Biological Cybernetics最新文献_第2页

Neuroscientific insights about computer vision models: a concise review. 关于计算机视觉模型的神经科学见解：简明综述。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-01 Epub Date: 2024-10-09 DOI: 10.1007/s00422-024-00998-9

Seba Susan

{"title":"Neuroscientific insights about computer vision models: a concise review.","authors":"Seba Susan","doi":"10.1007/s00422-024-00998-9","DOIUrl":"10.1007/s00422-024-00998-9","url":null,"abstract":"The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"331-348"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395516","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

Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data? 类似希比安的学习规则能否避免稀疏分布式数据中的维度诅咒？

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-01 Epub Date: 2024-09-09 DOI: 10.1007/s00422-024-00995-y

Maria Osório, Luis Sa-Couto, Andreas Wichert

{"title":"Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?","authors":"Maria Osório, Luis Sa-Couto, Andreas Wichert","doi":"10.1007/s00422-024-00995-y","DOIUrl":"10.1007/s00422-024-00995-y","url":null,"abstract":"It is generally assumed that the brain uses something akin to sparse distributed representations. These representations, however, are high-dimensional and consequently they affect classification performance of traditional Machine Learning models due to the \"curse of dimensionality\". In tasks for which there is a vast amount of labeled data, Deep Networks seem to solve this issue with many layers and a non-Hebbian backpropagation algorithm. The brain, however, seems to be able to solve the problem with few layers. In this work, we hypothesize that this happens by using Hebbian learning. Actually, the Hebbian-like learning rule of Restricted Boltzmann Machines learns the input patterns asymmetrically. It exclusively learns the correlation between non-zero values and ignores the zeros, which represent the vast majority of the input dimensionality. By ignoring the zeros the \"curse of dimensionality\" problem can be avoided. To test our hypothesis, we generated several sparse datasets and compared the performance of a Restricted Boltzmann Machine classifier with some Backprop-trained networks. The experiments using these codes confirm our initial intuition as the Restricted Boltzmann Machine shows a good generalization performance, while the Neural Networks trained with the backpropagation algorithm overfit the training data.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"267-276"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156679","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

Astrocyte-mediated neuronal irregularities and dynamics: the complexity of the tripartite synapse 星形胶质细胞介导的神经元不规则性和动力学：三方突触的复杂性

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-09-14 DOI: 10.1007/s00422-024-00994-z

Den Whilrex Garcia, Sabir Jacquir

{"title":"Astrocyte-mediated neuronal irregularities and dynamics: the complexity of the tripartite synapse","authors":"Den Whilrex Garcia, Sabir Jacquir","doi":"10.1007/s00422-024-00994-z","DOIUrl":"https://doi.org/10.1007/s00422-024-00994-z","url":null,"abstract":"Despite significant advancements in recent decades, gaining a comprehensive understanding of brain computations remains a significant challenge in neuroscience. Using computational models is crucial for unraveling this complex phenomenon and is equally indispensable for studying neurological disorders. This endeavor has created many neuronal models that capture brain dynamics at various scales and complexities. However, most existing models do not account for the potential influence of glial cells, particularly astrocytes, on neuronal physiology. This gap persists even with the emerging evidence indicating their critical role in regulating neural network activity, plasticity, and even neurological pathologies. To address this gap, some works proposed models that include neuron–glia interactions. Also, while some literature focuses on sophisticated models of neuron–glia interactions that mimic the complexity of physiological phenomena, there are also existing works that propose simplified models of neural–glial ensembles. Building upon these efforts, we aimed to contribute further to the field by proposing a simplified tripartite synapse model that encompasses the presynaptic neuron, postsynaptic neuron, and astrocyte. We defined the tripartite synapse model based on the Adaptive Exponential Integrate-and-Fire neuron model and a simplified scheme of the astrocyte model previously proposed by Postnov. Through our simulations, we demonstrated how astrocytes can influence neuronal firing behavior by sequentially activating and deactivating different pathways within the tripartite synapse. This modulation by astrocytes can shape neuronal behavior and introduce irregularities in the firing patterns of both presynaptic and postsynaptic neurons through the introduction of new pathways and configurations of relevant parameters.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"314 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142252866","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

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