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A high-efficiency model indicating the role of inhibition in the resilience of neuronal networks to damage resulting from traumatic injury. 一种高效模型,表明抑制在神经元网络对创伤损伤的恢复力中的作用。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-11-01 Epub Date: 2023-08-26 DOI: 10.1007/s10827-023-00860-0
Brian L Frost, Stanislav M Mintchev

Recent investigations of traumatic brain injuries have shown that these injuries can result in conformational changes at the level of individual neurons in the cerebral cortex. Focal axonal swelling is one consequence of such injuries and leads to a variable width along the cell axon. Simulations of the electrical properties of axons impacted in such a way show that this damage may have a nonlinear deleterious effect on spike-encoded signal transmission. The computational cost of these simulations complicates the investigation of the effects of such damage at a network level. We have developed an efficient algorithm that faithfully reproduces the spike train filtering properties seen in physical simulations. We use this algorithm to explore the impact of focal axonal swelling on small networks of integrate and fire neurons. We explore also the effects of architecture modifications to networks impacted in this manner. In all tested networks, our results indicate that the addition of presynaptic inhibitory neurons either increases or leaves unchanged the fidelity, in terms of bandwidth, of the network's processing properties with respect to this damage.

最近对创伤性脑损伤的研究表明,这些损伤会导致大脑皮层单个神经元水平的构象变化。局灶性轴突肿胀是这种损伤的后果之一,并导致沿着细胞轴突的宽度可变。对以这种方式受到影响的轴突的电特性的模拟表明,这种损伤可能对刺突编码的信号传输具有非线性有害影响。这些模拟的计算成本使在网络层面上研究这种损伤的影响变得复杂。我们开发了一种高效的算法,它忠实地再现了物理模拟中看到的尖峰序列滤波特性。我们使用该算法来探索局灶性轴突肿胀对整合和激发神经元的小网络的影响。我们还探讨了架构修改对以这种方式受到影响的网络的影响。在所有测试的网络中,我们的结果表明,突触前抑制性神经元的加入增加了网络处理特性对这种损伤的保真度,或者在带宽方面保持不变。
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引用次数: 0
Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks. 探索为时间和决策任务训练的递归神经网络中的权重初始化、解的多样性和退化。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-11-01 Epub Date: 2023-08-10 DOI: 10.1007/s10827-023-00857-9
Cecilia Jarne, Rodrigo Laje

Recurrent Neural Networks (RNNs) are frequently used to model aspects of brain function and structure. In this work, we trained small fully-connected RNNs to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and also how the performance gracefully degrades as either network size is decreased, interval duration is increased, or connectivity damage is induced. For the considered tasks, we explored how robust the network obtained after training can be according to task parameterization. In the process, we developed a framework that can be useful to parameterize other tasks of interest in computational neuroscience. Our results are useful to quantify different aspects of the models, which are normally used as black boxes and need to be understood in order to model the biological response of cerebral cortex areas.

递归神经网络(RNN)经常用于对大脑功能和结构的各个方面进行建模。在这项工作中,我们训练了小型完全连接的RNN,以在具有时变刺激的情况下执行时间和流量控制任务。我们的结果表明,不同的RNN可以通过收敛到不同的底层动态来解决相同的任务,以及性能如何随着网络大小的减小、间隔持续时间的增加或连接损坏而优雅地降低。对于所考虑的任务,我们探索了根据任务参数化,训练后获得的网络的鲁棒性。在此过程中,我们开发了一个框架,该框架可用于参数化计算神经科学中感兴趣的其他任务。我们的结果有助于量化模型的不同方面,这些模型通常被用作黑匣子,需要理解才能对大脑皮层区域的生物反应进行建模。
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引用次数: 0
Responses in fast-spiking interneuron firing rates to parameter variations associated with degradation of perineuronal nets. 快速尖峰的中间神经元放电率对与神经周围网络退化相关的参数变化的反应。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00849-9
Kine Ødegård Hanssen, Sverre Grødem, Marianne Fyhn, Torkel Hafting, Gaute T Einevoll, Torbjørn Vefferstad Ness, Geir Halnes

The perineuronal nets (PNNs) are sugar coated protein structures that encapsulate certain neurons in the brain, such as parvalbumin positive (PV) inhibitory neurons. As PNNs are theorized to act as a barrier to ion transport, they may effectively increase the membrane charge-separation distance, thereby affecting the membrane capacitance. Tewari et al. (2018) found that degradation of PNNs induced a 25%-50% increase in membrane capacitance [Formula: see text] and a reduction in the firing rates of PV-cells. In the current work, we explore how changes in [Formula: see text] affects the firing rate in a selection of computational neuron models, ranging in complexity from a single compartment Hodgkin-Huxley model to morphologically detailed PV-neuron models. In all models, an increased [Formula: see text] lead to reduced firing, but the experimentally reported increase in [Formula: see text] was not alone sufficient to explain the experimentally reported reduction in firing rate. We therefore hypothesized that PNN degradation in the experiments affected not only [Formula: see text], but also ionic reversal potentials and ion channel conductances. In simulations, we explored how various model parameters affected the firing rate of the model neurons, and identified which parameter variations in addition to [Formula: see text] that are most likely candidates for explaining the experimentally reported reduction in firing rate.

神经元周围网(PNNs)是一种糖包被的蛋白质结构,包裹着大脑中的某些神经元,如小白蛋白阳性(PV)抑制神经元。由于理论上pnn作为离子传输的屏障,它们可以有效地增加膜电荷分离距离,从而影响膜电容。Tewari等人(2018)发现,pnn的降解会导致膜电容增加25%-50%[公式:见文本],并降低pv电池的放电速率。在当前的工作中,我们探索了[公式:见文本]的变化如何影响选择计算神经元模型中的放电率,从单室霍奇金-赫胥黎模型到形态详细的pv神经元模型的复杂性。在所有模型中,增加的[公式:见文]导致射击减少,但实验报告的[公式:见文]增加并不足以解释实验报告的射击率减少。因此,我们假设实验中的PNN退化不仅影响[公式:见文本],还影响离子反转电位和离子通道电导。在模拟中,我们探索了各种模型参数如何影响模型神经元的放电速率,并确定了除了[公式:见文本]之外,哪些参数变化最有可能解释实验报告的放电速率降低。
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引用次数: 1
Adaptive unscented Kalman filter for neuronal state and parameter estimation. 用于神经元状态和参数估计的自适应无气味卡尔曼滤波。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00845-z
Loïc J Azzalini, David Crompton, Gabriele M T D'Eleuterio, Frances Skinner, Milad Lankarany

Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filter's performance against existing nonlinear Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults.

用于状态和参数估计的数据同化技术经常应用于计算神经科学。在这项工作中,我们展示了无气味卡尔曼滤波器(UKF)的自适应变体如何对基于电导的神经元模型进行跟踪。与标准递归滤波器实现不同,鲁棒自适应无气味卡尔曼滤波器(RAUKF)在基于创新和残差信息在线调整噪声协方差矩阵的同时,联合估计神经元模型的状态和参数。我们将自适应滤波器的性能与现有的非线性卡尔曼滤波器进行比较,并探讨了滤波器参数对被建模系统的灵敏度。为了评估所提出的解决方案的鲁棒性,我们模拟了挑战跟踪性能的实际设置,例如模型不匹配和测量错误。与标准卡尔曼滤波相比,本文实现的自适应卡尔曼滤波具有更高的精度和对故障的鲁棒性。
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引用次数: 2
Different parameter solutions of a conductance-based model that behave identically are not necessarily degenerate. 行为相同的基于电导的模型的不同参数解不一定是简并的。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00848-w
Loïs Naudin
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引用次数: 0
Bayesian prediction of psychophysical detection responses from spike activity in the rat sensorimotor cortex. 大鼠感觉运动皮层尖峰活动的心理物理检测反应的贝叶斯预测。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00844-0
Sevgi Öztürk, İsmail Devecioğlu, Burak Güçlü

Decoding of sensorimotor information is essential for brain-computer interfaces (BCIs) as well as in normal functioning organisms. In this study, Bayesian models were developed for the prediction of binary decisions of 10 awake freely-moving male/female rats based on neural activity in a vibrotactile yes/no detection task. The vibrotactile stimuli were 40-Hz sinusoidal displacements (amplitude: 200 µm, duration: 0.5 s) applied on the glabrous skin. The task was to depress the right lever for stimulus detection and left lever for stimulus-off condition. Spike activity was recorded from 16-channel microwire arrays implanted in the hindlimb representation of primary somatosensory cortex (S1), overlapping also with the associated representation in the primary motor cortex (M1). Single-/multi-unit average spike rate (Rd) within the stimulus analysis window was used as the predictor of the stimulus state and the behavioral response at each trial based on a Bayesian network model. Due to high neural and psychophysical response variability for each rat and also across subjects, mean Rd was not correlated with hit and false alarm rates. Despite the fluctuations in the neural data, the Bayesian model for each rat generated moderately good accuracy (0.60-0.90) and good class prediction scores (recall, precision, F1) and was also tested with subsets of data (e.g. regular vs. fast spike groups). It was generally observed that the models were better for rats with lower psychophysical performance (lower sensitivity index A'). This suggests that Bayesian inference and similar machine learning techniques may be especially helpful during the training phase of BCIs or for rehabilitation with neuroprostheses.

感觉运动信息的解码对于脑机接口(bci)以及正常功能的生物体都是必不可少的。在这项研究中,基于振动触觉是/否检测任务中的神经活动,建立了10只清醒的自由运动雄性/雌性大鼠的贝叶斯模型来预测二元决策。振动触觉刺激是施加在无毛皮肤上的40 hz正弦位移(振幅:200µm,持续时间:0.5 s)。任务是在刺激检测条件下按下右侧杠杆,在刺激关闭条件下按下左侧杠杆。通过植入后肢初级躯体感觉皮层(S1)的16通道微线阵列记录了Spike活动,并与初级运动皮层(M1)的相关表征重叠。基于贝叶斯网络模型,以刺激分析窗口内的单/多单元平均尖峰率(Rd)作为每次试验的刺激状态和行为反应的预测因子。由于每只大鼠和受试者之间的神经和心理物理反应具有很高的可变性,因此平均Rd与命中率和误报率无关。尽管神经数据有波动,但每只大鼠的贝叶斯模型产生了中等好的准确性(0.60-0.90)和良好的类别预测分数(召回率,精度,F1),并且还使用数据子集(例如常规与快速尖峰组)进行了测试。一般观察到,对于心理物理性能较低的大鼠(敏感性指数A′较低),模型效果较好。这表明贝叶斯推理和类似的机器学习技术可能在脑机接口的训练阶段或神经假体的康复中特别有用。
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引用次数: 0
Slow negative feedback enhances robustness of square-wave bursting. 慢负反馈增强了方波爆破的鲁棒性。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00846-y
Sushmita Rose John, Bernd Krauskopf, Hinke M Osinga, Jonathan E Rubin

Square-wave bursting is an activity pattern common to a variety of neuronal and endocrine cell models that has been linked to central pattern generation for respiration and other physiological functions. Many of the reduced mathematical models that exhibit square-wave bursting yield transitions to an alternative pseudo-plateau bursting pattern with small parameter changes. This susceptibility to activity change could represent a problematic feature in settings where the release events triggered by spike production are necessary for function. In this work, we analyze how model bursting and other activity patterns vary with changes in a timescale associated with the conductance of a fast inward current. Specifically, using numerical simulations and dynamical systems methods, such as fast-slow decomposition and bifurcation and phase-plane analysis, we demonstrate and explain how the presence of a slow negative feedback associated with a gradual reduction of a fast inward current in these models helps to maintain the presence of spikes within the active phases of bursts. Therefore, although such a negative feedback is not necessary for burst production, we find that its presence generates a robustness that may be important for function.

方波爆发是多种神经元和内分泌细胞模型中常见的一种活动模式,与呼吸和其他生理功能的中枢模式产生有关。许多显示方波爆破的简化数学模型的产量转变为一种可选择的伪平台爆破模式,参数变化很小。这种对活动变化的易感性可能在由尖峰产生触发的释放事件是功能所必需的设置中代表一个有问题的特征。在这项工作中,我们分析了模型破裂和其他活动模式如何随着与快速内向电流电导相关的时间尺度的变化而变化。具体来说,使用数值模拟和动态系统方法,如快慢分解和分岔和相平面分析,我们展示并解释了这些模型中与快速内向电流逐渐减少相关的缓慢负反馈的存在如何有助于在爆发的活跃阶段保持峰值的存在。因此,虽然这样的负反馈对突发产生不是必需的,但我们发现它的存在产生了对函数可能很重要的鲁棒性。
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引用次数: 2
A biophysical and statistical modeling paradigm for connecting neural physiology and function. 连接神经生理学和功能的生物物理和统计建模范式。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-05-01 DOI: 10.1007/s10827-023-00847-x
Nathan G Glasgow, Yu Chen, Alon Korngreen, Robert E Kass, Nathan N Urban

To understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between spiking patterns and the stimuli they encode. We used public biophysical models of two morphologically and functionally distinct projection neuron cell types: mitral cells (MCs) of the main olfactory bulb, and layer V cortical pyramidal cells (PCs). We first simulated sequences of action potentials according to certain stimuli while scaling individual ion channel conductances. We then fitted point process generalized linear models (PP-GLMs), and we constructed a mapping between the parameters in the two types of models. This framework lets us detect effects on stimulus encoding of changing an ion channel conductance. The computational pipeline combines models across scales and can be applied as a screen of channels, in any cell type of interest, to identify ways that channel properties influence single neuron computation.

为了理解单个神经元的计算,有必要了解特定的生理参数如何影响特定刺激下出现的神经尖峰模式。在这里,我们提出了一个结合生物物理和统计模型的计算管道,提供了功能性离子通道表达变化和单个神经元刺激编码变化之间的联系。更具体地说,我们创建了从生物物理模型参数到刺激编码统计模型参数的映射。生物物理模型提供了机制上的洞察,而统计模型可以识别出尖峰模式和它们编码的刺激之间的联系。我们使用了两种形态和功能不同的投射神经元细胞类型的公共生物物理模型:主嗅球的二尖瓣细胞(MCs)和V层皮质锥体细胞(PCs)。我们首先根据某些刺激模拟动作电位序列,同时缩放单个离子通道电导。然后,我们拟合了点过程广义线性模型(PP-GLMs),并构建了两类模型中参数之间的映射关系。这个框架使我们能够检测到改变离子通道电导对刺激编码的影响。计算管道结合了跨尺度的模型,可以在任何感兴趣的细胞类型中作为通道屏幕应用,以确定通道属性影响单个神经元计算的方式。
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引用次数: 0
Variations of the spontaneous electrical activities of the neuronal networks imposed by the exposure of electromagnetic radiations using computational map-based modeling. 利用基于计算图的模型研究暴露于电磁辐射下神经元网络自发电活动的变化。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-02-01 DOI: 10.1007/s10827-022-00842-8
Mohsen Kamelian Rad, Meysam Hedayati Hamedani, Mohammad Bagher Khodabakhshi

The interaction between neurons in a neuronal network develops spontaneous electrical activities. But the effects of electromagnetic radiation on these activities have not yet been well explored. In this study, a ring of three coupled 1-dimensional Rulkov neurons and the generated electromagnetic field (EMF) are considered to investigate how the spontaneous activities might change regarding the EMF exposure. By employing the bifurcation analysis and time series, a comprehensive view of neuronal behavioral changes due to electromagnetic inductions is provided. The main findings of this study are as follows: 1) When a neuronal network is showing a spontaneous chaotic firing manner (without any external stimuli), a generated magnetic field inhibits this type of behavior. In fact, EMF completely eliminated the chaotic intrinsic behaviors of the neuronal loop. 2) When the network is exhibiting regular period-3 spiking patterns, the generated magnetic field changes its firing pattern to chaotic spiking, which is similar to epileptic seizures. 3) With weak synaptic connections, electromagnetic radiation inhibits and suppresses neuronal activities. 4) If the external magnetic flux has a high amplitude, it can change the shape of the induction current according to its shape 5) when there are weak synaptic connections in the network, a high-frequency external magnetic flux engenders high-frequency fluctuations in the membrane voltages. On the whole, electromagnetic radiation changes the pattern of the spontaneous activities of neuronal networks in the brain according to synaptic strengths and initial states of the neurons.

神经元网络中神经元之间的相互作用产生自发的电活动。但是电磁辐射对这些活动的影响还没有得到很好的研究。在本研究中,考虑了三个耦合的一维鲁可夫神经元环和产生的电磁场(EMF),以研究电磁场暴露下自发活动的变化。通过分岔分析和时间序列分析,对电磁感应引起的神经元行为变化有了一个全面的认识。本研究的主要发现如下:1)当神经元网络表现出自发的混沌放电方式(没有任何外界刺激)时,产生的磁场会抑制这种行为。事实上,电动势完全消除了神经元环路的混沌固有行为。2)当神经网络呈现规则的3周期尖峰模式时,产生的磁场将其发射模式改变为混沌尖峰,类似于癫痫发作。3)在突触连接薄弱的情况下,电磁辐射抑制和抑制神经元活动。4)当外磁通量幅值较高时,可根据其形状改变感应电流的形状。5)当网络中存在弱突触连接时,高频外磁通量会引起膜电压的高频波动。总的来说,电磁辐射根据神经元的突触强度和初始状态改变了大脑中神经元网络自发活动的模式。
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引用次数: 0
A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications. 钾和钙通道修饰作用下非尖峰神经元动力学的一般模式。
IF 1.2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Pub Date : 2023-02-01 DOI: 10.1007/s10827-022-00840-w
Loïs Naudin, Laetitia Raison-Aubry, Laure Buhry

Electrical activity of excitable cells results from ion exchanges through cell membranes, so that genetic or epigenetic changes in genes encoding ion channels are likely to affect neuronal electrical signaling throughout the brain. There is a large literature on the effect of variations in ion channels on the dynamics of spiking neurons that represent the main type of neurons found in the vertebrate nervous systems. Nevertheless, non-spiking neurons are also ubiquitous in many nervous tissues and play a critical role in the processing of some sensory systems. To our knowledge, however, how conductance variations affect the dynamics of non-spiking neurons has never been assessed. Based on experimental observations reported in the biological literature and on mathematical considerations, we first propose a phenotypic classification of non-spiking neurons. Then, we determine a general pattern of the phenotypic evolution of non-spiking neurons as a function of changes in calcium and potassium conductances. Furthermore, we study the homeostatic compensatory mechanisms of ion channels in a well-posed non-spiking retinal cone model. We show that there is a restricted range of ion conductance values for which the behavior and phenotype of the neuron are maintained. Finally, we discuss the implications of the phenotypic changes of individual cells at the level of neuronal network functioning of the C. elegans worm and the retina, which are two non-spiking nervous tissues composed of neurons with various phenotypes.

可兴奋细胞的电活动是通过细胞膜进行离子交换的结果,因此编码离子通道的基因的遗传或表观遗传变化可能影响整个大脑的神经元电信号。关于离子通道变化对棘突神经元动力学的影响有大量的文献,棘突神经元是脊椎动物神经系统中发现的主要类型的神经元。然而,非尖峰神经元也普遍存在于许多神经组织中,在一些感觉系统的处理中起着关键作用。然而,据我们所知,电导变化如何影响非尖峰神经元的动力学从未被评估过。基于生物学文献中的实验观察和数学考虑,我们首先提出了非尖峰神经元的表型分类。然后,我们确定了非尖峰神经元表型进化的一般模式,作为钙和钾电导变化的功能。此外,我们研究了离子通道的稳态补偿机制,在一个适定的非尖峰视网膜锥体模型。我们表明,有一个限制范围的离子电导值的行为和表型神经元是维持。最后,我们讨论了秀丽隐杆线虫和视网膜这两个由不同表型神经元组成的非尖峰神经组织在神经元网络功能水平上单个细胞表型变化的意义。
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引用次数: 1
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Journal of Computational Neuroscience
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