Biological Cybernetics最新文献_第7页

Comparison of event-related modulation index and traditional methods for evaluating phase-amplitude coupling using simulated brain signals. 事件相关调制指数与利用模拟脑信号评价相幅耦合的传统方法的比较。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 DOI: 10.1007/s00422-022-00944-7

Chung-Chieh Tsai, Hong-Hsiang Liu, Yi-Li Tseng

引用次数: 1

Correction: Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning. 修正:基于模型预测控制和深度强化学习的肌肉骨骼系统的最佳轨迹学习。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 DOI: 10.1007/s00422-022-00947-4

Berat Denizdurduran, Henry Markram, Marc-Oliver Gewaltig

引用次数: 0

Extreme Image Transformations Affect Humans and Machines Differently 极端图像变换对人类和机器的影响是不同的

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-11-30 DOI: 10.48550/arXiv.2212.13967

Girik Malik, Dakarai Crowder, E. Mingolla

引用次数: 2

Autoencoders reloaded. Autoencoders reloaded。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-08-01 Epub Date: 2022-06-21 DOI: 10.1007/s00422-022-00937-6

Hervé Bourlard, Selen Hande Kabil

{"title":"Autoencoders reloaded.","authors":"Hervé Bourlard, Selen Hande Kabil","doi":"10.1007/s00422-022-00937-6","DOIUrl":"https://doi.org/10.1007/s00422-022-00937-6","url":null,"abstract":"In Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), it was theoretically proven that autoencoders (AE) with single hidden layer (previously called \"auto-associative multilayer perceptrons\") were, in the best case, implementing singular value decomposition (SVD) Golub and Reinsch (Linear algebra, Singular value decomposition and least squares solutions, pp 134-151. Springer, 1971), equivalent to principal component analysis (PCA) Hotelling (Educ Psychol 24(6/7):417-441, 1993); Jolliffe (Principal component analysis, springer series in statistics, 2nd edn. Springer, New York ). That is, AE are able to derive the eigenvalues that represent the amount of variance covered by each component even with the presence of the nonlinear function (sigmoid-like, or any other nonlinear functions) present on their hidden units. Today, with the renewed interest in \"deep neural networks\" (DNN), multiple types of (deep) AE are being investigated as an alternative to manifold learning Cayton (Univ California San Diego Tech Rep 12(1-17):1, 2005) for conducting nonlinear feature extraction or fusion, each with its own specific (expected) properties. Many of those AE are currently being developed as powerful, nonlinear encoder-decoder models, or used to generate reduced and discriminant feature sets that are more amenable to different modeling and classification tasks. In this paper, we start by recalling and further clarifying the main conclusions of Bourlard and Kamp (Biol Cybern 59(4):291-294, 1998), supporting them by extensive empirical evidences, which were not possible to be provided previously (in 1988), due to the dataset and processing limitations. Upon full understanding of the underlying mechanisms, we show that it remains hard (although feasible) to go beyond the state-of-the-art PCA/SVD techniques for auto-association. Finally, we present a brief overview on different autoencoder models that are mainly in use today and discuss their rationale, relations and application areas.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287259/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40121803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 6

Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation. 听觉皮层建模为振荡的动态网络:理解事件相关领域及其适应。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-08-01 Epub Date: 2022-06-20 DOI: 10.1007/s00422-022-00936-7

Aida Hajizadeh, Artur Matysiak, Matthias Wolfrum, Patrick J C May, Reinhard König

{"title":"Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation.","authors":"Aida Hajizadeh, Artur Matysiak, Matthias Wolfrum, Patrick J C May, Reinhard König","doi":"10.1007/s00422-022-00936-7","DOIUrl":"https://doi.org/10.1007/s00422-022-00936-7","url":null,"abstract":"Adaptation, the reduction of neuronal responses by repetitive stimulation, is a ubiquitous feature of auditory cortex (AC). It is not clear what causes adaptation, but short-term synaptic depression (STSD) is a potential candidate for the underlying mechanism. In such a case, adaptation can be directly linked with the way AC produces context-sensitive responses such as mismatch negativity and stimulus-specific adaptation observed on the single-unit level. We examined this hypothesis via a computational model based on AC anatomy, which includes serially connected core, belt, and parabelt areas. The model replicates the event-related field (ERF) of the magnetoencephalogram as well as ERF adaptation. The model dynamics are described by excitatory and inhibitory state variables of cell populations, with the excitatory connections modulated by STSD. We analysed the system dynamics by linearising the firing rates and solving the STSD equation using time-scale separation. This allows for characterisation of AC dynamics as a superposition of damped harmonic oscillators, so-called normal modes. We show that repetition suppression of the N1m is due to a mixture of causes, with stimulus repetition modifying both the amplitudes and the frequencies of the normal modes. In this view, adaptation results from a complete reorganisation of AC dynamics rather than a reduction of activity in discrete sources. Further, both the network structure and the balance between excitation and inhibition contribute significantly to the rate with which AC recovers from adaptation. This lifetime of adaptation is longer in the belt and parabelt than in the core area, despite the time constants of STSD being spatially homogeneous. Finally, we critically evaluate the use of a single exponential function to describe recovery from adaptation.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39999787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 3

Codimension-2 parameter space structure of continuous-time recurrent neural networks. 连续时间递归神经网络的协维-2参数空间结构。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-08-01 Epub Date: 2022-06-20 DOI: 10.1007/s00422-022-00938-5

Randall D Beer

引用次数: 2

Dissecting cell fate dynamics in pediatric glioblastoma through the lens of complex systems and cellular cybernetics 通过复杂系统和细胞控制论的视角解剖小儿胶质母细胞瘤的细胞命运动力学

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-06-09 DOI: 10.1007/s00422-022-00935-8

A. Uthamacumaran

引用次数: 1

Optimal reaching trajectories based on feedforward control 基于前馈控制的最优到达轨迹

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-06-04 DOI: 10.1007/s00422-022-00939-4

Y. Taniai, T. Naniwa, J. Nishii

引用次数: 2

Sensory feedback expands dynamic complexity and aids in robustness against noise. 感官反馈扩大了动态复杂性，并有助于对噪声的鲁棒性。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-06-01 Epub Date: 2022-01-04 DOI: 10.1007/s00422-021-00917-2

Alexander J White

引用次数: 0

A review of motor neural system robotic modeling approaches and instruments. 运动神经系统机器人建模方法和仪器综述。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-06-01 Epub Date: 2022-01-18 DOI: 10.1007/s00422-021-00918-1

Alexander S Migalev, Kristina D Vigasina, Pavel M Gotovtsev

{"title":"A review of motor neural system robotic modeling approaches and instruments.","authors":"Alexander S Migalev, Kristina D Vigasina, Pavel M Gotovtsev","doi":"10.1007/s00422-021-00918-1","DOIUrl":"https://doi.org/10.1007/s00422-021-00918-1","url":null,"abstract":"In this review, we are considering an actively developing tool in neuroscience-robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39691350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1