Cognitive Neurodynamics最新文献_第6页

Single-trial neurodynamics reveal N400 and P600 coupling in language comprehension. 单试验神经动力学揭示语言理解中N400和P600的耦合

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2023-06-20 DOI: 10.1007/s11571-023-09983-7

Christoph Aurnhammer, Matthew W Crocker, Harm Brouwer

{"title":"Single-trial neurodynamics reveal N400 and P600 coupling in language comprehension.","authors":"Christoph Aurnhammer, Matthew W Crocker, Harm Brouwer","doi":"10.1007/s11571-023-09983-7","DOIUrl":"10.1007/s11571-023-09983-7","url":null,"abstract":"Theories of the electrophysiology of language comprehension are mostly informed by event-related potential effects observed between condition averages. We here argue that a dissociation between competing effect-level explanations of event-related potentials can be achieved by turning to predictions and analyses at the single-trial level. Specifically, we examine the single-trial dynamics in event-related potential data that exhibited a biphasic N400-P600 effect pattern. A group of multi-stream models can explain biphasic effects by positing that each individual trial should induce either an N400 increase or a P600 increase, but not both. An alternative, single-stream account, Retrieval-Integration theory, explicitly predicts that N400 amplitude and P600 amplitude should be correlated at the single-trial level. In order to investigate the single-trial dynamics of the N400 and the P600, we apply a regression-based technique in which we quantify the extent to which N400 amplitudes are predictive of the electroencephalogram in the P600 time window. Our findings suggest that, indeed, N400 amplitudes and P600 amplitudes are inversely correlated within-trial and, hence, the N400 effect and the P600 effect in biphasic data are driven by the same trials. Critically, we demonstrate that this finding also extends to data which exhibited only monophasic effects between conditions. In sum, the observation that the N400 is inversely correlated with the P600 on a by-trial basis supports a single stream view, such as Retrieval-Integration theory, and is difficult to reconcile with the processing mechanisms proposed by multi-stream models.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3309-3325"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49617519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning. 基于时空动态和深度学习的跨模态识别的脑启发多感觉整合神经网络

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2023-02-02 DOI: 10.1007/s11571-023-09932-4

Haitao Yu, Quanfa Zhao

{"title":"Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.","authors":"Haitao Yu, Quanfa Zhao","doi":"10.1007/s11571-023-09932-4","DOIUrl":"10.1007/s11571-023-09932-4","url":null,"abstract":"The integration and interaction of cross-modal senses in brain neural networks can facilitate high-level cognitive functionalities. In this work, we proposed a bioinspired multisensory integration neural network (MINN) that integrates visual and audio senses for recognizing multimodal information across different sensory modalities. This deep learning-based model incorporates a cascading framework of parallel convolutional neural networks (CNNs) for extracting intrinsic features from visual and audio inputs, and a recurrent neural network (RNN) for multimodal information integration and interaction. The network was trained using synthetic training data generated for digital recognition tasks. It was revealed that the spatial and temporal features extracted from visual and audio inputs by CNNs were encoded in subspaces orthogonal with each other. In integration epoch, network state evolved along quasi-rotation-symmetric trajectories and a structural manifold with stable attractors was formed in RNN, supporting accurate cross-modal recognition. We further evaluated the robustness of the MINN algorithm with noisy inputs and asynchronous digital inputs. Experimental results demonstrated the superior performance of MINN for flexible integration and accurate recognition of multisensory information with distinct sense properties. The present results provide insights into the computational principles governing multisensory integration and a comprehensive neural network model for brain-inspired intelligence.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3615-3628"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49113589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stability of synchronization manifolds and its nonlinear behaviour in memristive coupled discrete neuron model. 忆阻耦合离散神经元模型中同步流形的稳定性及其非线性行为。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-11-14 DOI: 10.1007/s11571-024-10165-2

Dianavinnarasi Joseph, Suresh Kumarasamy, Sayooj Aby Jose, Karthikeyan Rajagopal

{"title":"Stability of synchronization manifolds and its nonlinear behaviour in memristive coupled discrete neuron model.","authors":"Dianavinnarasi Joseph, Suresh Kumarasamy, Sayooj Aby Jose, Karthikeyan Rajagopal","doi":"10.1007/s11571-024-10165-2","DOIUrl":"10.1007/s11571-024-10165-2","url":null,"abstract":"In this study, we investigate the impact of first and second-order coupling strengths on the stability of a synchronization manifold in a Discrete FitzHugh-Nagumo (DFHN) neuron model with memristor coupling. Master Stability Function (MSF) is used to estimate the stability of the synchronized manifold. The MSF of the DFHN model exhibits two zero crossings as we vary the coupling strengths, which is categorized as class <math><msub><mi>Γ</mi> <mn>2</mn></msub> </math> . Interestingly, both zero-crossing points demonstrate a power-law relationship with respect to both the first-order coupling strength and flux coefficient, as well as the second-order coupling strength and flux coefficient. In contrast, the zero crossings follow a linear relationship between first-order and second-order coupling strength. These linear and nonlinear relationships enable us to forecast the zero-crossing point and, consequently, determine the coupling strengths at which the stability of the synchronization manifold changes for any given set of parameters. We further explore the regime of the stable synchronization manifold within a defined parameter space. Lower values of both first and second-order coupling strengths have minimal impact on the transition between stable and unstable synchronization regimes. Conversely, higher coupling strengths lead to a shrinking regime of the stable synchronization manifold. This reduction follows an exponential relationship with the coupling strengths. This study is helpful in brain-inspired computing systems by understanding synchronization stability in neuron models with memristor coupling. It helps to create more efficient neural networks for tasks like pattern recognition and data processing.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4089-4099"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive learning rate in dynamical binary environments: the signature of adaptive information processing. 动态二元环境下的自适应学习率：自适应信息处理的特征。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-10-21 DOI: 10.1007/s11571-024-10128-7

Changbo Zhu, Ke Zhou, Yandong Tang, Fengzhen Tang, Bailu Si

引用次数: 0

Generative models for sequential dynamics in active inference. 主动推理中序列动力学的生成模型

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2023-04-26 DOI: 10.1007/s11571-023-09963-x

Thomas Parr, Karl Friston, Giovanni Pezzulo

{"title":"Generative models for sequential dynamics in active inference.","authors":"Thomas Parr, Karl Friston, Giovanni Pezzulo","doi":"10.1007/s11571-023-09963-x","DOIUrl":"10.1007/s11571-023-09963-x","url":null,"abstract":"A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This processing in turn emerges from continuous neuronal dynamics. Notable examples are sequences of words during linguistic communication or sequences of locations during navigation. In this perspective, we address the problem of sequential brain processing from the perspective of active inference, which inherits from a Helmholtzian view of the predictive (Bayesian) brain. Underneath the active inference lies a generative model; namely, a probabilistic description of how (observable) consequences are generated by (unobservable) causes. We show that one can account for many aspects of sequential brain processing by assuming the brain entails a generative model of the sensed world that comprises central pattern generators, narratives, or well-defined sequences. We provide examples in the domains of motor control (e.g., handwriting), perception (e.g., birdsong recognition) through to planning and understanding (e.g., language). The solutions to these problems include the use of sequences of attracting points to direct complex movements-and the move from continuous representations of auditory speech signals to the discrete words that generate those signals.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3259-3272"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49649292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust working memory in a two-dimensional continuous attractor network. 二维连续吸引子网络中的鲁棒工作记忆

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2023-05-29 DOI: 10.1007/s11571-023-09979-3

Weronika Wojtak, Stephen Coombes, Daniele Avitabile, Estela Bicho, Wolfram Erlhagen

{"title":"Robust working memory in a two-dimensional continuous attractor network.","authors":"Weronika Wojtak, Stephen Coombes, Daniele Avitabile, Estela Bicho, Wolfram Erlhagen","doi":"10.1007/s11571-023-09979-3","DOIUrl":"10.1007/s11571-023-09979-3","url":null,"abstract":"Continuous bump attractor networks (CANs) have been widely used in the past to explain the phenomenology of working memory (WM) tasks in which continuous-valued information has to be maintained to guide future behavior. Standard CAN models suffer from two major limitations: the stereotyped shape of the bump attractor does not reflect differences in the representational quality of WM items and the recurrent connections within the network require a biologically unrealistic level of fine tuning. We address both challenges in a two-dimensional (2D) network model formalized by two coupled neural field equations of Amari type. It combines the lateral-inhibition-type connectivity of classical CANs with a locally balanced excitatory and inhibitory feedback loop. We first use a radially symmetric connectivity to analyze the existence, stability and bifurcation structure of 2D bumps representing the conjunctive WM of two input dimensions. To address the quality of WM content, we show in model simulations that the bump amplitude reflects the temporal integration of bottom-up and top-down evidence for a specific combination of input features. This includes the network capacity to transform a stable subthreshold memory trace of a weak input into a high fidelity memory representation by an unspecific cue given retrospectively during WM maintenance. To address the fine-tuning problem, we test numerically different perturbations of the assumed radial symmetry of the connectivity function including random spatial fluctuations in the connection strength. Different to the behavior of standard CAN models, the bump does not drift in representational space but remains stationary at the input position.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3273-3289"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43917510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A memristor-based circuit design of avoidance learning with time delay and its application 基于忆阻器的时延回避学习电路设计及其应用

IF 3.7 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-09-19 DOI: 10.1007/s11571-024-10173-2

Junwei Sun, Haojie Wang, Yuanpeng Xu, Peng Liu, Yanfeng Wang

{"title":"A memristor-based circuit design of avoidance learning with time delay and its application","authors":"Junwei Sun, Haojie Wang, Yuanpeng Xu, Peng Liu, Yanfeng Wang","doi":"10.1007/s11571-024-10173-2","DOIUrl":"https://doi.org/10.1007/s11571-024-10173-2","url":null,"abstract":"Currently, the research in memristor-based associative memory neural networks pays more attention to positive stimuli and lays less attention to negative stimuli. Negative stimuli are superior to positive stimuli in some ways, but lack the associated circuit implementation. In this paper, a memristor-based circuit design of avoidance learning with time delay is designed. The circuit can respond to a negative stimulus after initial avoidance learning and the effect of delay time between stimuli is considered. The realization of avoidance learning is confirmed in the PSPICE simulation results. In addition, an extended application circuit based on the memristor-based circuit design of avoidance learning with time delay is proposed. The application circuit is based on the advantage of negative stimuli is more difficult to forget than positive stimuli in associative memory. Based on the features of objects as input, the output of the circuit is used to achieve the function of avoidance learning. The application circuit provides more references for neural networks of automatic driving with further development.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Perceptual information processing in table tennis players: based on top-down hierarchical predictive coding 乒乓球运动员的感知信息处理：基于自上而下的分层预测编码

IF 3.7 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-09-13 DOI: 10.1007/s11571-024-10171-4

Ziyi Peng, Lin Xu, Jie Lian, Xin An, Shufang Chen, Yongcong Shao, Fubing Jiao, Jing Lv

{"title":"Perceptual information processing in table tennis players: based on top-down hierarchical predictive coding","authors":"Ziyi Peng, Lin Xu, Jie Lian, Xin An, Shufang Chen, Yongcong Shao, Fubing Jiao, Jing Lv","doi":"10.1007/s11571-024-10171-4","DOIUrl":"https://doi.org/10.1007/s11571-024-10171-4","url":null,"abstract":"Long-term training induces neural plasticity in the visual cognitive processing cortex of table tennis athletes, who perform cognitive processing in a resource-conserving manner. However, further discussion is needed to determine whether the spatial processing advantage of table tennis players manifests in the early stage of sensory input or the late stage of processing. This study aims to explore the processing styles and neural activity characteristics of table tennis players during spatial cognitive processing. Spatial cognitive tasks were completed by 28 college students and 20 s-level table tennis players, and event-related potentials (ERP) data were recorded during the task. The behavioral results showed that the table tennis group performed better on the task than the college students group (control). The ERP results showed that the amplitude of the N1 component of the table tennis group was significantly lower than that of the control group. The amplitude of the P2 and P3 components of the table tennis group was higher than that of the control group. Table tennis players showed significant synergistic activity between electrodes in the β-band. The results of this study suggest that table tennis players significantly deploy attentional resources and cognitive control. Further, they employ stored motor experience to process spatial information in a hierarchical predictive coding manner.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"1 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The dynamical behavior effects of different numbers of discrete memristive synaptic coupled neurons 不同数量离散记忆性突触耦合神经元的动态行为效应

IF 3.7 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-09-13 DOI: 10.1007/s11571-024-10172-3

Minyuan Cheng, Kaihua Wang, Xianying Xu, Jun Mou

{"title":"The dynamical behavior effects of different numbers of discrete memristive synaptic coupled neurons","authors":"Minyuan Cheng, Kaihua Wang, Xianying Xu, Jun Mou","doi":"10.1007/s11571-024-10172-3","DOIUrl":"https://doi.org/10.1007/s11571-024-10172-3","url":null,"abstract":"Two types of neuron models are constructed in this paper, namely the single discrete memristive synaptic neuron model and the dual discrete memristive synaptic neuron model. Firstly, it is proved that both models have only one unstable equilibrium point. Then, the influence of the coupling strength parameters and neural membrane amplification coefficient of the corresponding system of the two models on the rich dynamical behavior of the systems is analyzed. Research has shown that when the number of discrete local active memristor used as simulation synapses in the system increases from one to two, the coupling strength parameter of the same memristor has significantly different effects on the dynamical behavior of the system within the same range, that is, from a state with periodicity, chaos, and periodicity window to a state with only chaos. In addition, under the influence of coupling strength parameters and neural membrane amplification coefficients, the complexity of the system weakens to varying degrees. Moreover, under the effect of two memristors, the system exhibits a rare and interesting phenomenon where the coupling strength parameter and the neural membrane amplification coefficient can mutually serve as control parameter, resulting in the generation of a remerging Feigenbaum tree. Finally, the pseudo-randomness of the chaotic systems corresponding to the two models are detected by NIST SP800-22, and relevant simulation results are verified on the DSP hardware experimental platform. The discrete memristive synaptic neuron models established in this article provide assistance in studying the relevant working principles of real neurons.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"44 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review 通过深度学习和静息态功能成像生物标记物自动诊断自闭症谱系障碍的进展：系统综述

IF 3.7 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-09-13 DOI: 10.1007/s11571-024-10176-z

Shiza Huda, Danish Mahmood Khan, Komal Masroor, Warda, Ayesha Rashid, Mariam Shabbir

{"title":"Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review","authors":"Shiza Huda, Danish Mahmood Khan, Komal Masroor, Warda, Ayesha Rashid, Mariam Shabbir","doi":"10.1007/s11571-024-10176-z","DOIUrl":"https://doi.org/10.1007/s11571-024-10176-z","url":null,"abstract":"Autism Spectrum Disorder(ASD) is a type of neurological disorder that is common among children. The diagnosis of this disorder at an early stage is the key to reducing its effects. The major symptoms include anxiety, lack of communication, and less social interaction. This paper presents a systematic review conducted based on PRISMA guidelines for automated diagnosis of ASD. With rapid development in the field of Data Science, numerous methods have been proposed that can diagnose the disease at an early stage which can minimize the effects of the disorder. Machine learning and deep learning have proven suitable techniques for the automated diagnosis of ASD. These models have been developed on various datasets such as ABIDE I and ABIDE II, a frequently used dataset based on rs-fMRI images. Approximately 26 articles have been reviewed after the screening process. The paper highlights a comparison between different algorithms used and their accuracy as well. It was observed that most researchers used DL algorithms to develop the ASD detection model. Different accuracies were recorded with a maximum accuracy close to 0.99. Recommendations for future work have also been discussed in a later section. This analysis derived a conclusion that AI-emerged DL and ML technologies can diagnose ASD through rs-fMRI images with maximum accuracy. The comparative analysis has been included to show the accuracy range.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"3 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0