A fractional-order Wilson-Cowan formulation of cortical disinhibition.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2024-02-01 Epub Date: 2023-10-03 DOI:10.1007/s10827-023-00862-y
L R González-Ramírez
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Abstract

This work presents a fractional-order Wilson-Cowan model derivation under Caputo's formalism, considering an order of 0 < α 1 . To that end, we propose memory-dependent response functions and average neuronal excitation functions that permit us to naturally arrive at a fractional-order model that incorporates past dynamics into the description of synaptically coupled neuronal populations' activity. We then shift our focus on a particular example, aiming to analyze the fractional-order dynamics of the disinhibited cortex. This system mimics cortical activity observed during neurological disorders such as epileptic seizures, where an imbalance between excitation and inhibition is present, which allows brain dynamics to transition to a hyperexcited activity state. In the context of the first-order mathematical model, we recover traditional results showing a transition from a low-level activity state to a potentially pathological high-level activity state as an external factor modifies cortical inhibition. On the other hand, under the fractional-order formulation, we establish novel results showing that the system resists such transition as the order is decreased, permitting the possibility of staying in the low-activity state even with increased disinhibition. Furthermore, considering the memory index interpretation of the fractional-order model motivation here developed, our results establish that by increasing the memory index, the system becomes more resistant to transitioning towards the high-level activity state. That is, one possible effect of the memory index is to stabilize neuronal activity. Noticeably, this neuronal stabilizing effect is similar to homeostatic plasticity mechanisms. To summarize our results, we present a two-parameter structural portrait describing the system's dynamics dependent on a proposed disinhibition parameter and the order. We also explore numerical model simulations to validate our results.

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皮层去抑制的分数阶Wilson Cowan公式。
这项工作提出了在Caputo形式下的分数阶Wilson Cowan模型推导,考虑了[公式:见正文]的阶数。为此,我们提出了记忆依赖性反应函数和平均神经元兴奋函数,使我们能够自然地得出一个分数阶模型,该模型将过去的动力学纳入突触耦合神经元群体活动的描述中。然后,我们将重点转移到一个特定的例子上,旨在分析去抑制皮层的分数阶动力学。该系统模拟了在癫痫发作等神经系统疾病中观察到的皮层活动,在癫痫发作中,兴奋和抑制之间存在不平衡,这使大脑动力学转变为过度兴奋的活动状态。在一阶数学模型的背景下,我们恢复了传统的结果,显示随着外部因素改变皮层抑制,从低水平活动状态转变为潜在的病理性高水平活动状态。另一方面,在分数阶公式下,我们建立了新的结果,表明当阶数降低时,系统抵抗这种转变,即使在去抑制增加的情况下,也有可能保持在低活性状态。此外,考虑到这里开发的分数阶模型动机的记忆指数解释,我们的结果表明,通过增加记忆指数,系统变得更抗拒向高级活动状态过渡。也就是说,记忆指数的一个可能作用是稳定神经元活动。值得注意的是,这种神经元稳定作用类似于稳态可塑性机制。为了总结我们的结果,我们提出了一个双参数结构画像,描述了依赖于所提出的去抑制参数和阶数的系统动力学。我们还探索了数值模型模拟来验证我们的结果。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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