Longo Lucas, Lima Thiago, Bila Maria Clara, Brogin João, Faber Jean
School of Medical Sciences, Federal Fluminense University - UFF, Niterói, Rio de Janeiro, Brazil.
Department of Mechanical Engineering, Universidade Estadual de São Paulo - UNESP, São Paulo, Brazil.
PLoS One. 2025 Apr 29;20(4):e0319641. doi: 10.1371/journal.pone.0319641. eCollection 2025.
The hippocampal formation is vital for processing memory, learning, and spatial navigation. Existing methods are obsolete to address new emerging questions as our understanding of hippocampal circuits and its connections advances. Hence, new techniques with an accessible approach for visualizing and understanding its inner connections and circuitry are needed. Research requires a quick update of textbooks and a better integration of new media to facilitate the teaching of these neural structures. For instance, pictures and diagrams are not enough to fully express the structural and functional effects that each neural circuit imparts. Computational models adapted to these diverse contexts might be a possible solution for such challenge. The construction of minimalist computational models can be an excellent alternative in teaching complex dynamics since they reduce the use of animal models, amplify and simplify structural relationships, promote quick and easy visualization, and uncover possible functional and structural interventions with an educational goal. This interactivity is crucial for a better understanding of the causal relationships between nuclei and neural circuits. Conversely, it is important that those models are simple enough so that any student, regardless of their mathematical background, can understand and manipulate features of interest. Further, software packages that do not require programming knowledge for its use are indispensable, even though this limitation also restricts the representations possible for study. Here, we demonstrate the use of Neuronify software, which uses simple functional representations of neurons and circuits. We represent the most important pathways and connections of the hippocampal formation by building an educational and a simplified models that shows the main known relations between the subregions [Cornu Ammonis (CA)1, CA2, CA3, and CA4], afferent and efferent nucleus (dentate gyrus and subiculum), the first also seeking to couple hippocampal neuroarchitecture, with posterior validation of both by application in an educational context.
海马结构对于处理记忆、学习和空间导航至关重要。随着我们对海马回路及其连接的理解不断深入,现有的方法已过时,无法解决新出现的问题。因此,需要新的技术,以一种易于理解的方式来可视化和理解其内部连接和电路。研究需要快速更新教科书,并更好地整合新媒体,以促进这些神经结构的教学。例如,图片和图表不足以充分表达每个神经回路所赋予的结构和功能效应。适用于这些不同情境的计算模型可能是应对此类挑战的一种解决方案。构建极简计算模型可能是教授复杂动力学的一个绝佳选择,因为它们减少了动物模型的使用,放大并简化了结构关系,促进了快速且易于理解的可视化,并揭示了具有教育目的的可能的功能和结构干预措施。这种交互性对于更好地理解核团与神经回路之间的因果关系至关重要。相反,这些模型要足够简单,以便任何学生,无论其数学背景如何,都能理解和操作感兴趣的特征,这一点很重要。此外,即使这种限制也限制了可供研究的表示方式,但使用时不需要编程知识的软件包是必不可少的。在这里,我们展示了Neuronify软件的使用,该软件使用神经元和电路的简单功能表示。我们通过构建一个教育模型和一个简化模型来表示海马结构的最重要通路和连接,该简化模型展示了各子区域[海马角(CA)1、CA2、CA3和CA4]、传入和传出核团(齿状回和海马下脚)之间的主要已知关系,第一个模型还试图将海马神经结构联系起来,并通过在教育背景中的应用对两者进行后续验证。