A cortical field theory - dynamics and symmetries.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2024-11-01 Epub Date: 2024-10-01 DOI:10.1007/s10827-024-00878-y
Gerald K Cooray, Vernon Cooray, Karl Friston
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引用次数: 0

Abstract

We characterise cortical dynamics using partial differential equations (PDEs), analysing various connectivity patterns within the cortical sheet. This exploration yields diverse dynamics, encompassing wave equations and limit cycle activity. We presume balanced equations between excitatory and inhibitory neuronal units, reflecting the ubiquitous oscillatory patterns observed in electrophysiological measurements. Our derived dynamics comprise lowest-order wave equations (i.e., the Klein-Gordon model), limit cycle waves, higher-order PDE formulations, and transitions between limit cycles and near-zero states. Furthermore, we delve into the symmetries of the models using the Lagrangian formalism, distinguishing between continuous and discontinuous symmetries. These symmetries allow for mathematical expediency in the analysis of the model and could also be useful in studying the effect of symmetrical input from distributed cortical regions. Overall, our ability to derive multiple constraints on the fields - and predictions of the model - stems largely from the underlying assumption that the brain operates at a critical state. This assumption, in turn, drives the dynamics towards oscillatory or semi-conservative behaviour. Within this critical state, we can leverage results from the physics literature, which serve as analogues for neural fields, and implicit construct validity. Comparisons between our model predictions and electrophysiological findings from the literature - such as spectral power distribution across frequencies, wave propagation speed, epileptic seizure generation, and pattern formation over the cortical surface - demonstrate a close match. This study underscores the importance of utilizing symmetry preserving PDE formulations for further mechanistic insights into cortical activity.

皮层场理论--动力学和对称性。
我们利用偏微分方程(PDEs)描述大脑皮层的动力学特征,分析大脑皮层薄片内的各种连接模式。这种探索产生了多种多样的动力学,包括波方程和极限周期活动。我们假定兴奋性和抑制性神经元单元之间存在平衡方程,这反映了电生理测量中观察到的无处不在的振荡模式。我们推导的动力学包括最低阶的波方程(即克莱因-戈登模型)、极限周期波、高阶 PDE 公式以及极限周期和近零状态之间的转换。此外,我们还利用拉格朗日形式主义深入研究了模型的对称性,区分了连续对称性和非连续对称性。这些对称性使模型分析在数学上更加简便,也有助于研究来自分布式皮层区域的对称输入的影响。总之,我们能够推导出对场的多重约束以及模型的预测,这在很大程度上源于大脑在临界状态下运行的基本假设。而这一假设又反过来推动动力学走向振荡或半保守行为。在这种临界状态下,我们可以利用物理学文献中的结果,作为神经场的类比,并隐含建构有效性。我们的模型预测与文献中的电生理学研究结果(如不同频率的频谱功率分布、波的传播速度、癫痫发作的产生以及皮层表面的模式形成)之间的比较表明两者非常吻合。这项研究强调了利用保持对称性的 PDE 公式进一步深入了解大脑皮层活动机理的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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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