Frontiers in Computational Neuroscience最新文献_第2页

Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-22 eCollection Date: 2025-01-01 DOI: 10.3389/fncom.2025.1452203

Jiayi Zheng, Yaping Wan, Xin Yang, Hua Zhong, Minghua Du, Gang Wang

{"title":"Motion feature extraction using magnocellular-inspired spiking neural networks for drone detection.","authors":"Jiayi Zheng, Yaping Wan, Xin Yang, Hua Zhong, Minghua Du, Gang Wang","doi":"10.3389/fncom.2025.1452203","DOIUrl":"10.3389/fncom.2025.1452203","url":null,"abstract":"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 learn to identify potential regions of moving targets through motion saliency estimation and subsequently integrates the information into the popular object detection algorithms to design the retinal-inspired spiking neural network module for drone motion extraction and object detection architecture, which integrates motion and spatial features before object detection to enhance detection accuracy. To design and train the MG-SNN, we propose a new backpropagation method called Dynamic Threshold Multi-frame Spike Time Sequence (DT-MSTS), and establish a dataset for the training and validation of MG-SNN, effectively extracting and updating visual motion features. Experimental results in terms of drone detection performance indicate that the incorporation of MG-SNN significantly improves the accuracy of low-altitude drone detection tasks compared to popular small object detection algorithms, acting as a cheap plug-and-play module in detecting small flying targets against complex backgrounds.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1452203"},"PeriodicalIF":2.1,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11794278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-15 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1462110

David Kappel, Sen Cheng

{"title":"Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model.","authors":"David Kappel, Sen Cheng","doi":"10.3389/fncom.2024.1462110","DOIUrl":"https://doi.org/10.3389/fncom.2024.1462110","url":null,"abstract":"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.Methods: We use a deep neural network-based reinforcement learning agent to study the learning dynamics that occur during spatial learning and context switching in a simulated ABA extinction and renewal paradigm in a 3D virtual environment.Results: Despite its simplicity, the network exhibits a number of features typically found in the CA1 and CA3 regions of the hippocampus. A significant proportion of neurons in deeper layers of the network are tuned to a specific spatial position of the agent in the environment-similar to place cells in the hippocampus. These complex spatial representations and dynamics occur spontaneously in the hidden layer of a deep network during learning. These spatial representations exhibit global remapping when the agent is exposed to a new context. The spatial maps are restored when the agent returns to the previous context, accompanied by renewal of the conditioned behavior. Remapping is facilitated by memory replay of experiences during training.Discussion: Our results show that integrated codes that jointly represent spatial and task-relevant contextual variables are the mechanism underlying renewal in a simulated DQN agent.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1462110"},"PeriodicalIF":2.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11774835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143064655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

How to be an integrated information theorist without losing your body.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-09 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1510066

Ignacio Cea, Camilo Miguel Signorelli

引用次数: 0

Memory consolidation from a reinforcement learning perspective.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-08 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1538741

Jong Won Lee, Min Whan Jung

{"title":"Memory consolidation from a reinforcement learning perspective.","authors":"Jong Won Lee, Min Whan Jung","doi":"10.3389/fncom.2024.1538741","DOIUrl":"10.3389/fncom.2024.1538741","url":null,"abstract":"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 the CA3 region playing a pivotal role in generating novel activity patterns. Additionally, a growing body of evidence indicates the involvement of the hippocampus, especially the CA1 region, in valuation processes. Based on these findings, we propose that the CA3 region of the hippocampus generates diverse activity patterns, while the CA1 region evaluates and reinforces those patterns most likely to maximize rewards. This framework closely parallels Dyna, a reinforcement learning algorithm introduced by Sutton in 1991. In Dyna, an agent performs offline simulations to supplement trial-and-error value learning, greatly accelerating the learning process. We suggest that memory consolidation might be viewed as a process of deriving optimal strategies based on simulations derived from limited experiences, rather than merely strengthening incidental memories. From this perspective, memory consolidation functions as a form of offline reinforcement learning, aimed at enhancing adaptive decision-making.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1538741"},"PeriodicalIF":2.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal consumer choice prediction using EEG signals and eye tracking.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-08 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1516440

Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair

{"title":"Multimodal consumer choice prediction using EEG signals and eye tracking.","authors":"Syed Muhammad Usman, Shehzad Khalid, Aimen Tanveer, Ali Shariq Imran, Muhammad Zubair","doi":"10.3389/fncom.2024.1516440","DOIUrl":"10.3389/fncom.2024.1516440","url":null,"abstract":"Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. Noise from EEG signals is mitigated using a bandpass filter, Artifact Subspace Reconstruction (ASR), and Fast Orthogonal Regression for Classification and Estimation (FORCE). Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. For ET data, preprocessing involved interpolation, gaze plots, and SMOTE, followed by feature extraction using LeNet-5 and handcrafted features like fixations and saccades. Multimodal feature space representation was generated by performing feature-level fusion for EEG and ET, which was later fed into a meta-learner-based ensemble classifier with three base classifiers, including Random Forest, Extended Gradient Boosting, and Gradient Boosting, and Random Forest as the meta-classifier, to perform classification between buy vs. not buy. The performance of the proposed approach is evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1 score. Our model demonstrated superior performance compared to competitors by achieving 84.01% accuracy in predicting consumer choices and 83% precision in identifying positive consumer preferences.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1516440"},"PeriodicalIF":2.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: 15 years of frontiers in computational neuroscience - computational perception and cognition.

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-07 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1531155

Nicolangelo Iannella

引用次数: 0

Editorial: Advances in computer science and their impact on data acquisition and analysis in neuroscience. 编辑：计算机科学的进步及其对神经科学数据采集和分析的影响。

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2025-01-06 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1537106

Peter Koulen, Amirfarhang Mehdizadeh, Mei-Ling Shyu, Chengcui Zhang, Shu-Ching Chen

引用次数: 0

Learning delays through gradients and structure: emergence of spatiotemporal patterns in spiking neural networks. 通过梯度和结构的学习延迟：尖峰神经网络时空模式的出现。

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2024-12-20 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1460309

Balázs Mészáros, James C Knight, Thomas Nowotny

{"title":"Learning delays through gradients and structure: emergence of spatiotemporal patterns in spiking neural networks.","authors":"Balázs Mészáros, James C Knight, Thomas Nowotny","doi":"10.3389/fncom.2024.1460309","DOIUrl":"https://doi.org/10.3389/fncom.2024.1460309","url":null,"abstract":"We present a Spiking Neural Network (SNN) model that incorporates learnable synaptic delays through two approaches: per-synapse delay learning via Dilated Convolutions with Learnable Spacings (DCLS) and a dynamic pruning strategy that also serves as a form of delay learning. In the latter approach, the network dynamically selects and prunes connections, optimizing the delays in sparse connectivity settings. We evaluate both approaches on the Raw Heidelberg Digits keyword spotting benchmark using Backpropagation Through Time with surrogate gradients. Our analysis of the spatio-temporal structure of synaptic interactions reveals that, after training, excitation and inhibition group together in space and time. Notably, the dynamic pruning approach, which employs DEEP R for connection removal and RigL for reconnection, not only preserves these spatio-temporal patterns but outperforms per-synapse delay learning in sparse networks. Our results demonstrate the potential of combining delay learning with dynamic pruning to develop efficient SNN models for temporal data processing. Moreover, the preservation of spatio-temporal dynamics throughout pruning and rewiring highlights the robustness of these features, providing a solid foundation for future neuromorphic computing applications.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1460309"},"PeriodicalIF":2.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11695354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142931129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial: Deep learning and neuroimage processing in understanding neurological diseases. 社论：深度学习和神经图像处理在理解神经系统疾病中的应用。

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2024-12-19 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1523973

Joana Carvalho, Ali Abdollahzadeh, Ricardo José Ferrari

引用次数: 0

Alleviating the medical strain: a triage method via cross-domain text classification. 缓解医疗压力：通过跨域文本分类的分类方法。

IF 2.1 4区医学

Frontiers in Computational Neuroscience Pub Date : 2024-12-18 eCollection Date: 2024-01-01 DOI: 10.3389/fncom.2024.1468519

Xiao Xiao, Shuqin Wang, Feng Jiang, Tingyue Qi, Wei Wang

{"title":"Alleviating the medical strain: a triage method via cross-domain text classification.","authors":"Xiao Xiao, Shuqin Wang, Feng Jiang, Tingyue Qi, Wei Wang","doi":"10.3389/fncom.2024.1468519","DOIUrl":"10.3389/fncom.2024.1468519","url":null,"abstract":"It is a universal phenomenon for patients who do not know which clinical department to register in large general hospitals. Although triage nurses can help patients, due to the larger number of patients, they have to stand in a queue for minutes to consult. Recently, there have already been some efforts to devote deep-learning techniques or pre-trained language models (PLMs) to triage recommendations. However, these methods may suffer two main limitations: (1) These methods typically require a certain amount of labeled or unlabeled data for model training, which are not always accessible and costly to acquire. (2) These methods have not taken into account the distortion of semantic feature structure and the loss of category discriminability in the model training. To overcome these limitations, in this study, we propose a cross-domain text classification method based on prompt-tuning, which can classify patients' questions or texts about their symptoms into several given categories to give suggestions on which kind of consulting room patients could choose. Specifically, first, different prompt templates are manually crafted based on various data contents, embedding source domain information into the prompt templates to generate another text with similar semantic feature structures for performing classification tasks. Then, five different strategies are employed to expand the label word space for modifying prompts, and the integration of these strategies is used as the final verbalizer. The extensive experiments on Chinese Triage datasets demonstrate that our method achieved state-of-the-art performance.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"18 ","pages":"1468519"},"PeriodicalIF":2.1,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142914074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0