Biological Cybernetics最新文献_第2页

Efficient stochastic simulation of piecewise-deterministic Markov processes and its application to the Morris-Lecar model of neural dynamics. 分段确定性马尔可夫过程的高效随机模拟及其在神经动力学Morris-Lecar模型中的应用。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2025-01-24 DOI: 10.1007/s00422-025-01004-6

Arkady Pikovsky

引用次数: 0

Action of the Euclidean versus projective group on an agent's internal space in curiosity driven exploration. 好奇驱动探索中欧几里得对投影群对主体内部空间的作用。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2025-01-17 DOI: 10.1007/s00422-024-01001-1

Grégoire Sergeant-Perthuis, Nils Ruet, Dimitri Ognibene, Yvain Tisserand, Kenneth Williford, David Rudrauf

{"title":"Action of the Euclidean versus projective group on an agent's internal space in curiosity driven exploration.","authors":"Grégoire Sergeant-Perthuis, Nils Ruet, Dimitri Ognibene, Yvain Tisserand, Kenneth Williford, David Rudrauf","doi":"10.1007/s00422-024-01001-1","DOIUrl":"10.1007/s00422-024-01001-1","url":null,"abstract":"According to the Projective Consciousness Model (PCM), in human spatial awareness, 3-dimensional projective geometry structures information integration and action planning through perspective taking within an internal representation space. The way different perspectives are related to and transform a world model defines a specific perception and imagination scheme. In mathematics, such a collection of transformations corresponds to a 'group', whose 'actions' characterize the geometry of a space. Imbuing world models with a group structure may capture different agents' spatial awareness and affordance schemes. We used group action as a special class of policies for perspective-dependent control. We explored how such a geometric structure impacts agents' behaviors, comparing how the Euclidean versus projective groups act on epistemic value in active inference, drive curiosity, and exploration. We formally demonstrate and simulate how the groups induce distinct behaviors in a simple search task. The projective group's nonlinear magnification of information transformed epistemic value according to the choice of frame, generating behaviors of approach toward objects with uncertain locations due to limited sampling. The Euclidean group had no effect on epistemic value: no action was better than the initial idle state. In structuring a priori an agent's internal representation, we show how geometry can play a key role in information integration and action planning. Our results add further support to the PCM.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"119 1","pages":"4"},"PeriodicalIF":1.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11742296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016932","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

Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings. 从电压记录中提取具有自适应的随机积分-火灾模型参数。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-30 DOI: 10.1007/s00422-024-01000-2

Lilli Kiessling, Benjamin Lindner

{"title":"Extraction of parameters of a stochastic integrate-and-fire model with adaptation from voltage recordings.","authors":"Lilli Kiessling, Benjamin Lindner","doi":"10.1007/s00422-024-01000-2","DOIUrl":"10.1007/s00422-024-01000-2","url":null,"abstract":"Integrate-and-fire models are an important class of phenomenological neuronal models that are frequently used in computational studies of single neural activity, population activity, and recurrent neural networks. If these models are used to understand and interpret electrophysiological data, it is important to reliably estimate the values of the model's parameters. However, there are no standard methods for the parameter estimation of Integrate-and-fire models. Here, we identify the model parameters of an adaptive integrate-and-fire neuron with temporally correlated noise by analyzing membrane potential and spike trains in response to a current step. Explicit formulas for the parameters are analytically derived by stationary and time-dependent ensemble averaging of the model dynamics. Specifically, we give mathematical expressions for the adaptation time constant, the adaptation strength, the membrane time constant, and the mean constant input current. These theoretical predictions are validated by numerical simulations for a broad range of system parameters. Importantly, we demonstrate that parameters can be extracted by using only a modest number of trials. This is particularly encouraging, as the number of trials in experimental settings is often limited. Hence, our formulas may be useful for the extraction of effective parameters from neurophysiological data obtained from standard current-step experiments.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":"119 1","pages":"2"},"PeriodicalIF":1.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11685267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911260","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

Would you publish unrealistic models? 你会发表不切实际的模型吗？

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-30 DOI: 10.1007/s00422-024-00999-8

Damien Depannemaecker

引用次数: 0

Beyond the Nobel prizes: towards new synergies between Computational Neuroscience and Artificial Intelligence. 超越诺贝尔奖：迈向计算神经科学与人工智能之间的新协同效应。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-27 DOI: 10.1007/s00422-024-01002-0

Jean-Marc Fellous, Peter Thomas, Paul Tiesinga, Benjamin Lindner

引用次数: 0

Phase response curves and the role of coordinates. 相位响应曲线和坐标的作用。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-01 Epub Date: 2024-10-30 DOI: 10.1007/s00422-024-00997-w

Simon Wilshin, Matthew D Kvalheim, Shai Revzen

引用次数: 0

Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems. 对动力行程恢复系统中的感觉反馈机制进行变量分析。

IF 1.7 4区工程技术

Biological Cybernetics Pub Date : 2024-12-01 Epub Date: 2024-09-09 DOI: 10.1007/s00422-024-00996-x

Zhuojun Yu, Peter J Thomas

{"title":"Variational analysis of sensory feedback mechanisms in powerstroke-recovery systems.","authors":"Zhuojun Yu, Peter J Thomas","doi":"10.1007/s00422-024-00996-x","DOIUrl":"10.1007/s00422-024-00996-x","url":null,"abstract":"Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke-recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate-such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance-sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation-inhibition property of feedback mechanisms determines the sensitivity pattern while the activation-inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":" ","pages":"277-309"},"PeriodicalIF":1.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156680","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

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