Biological Cybernetics最新文献_第8页

Contrast independent biologically inspired translational optic flow estimation. 对比独立的生物启发平移光流估计。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 DOI: 10.1007/s00422-022-00948-3

Phillip S M Skelton, Anthony Finn, Russell S A Brinkworth

{"title":"Contrast independent biologically inspired translational optic flow estimation.","authors":"Phillip S M Skelton, Anthony Finn, Russell S A Brinkworth","doi":"10.1007/s00422-022-00948-3","DOIUrl":"https://doi.org/10.1007/s00422-022-00948-3","url":null,"abstract":"The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950's, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"116 5-6","pages":"635-660"},"PeriodicalIF":1.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10382668","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: 0

Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex. 前额皮质储层模型中导航过程中位置细胞激活的速度依赖时空结构整合。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 DOI: 10.1007/s00422-022-00945-6

Pablo Scleidorovich, Alfredo Weitzenfeld, Jean-Marc Fellous, Peter Ford Dominey

{"title":"Integration of velocity-dependent spatio-temporal structure of place cell activation during navigation in a reservoir model of prefrontal cortex.","authors":"Pablo Scleidorovich, Alfredo Weitzenfeld, Jean-Marc Fellous, Peter Ford Dominey","doi":"10.1007/s00422-022-00945-6","DOIUrl":"https://doi.org/10.1007/s00422-022-00945-6","url":null,"abstract":"Sequential behavior unfolds both in space and in time. The same spatial trajectory can be realized in different manners in the same overall time by changing instantaneous speeds. The current research investigates how speed profiles might be given behavioral significance and how cortical networks might encode this information. We first demonstrate that rats can associate different speed patterns on the same trajectory with distinct behavioral choices. In this novel experimental paradigm, rats follow a small baited robot in a large megaspace environment where the rat's speed is precisely controlled by the robot's speed. Based on this proof of concept and research showing that recurrent reservoir networks are ideal for representing spatio-temporal structures, we then test reservoir networks in simulated navigation contexts and demonstrate they can discriminate between traversals of the same path with identical durations but different speed profiles. We then test the networks in an embodied robotic setup, where we use place cell representations from physically navigating robots as input and again successfully discriminate between traversals. To demonstrate that this capability is inherent to recurrent networks, we compared the model against simple linear integrators. Interestingly, although the linear integrators could also perform the speed profile discrimination, a clear difference emerged when examining information coding in both models. Reservoir neurons displayed a form of statistical mixed selectivity as a complex interaction between spatial location and speed that was not as abundant in the linear integrators. This mixed selectivity is characteristic of cortex and reservoirs and allows us to generate specific predictions about the neural activity that will be recorded in rat cortex in future experiments.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"116 5-6","pages":"585-610"},"PeriodicalIF":1.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10322962","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: 0

Bursting hierarchy in an adaptive exponential integrate-and-fire network synchronization. 自适应指数型集火网络同步中的爆发层次。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 DOI: 10.1007/s00422-022-00942-9

Congping Lin, Xiaoyue Wu, Yiwei Zhang

引用次数: 0

Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems. 具有硬边界的极限循环的变量和相位响应分析，并应用于神经机械控制问题。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2022-12-01 Epub Date: 2022-11-18 DOI: 10.1007/s00422-022-00951-8

Yangyang Wang, Jeffrey P Gill, Hillel J Chiel, Peter J Thomas

{"title":"Variational and phase response analysis for limit cycles with hard boundaries, with applications to neuromechanical control problems.","authors":"Yangyang Wang, Jeffrey P Gill, Hillel J Chiel, Peter J Thomas","doi":"10.1007/s00422-022-00951-8","DOIUrl":"10.1007/s00422-022-00951-8","url":null,"abstract":"Motor systems show an overall robustness, but because they are highly nonlinear, understanding how they achieve robustness is difficult. In many rhythmic systems, robustness against perturbations involves response of both the shape and the timing of the trajectory. This makes the study of robustness even more challenging. To understand how a motor system produces robust behaviors in a variable environment, we consider a neuromechanical model of motor patterns in the feeding apparatus of the marine mollusk Aplysia californica (Shaw et al. in J Comput Neurosci 38(1):25-51, 2015; Lyttle et al. in Biol Cybern 111(1):25-47, 2017). We established in (Wang et al. in SIAM J Appl Dyn Syst 20(2):701-744, 2021. https://doi.org/10.1137/20M1344974 ) the tools for studying combined shape and timing responses of limit cycle systems under sustained perturbations and here apply them to study robustness of the neuromechanical model against increased mechanical load during swallowing. Interestingly, we discover that nonlinear biomechanical properties confer resilience by immediately increasing resistance to applied loads. In contrast, the effect of changed sensory feedback signal is significantly delayed by the firing rates' hard boundary properties. Our analysis suggests that sensory feedback contributes to robustness in swallowing primarily by shifting the timing of neural activation involved in the power stroke of the motor cycle (retraction). This effect enables the system to generate stronger retractor muscle forces to compensate for the increased load, and hence achieve strong robustness. The approaches that we are applying to understanding a neuromechanical model in Aplysia, and the results that we have obtained, are likely to provide insights into the function of other motor systems that encounter changing mechanical loads and hard boundaries, both due to mechanical and neuronal firing properties.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"116 5-6","pages":"687-710"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9691512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9129068","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: 0

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":" ","pages":"389-406"},"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":" ","pages":"475-499"},"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