Biological Cybernetics最新文献_第3页

Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica. 水蚤 I1/I3 卷缩肌复合体的全希尔型肌肉模型。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-06-26 DOI: 10.1007/s00422-024-00990-3

Ravesh Sukhnandan, Qianxue Chen, Jiayi Shen, Samantha Pao, Yu Huan, Gregory P Sutton, Jeffrey P Gill, Hillel J Chiel, Victoria A Webster-Wood

{"title":"Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica.","authors":"Ravesh Sukhnandan, Qianxue Chen, Jiayi Shen, Samantha Pao, Yu Huan, Gregory P Sutton, Jeffrey P Gill, Hillel J Chiel, Victoria A Webster-Wood","doi":"10.1007/s00422-024-00990-3","DOIUrl":"10.1007/s00422-024-00990-3","url":null,"abstract":"The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems' neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia's grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia's grasper, has not been characterized. Because of the importance of the musculature's properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior of Aplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle's peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex's kinematics in vivo.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"165-185"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452161","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

How the brain can be trained to achieve an intermittent control strategy for stabilizing quiet stance by means of reinforcement learning. 如何通过强化学习训练大脑实现稳定安静姿态的间歇控制策略。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-07-12 DOI: 10.1007/s00422-024-00993-0

Tomoki Takazawa, Yasuyuki Suzuki, Akihiro Nakamura, Risa Matsuo, Pietro Morasso, Taishin Nomura

{"title":"How the brain can be trained to achieve an intermittent control strategy for stabilizing quiet stance by means of reinforcement learning.","authors":"Tomoki Takazawa, Yasuyuki Suzuki, Akihiro Nakamura, Risa Matsuo, Pietro Morasso, Taishin Nomura","doi":"10.1007/s00422-024-00993-0","DOIUrl":"10.1007/s00422-024-00993-0","url":null,"abstract":"The stabilization of human quiet stance is achieved by a combination of the intrinsic elastic properties of ankle muscles and an active closed-loop activation of the ankle muscles, driven by the delayed feedback of the ongoing sway angle and the corresponding angular velocity in a way of a delayed proportional (P) and derivative (D) feedback controller. It has been shown that the active component of the stabilization process is likely to operate in an intermittent manner rather than as a continuous controller: the switching policy is defined in the phase-plane, which is divided in dangerous and safe regions, separated by appropriate switching boundaries. When the state enters a dangerous region, the delayed PD control is activated, and it is switched off when it enters a safe region, leaving the system to evolve freely. In comparison with continuous feedback control, the intermittent mechanism is more robust and capable to better reproduce postural sway patterns in healthy people. However, the superior performance of the intermittent control paradigm as well as its biological plausibility, suggested by experimental evidence of the intermittent activation of the ankle muscles, leaves open the quest of a feasible learning process, by which the brain can identify the appropriate state-dependent switching policy and tune accordingly the P and D parameters. In this work, it is shown how such a goal can be achieved with a reinforcement motor learning paradigm, building upon the evidence that, in general, the basal ganglia are known to play a central role in reinforcement learning for action selection and, in particular, were found to be specifically involved in postural stabilization.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"229-248"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592149","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

COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator. 呼吸节律发生器最小闭环模型中的 COVID-19 和无声低氧血症。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-06-17 DOI: 10.1007/s00422-024-00989-w

Casey O Diekman, Peter J Thomas, Christopher G Wilson

{"title":"COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator.","authors":"Casey O Diekman, Peter J Thomas, Christopher G Wilson","doi":"10.1007/s00422-024-00989-w","DOIUrl":"10.1007/s00422-024-00989-w","url":null,"abstract":"Silent hypoxemia, or \"happy hypoxia,\" is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation ( <math><msub><mtext>SaO</mtext> <mn>2</mn></msub> </math> < 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194-2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or the nucleus tractus solitarii are responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"145-163"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332591","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

A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network. 一个计算神经模型，其中包含蜻蜓摄食网络的内在动力和感觉反馈。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-05-20 DOI: 10.1007/s00422-024-00991-2

Yanjun Li, Victoria A Webster-Wood, Jeffrey P Gill, Gregory P Sutton, Hillel J Chiel, Roger D Quinn

{"title":"A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network.","authors":"Yanjun Li, Victoria A Webster-Wood, Jeffrey P Gill, Gregory P Sutton, Hillel J Chiel, Roger D Quinn","doi":"10.1007/s00422-024-00991-2","DOIUrl":"10.1007/s00422-024-00991-2","url":null,"abstract":"Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"187-213"},"PeriodicalIF":1.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072146","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

Neural coding of space by time. 时间对空间的神经编码

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-06-07 DOI: 10.1007/s00422-024-00992-1

Hubert Löffler, Daya Shankar Gupta, Andreas Bahmer

引用次数: 0

Controlling flat-foot limit cycle walkers with compliant joints based on local stability variation 基于局部稳定性变化控制带有顺应性关节的平足极限循环行走装置

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-04-20 DOI: 10.1007/s00422-024-00987-y

Yan Huang, Yue Gao, Qiang Huang, Qining Wang

引用次数: 0

Empirical modeling and prediction of neuronal dynamics 神经元动力学的经验建模和预测

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-04-10 DOI: 10.1007/s00422-024-00986-z

Pau Fisco-Compte, David Aquilué-Llorens, Nestor Roqueiro, Enric Fossas, Antoni Guillamon

{"title":"Empirical modeling and prediction of neuronal dynamics","authors":"Pau Fisco-Compte, David Aquilué-Llorens, Nestor Roqueiro, Enric Fossas, Antoni Guillamon","doi":"10.1007/s00422-024-00986-z","DOIUrl":"https://doi.org/10.1007/s00422-024-00986-z","url":null,"abstract":"Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"5 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564782","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

Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity 波动中的稳定性：对突触可塑性随机模型中的缩放、分岔和自发对称性破坏的二维研究

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-04-07 DOI: 10.1007/s00422-024-00985-0

{"title":"Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity","authors":"","doi":"10.1007/s00422-024-00985-0","DOIUrl":"https://doi.org/10.1007/s00422-024-00985-0","url":null,"abstract":"<h3>Abstract</h3> Stochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of spike-timing-dependent plasticity (STDP) acts as a temperature-like parameter, exhibiting a critical value below which neuronal structure formation occurs. The filter threshold scales this temperature-like parameter downwards, cooling the dynamics and enhancing stability. A key step in this calculation was a resetting approximation, essentially reducing the dynamics to one-dimensional processes. Here, we revisit our earlier study to examine this resetting approximation, with the aim of understanding in detail why it works so well by comparing it, and a simpler approximation, to the system’s full dynamics consisting of various embedded two-dimensional processes without resetting. Comparing the full system to the simpler approximation, to our original resetting approximation, and to a one-afferent system, we show that their equilibrium distributions of synaptic strengths and critical plasticity step sizes are all qualitatively similar, and increasingly quantitatively similar as the filter threshold increases. This increasing similarity is due to the decorrelation in changes in synaptic strength between different afferents caused by our STDP model, and the amplification of this decorrelation with larger synaptic filters.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"49 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140564829","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

What have we learned about artificial intelligence from studying the brain? 我们从研究大脑中学到了哪些关于人工智能的知识？

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-04-01 Epub Date: 2024-02-10 DOI: 10.1007/s00422-024-00983-2

Samuel J Gershman

引用次数: 0

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI. 用于运动意象 BCI 的快速多变量经验模式分解的脑电图节律分离和时频分析。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-04-01 Epub Date: 2024-03-12 DOI: 10.1007/s00422-024-00984-1

Yang Jiao, Qian Zheng, Dan Qiao, Xun Lang, Lei Xie, Yi Pan

{"title":"EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.","authors":"Yang Jiao, Qian Zheng, Dan Qiao, Xun Lang, Lei Xie, Yi Pan","doi":"10.1007/s00422-024-00984-1","DOIUrl":"10.1007/s00422-024-00984-1","url":null,"abstract":"Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"21-37"},"PeriodicalIF":1.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112301","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