Biological Cybernetics最新文献_第2页

A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia. 与帕金森病痴呆症有关的基底神经节-皮层并行回路动态计算模型。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-04-21 DOI: 10.1007/s00422-024-00988-x

Haodong Yang, XiaoLi Yang, SiLu Yan

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":null,"pages":null},"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":null,"pages":null},"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 1.9 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 1.9 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":null,"pages":null},"PeriodicalIF":1.9,"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

Fluctuation-response relations for integrate-and-fire models with an absolute refractory period. 具有绝对耐火期的积分燃烧模型的波动-响应关系。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2024-04-01 Epub Date: 2024-01-23 DOI: 10.1007/s00422-023-00982-9

Friedrich Puttkammer, Benjamin Lindner

引用次数: 0

Energy optimisation predicts the capacity of ion buffering in the brain 能量优化可预测大脑离子缓冲能力

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-12-16 DOI: 10.1007/s00422-023-00980-x

Reinoud Maex

{"title":"Energy optimisation predicts the capacity of ion buffering in the brain","authors":"Reinoud Maex","doi":"10.1007/s00422-023-00980-x","DOIUrl":"https://doi.org/10.1007/s00422-023-00980-x","url":null,"abstract":"Neurons store energy in the ionic concentration gradients they build across their cell membrane. The amount of energy stored, and hence the work the ions can do by mixing, can be enhanced by the presence of ion buffers in extra- and intracellular space. Buffers act as sources and sinks of ions, however, and unless the buffering capacities for different ion species obey certain relationships, a complete mixing of the ions may be impeded by the physical conditions of charge neutrality and isotonicity. From these conditions, buffering capacities were calculated that enabled each ion species to mix completely. In all valid buffer distributions, the (hbox {Ca}^{2+}) ions were buffered most, with a capacity exceeding that of (hbox {Na}^+) and (hbox {K}^+) buffering by at least an order of magnitude. The similar magnitude of the (oppositely directed) (hbox {Na}^+) and (hbox {K}^+) gradients made extracellular space behave as a (hbox {Na}^+)–(hbox {K}^+) exchanger. Anions such as (hbox {Cl}^-) were buffered least. The great capacity of the extra- and intracellular (hbox {Ca}^{2+}) buffers caused a large influx of (hbox {Ca}^{2+}) ions as is typically observed during energy deprivation. These results explain many characteristics of the physiological buffer distributions but raise the question how the brain controls the capacity of its ion buffers. It is suggested that neurons and glial cells, by their great sensitivity to gradients of charge and osmolarity, respectively, sense deviations from electro-neutral and isotonic mixing, and use these signals to tune the chemical composition, and buffering capacity, of the extra- and intracellular matrices.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138690201","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

Multistability in neural systems with random cross-connections. 具有随机交叉连接的神经系统的多稳定性

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-12-01 Epub Date: 2023-12-22 DOI: 10.1007/s00422-023-00981-w

Jordan Breffle, Subhadra Mokashe, Siwei Qiu, Paul Miller

{"title":"Multistability in neural systems with random cross-connections.","authors":"Jordan Breffle, Subhadra Mokashe, Siwei Qiu, Paul Miller","doi":"10.1007/s00422-023-00981-w","DOIUrl":"10.1007/s00422-023-00981-w","url":null,"abstract":"Neural circuits with multiple discrete attractor states could support a variety of cognitive tasks according to both empirical data and model simulations. We assess the conditions for such multistability in neural systems using a firing rate model framework, in which clusters of similarly responsive neurons are represented as single units, which interact with each other through independent random connections. We explore the range of conditions in which multistability arises via recurrent input from other units while individual units, typically with some degree of self-excitation, lack sufficient self-excitation to become bistable on their own. We find many cases of multistability-defined as the system possessing more than one stable fixed point-in which stable states arise via a network effect, allowing subsets of units to maintain each others' activity because their net input to each other when active is sufficiently positive. In terms of the strength of within-unit self-excitation and standard deviation of random cross-connections, the region of multistability depends on the response function of units. Indeed, multistability can arise with zero self-excitation, purely through zero-mean random cross-connections, if the response function rises supralinearly at low inputs from a value near zero at zero input. We simulate and analyze finite systems, showing that the probability of multistability can peak at intermediate system size, and connect with other literature analyzing similar systems in the infinite-size limit. We find regions of multistability with a bimodal distribution for the number of active units in a stable state. Finally, we find evidence for a log-normal distribution of sizes of attractor basins, which produces Zipf's Law when enumerating the proportion of trials within which random initial conditions lead to a particular stable state of the system.","PeriodicalId":55374,"journal":{"name":"Biological Cybernetics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138833073","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

Four attributes of intelligence, a thousand questions. 智力的四种属性，一千个问题。

IF 1.9 4区工程技术

Biological Cybernetics Pub Date : 2023-12-01 Epub Date: 2023-12-07 DOI: 10.1007/s00422-023-00979-4

Matthieu Bardal, Eric Chalmers

引用次数: 0