition algorithm based on YOLOv8s","authors":"Zhiyuan Yang, Yuanyuan Shen, Yanfei Shen","doi":"10.3389/fncom.2024.1341234","DOIUrl":"https://doi.org/10.3389/fncom.2024.1341234","url":null,"abstract":"<p>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. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model’s attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139904142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end model-based trajectory prediction for ro-ro ship route using dual-attention mechanism","authors":"Licheng Zhao, Yi Zuo, Wenjun Zhang, Tieshan Li, C. L. Philip Chen","doi":"10.3389/fncom.2024.1358437","DOIUrl":"https://doi.org/10.3389/fncom.2024.1358437","url":null,"abstract":"<p>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, this article proposes a dual-attention (DA) based end-to-end (E2E) neural network (DAE2ENet) for trajectory prediction. In the E2E structure, long short-term memory (LSTM) units are included for the task of pursuing sequential trajectory data from the encoder layer to the decoder layer. In DA mechanisms, global attention is introduced between the encoder and decoder layers to facilitate interactions between input and output trajectory sequences, and multi-head self-attention is utilized to extract sequential features from the input trajectory. In experiments, we use a ro-ro ship with a fixed navigation route as a case study. Compared with baseline models and benchmark neural networks, DAE2ENet can obtain higher performance on trajectory prediction, and better validation of environmental factors on ship navigation.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas","authors":"Geyu Weng, Kelsey Clark, Amir Akbarian, Behrad Noudoost, Neda Nategh","doi":"10.3389/fncom.2024.1273053","DOIUrl":"https://doi.org/10.3389/fncom.2024.1273053","url":null,"abstract":"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 and readout of sensory information during a behavior. The widely used point process generalized linear model (GLM) approach provides a powerful framework for a quantitative description of neuronal processing as a function of various sensory and non-sensory inputs (encoding) as well as linking particular response components to particular behaviors (decoding), at the level of single trials and individual neurons. However, most existing variations of GLMs assume the neural systems to be time-invariant, making them inadequate for modeling nonstationary characteristics of neuronal sensitivity in higher visual areas. In this review, we summarize some of the existing GLM variations, with a focus on time-varying extensions. We highlight their applications to understanding neural representations in higher visual areas and decoding transient neuronal sensitivity as well as linking physiology to behavior through manipulation of model components. This time-varying class of statistical models provide valuable insights into the neural basis of various visual behaviors in higher visual areas and hold significant potential for uncovering the fundamental computational principles that govern neuronal processing underlying various behaviors in different regions of the brain.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139585552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks","authors":"Chunxiao Lin, Muhammad Farhan Azmine, Yibin Liang, Yang Yi","doi":"10.3389/fncom.2024.1345644","DOIUrl":"https://doi.org/10.3389/fncom.2024.1345644","url":null,"abstract":"<p>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 critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139923810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural network models: implementation of functional near-infrared spectroscopy-based spontaneous lie detection in an interactive scenario","authors":"M. Raheel Bhutta, Muhammad Umair Ali, Amad Zafar, Kwang Su Kim, Jong Hyuk Byun, Seung Won Lee","doi":"10.3389/fncom.2023.1286664","DOIUrl":"https://doi.org/10.3389/fncom.2023.1286664","url":null,"abstract":"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 lie detection model was used in the present study. We interviewed 10 healthy subjects to identify deception using the fNIRS system. A card game frequently referred to as a bluff or cheat was introduced. This game was selected because its rules are ideal for testing our hypotheses. The optical probe of the fNIRS was placed on the subject’s forehead, and we acquired optical density signals, which were then converted into oxy-hemoglobin and deoxy-hemoglobin signals using the Modified Beer–Lambert law. The oxy-hemoglobin signal was preprocessed to eliminate noise. In this study, we proposed three artificial neural networks inspired by deep learning models, including AlexNet, ResNet, and GoogleNet, to classify deception and truth-telling. The proposed models achieved accuracies of 88.5%, 88.0%, and 90.0%, respectively. These proposed models were compared with other classification models, including k-nearest neighbor, linear support vector machines (SVM), quadratic SVM, cubic SVM, simple decision trees, and complex decision trees. These comparisons showed that the proposed models performed better than the other state-of-the-art methods.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139558858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management","authors":"Hsin-Ping Liu, Frederick Kin Hing Phoa, Yun-Heh Chen-Burger, Shau-Ping Lin","doi":"10.3389/fncom.2024.1283974","DOIUrl":"https://doi.org/10.3389/fncom.2024.1283974","url":null,"abstract":"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 complexities caused by the high-dimensional problems and cross-dimensional constraints.MethodIn this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration.ResultsThe performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139498854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information representation in an oscillating neural field model modulated by working memory signals","authors":"William H. Nesse, Kelsey L. Clark, Behrad Noudoost","doi":"10.3389/fncom.2023.1253234","DOIUrl":"https://doi.org/10.3389/fncom.2023.1253234","url":null,"abstract":"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 model has a spatial-like domain in which an input that preferentially targets a point—a stimulus feature—on the domain will induce feature-specific activity changes. These feature-specific activity changes affect both the mean rate of spikes and the relative timing of spiking activity to the global field oscillation—the phase of the spiking activity. From these two dynamical signatures, we define both a spike rate code and an oscillatory phase code. We assess the performance of these two codes to discriminate stimulus features using an information-theoretic analysis. We show that global WM input modulations can enhance phase code discrimination while simultaneously reducing rate code discrimination. Moreover, we find that the phase code performance is roughly two orders of magnitude larger than that of the rate code defined for the same model solutions. The results of our model have applications to sensory areas of the brain, to which prefrontal areas send inputs reflecting the content of WM. These WM inputs to sensory areas have been established to induce oscillatory changes similar to our model. Our model results suggest a mechanism by which WM signals may enhance sensory information represented in oscillatory activity beyond the comparatively weak representations based on the mean rate activity.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139498778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on eight machine learning algorithms applicability on different characteristics data sets in medical classification tasks","authors":"Yiyan Zhang, Qin Li, Yi Xin","doi":"10.3389/fncom.2024.1345575","DOIUrl":"https://doi.org/10.3389/fncom.2024.1345575","url":null,"abstract":"<p>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 applicability rules of the data mining algorithm suitable for the characteristics of the current research data set. The study quantified characteristics of the research data set with 26 indicators, including simple indicators, statistical indicators and information theory indicators. Eight machine learning algorithms with high maturity, low user involvement and strong family representation were selected as the base algorithms. The algorithm performances were evaluated by three aspects: prediction accuracy, running speed and memory consumption. By constructing decision tree and stepwise regression model to learn the above metadata, the algorithm applicability knowledge of medical data set is obtained. Through cross-verification, the accuracy of all the algorithm applicability prediction models is above 75%, which proves the validity and feasibility of the applicability knowledge.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139658921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}