Cognitive Neurodynamics最新文献_第10页

Cortex level connectivity between ACT-R modules during EEG-based n-back task. 在基于脑电图的n-back任务中，ACT-R模块之间的皮质水平连接。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-10-21 DOI: 10.1007/s11571-024-10177-y

Debashis Das Chakladar

{"title":"Cortex level connectivity between ACT-R modules during EEG-based n-back task.","authors":"Debashis Das Chakladar","doi":"10.1007/s11571-024-10177-y","DOIUrl":"10.1007/s11571-024-10177-y","url":null,"abstract":"Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based n-back task (for validating working memory performance). Initially, the source localization method is performed on the EEG signal, and the mapping between ACT-R modules and corresponding brain scouts (on the cortex surface) is performed. Once the brain scouts are identified for ACT-R modules, then those scouts are called ACT-R scouts. The linear (Granger Causality: GC) and non-linear effective connectivity (Multivariate Transfer Entropy: MTE) methods are applied over the scouts' time series data. From the GC and MTE analysis, for all n-back tasks, information flow is observed from the visual-to-imaginal ACT-R scout for storing the visual stimuli (i.e., input letter) in short-term memory. For 2 and 3-back tasks, causal flow exists from imaginal to retrieval ACT-R scout and vice-versa. Causal flow from procedural to the imaginal ACT-R scout is also observed for all workload levels to execute the set of productions. Identifying the relationship among ACT-R modules through scout-level connectivity in the cortical surface facilitates the effects of human cognition in terms of brain dynamics.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4033-4045"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876455","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

Correction to: Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory. 修正：利用多通道脑电图信号和粗糙集理论优化精神分裂症检测的特征子集。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-10-04 DOI: 10.1007/s11571-024-10179-w

Sridevi Srinivasan, Shiny Duela Johnson

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Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought. 超越神经元和尖峰：cognon，层次动态的思维单位

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2023-07-11 DOI: 10.1007/s11571-023-09987-3

Mikhail Rabinovich, Christian Bick, Pablo Varona

{"title":"Beyond neurons and spikes: cognon, the hierarchical dynamical unit of thought.","authors":"Mikhail Rabinovich, Christian Bick, Pablo Varona","doi":"10.1007/s11571-023-09987-3","DOIUrl":"10.1007/s11571-023-09987-3","url":null,"abstract":"From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term \"cognon\". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3327-3335"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44622024","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}

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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

Collective behavior of an adapting synapse-based neuronal network with memristive effect and randomness. 具有记忆效应和随机性的自适应突触神经网络的集体行为。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2024-12-01 Epub Date: 2024-11-12 DOI: 10.1007/s11571-024-10178-x

Vinoth Seralan, D Chandrasekhar, Sarasu Pakiriswamy, Karthikeyan Rajagopal

{"title":"Collective behavior of an adapting synapse-based neuronal network with memristive effect and randomness.","authors":"Vinoth Seralan, D Chandrasekhar, Sarasu Pakiriswamy, Karthikeyan Rajagopal","doi":"10.1007/s11571-024-10178-x","DOIUrl":"10.1007/s11571-024-10178-x","url":null,"abstract":"This study delves into the examination of a network of adaptive synapse neurons characterized by a small-world network topology connected through electromagnetic flux and infused with randomness. First, this research extensively explores the existence of the global multi-stability of a single adaptive synapse-based neuron model with magnetic flux. The non-autonomous neuron model exhibits periodically switchable equilibrium states that are strongly related to the transitions between stable and unstable points in every whole periodic cycle, leading to the creation of global multi-stability. Various numerical measures, including bifurcation plots, phase plots, and basin of attraction, illustrate the intricate dynamics of diverse coexisting global firing activities. Moreover, the model is extended by coupling two neurons with a memristive synapse. The dynamics of the coupled neurons model are showcased with the help of largest Lyapunov exponents, and synchronized dynamics are viewed with the help of mean average error. Next, we consider a regular network of neurons connected to their nearest neighbors through the memristive synapse. We then reconstruct it into a small-world network by increasing the randomness in the rewiring links. Consequently, we observed collective behavior influenced by the number of neighborhood connections, coupling strength, and rewiring probability. We used spatio-temporal patterns, recurrence plots, as well as global-order parameters to verify the reported results.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4071-4087"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876431","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}

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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