PDF(Pubmed)
Abstract：
Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications.
摘要：
具有数据驱动的生理细节的计算模拟可以促进对认知中涉及的神经机制的更深入理解。这里,我们利用Hippencome.org的大量细胞特性，通过内侧内嗅皮层回路活动的尖峰连续吸引子网络模型研究空间编码的神经机制。主要目标是调查添加这样的现实约束是否可以产生类似于在真实神经元中测量的放电模式。工作中包含的生物学特征是兴奋性，连通性,和神经元类型的突触信号主要由它们的轴突和树突形态定义。我们研究了特定神经元类型的尖峰动力学以及神经元组之间的突触活动。对啮齿动物海马结构进行建模将模拟保持在计算上合理的规模,同时还将参数和结果锚定到实验测量。我们的模型生成的网格细胞活动与间距非常匹配，尺寸,以及从已发布的数据集和为本研究进行的新实验中记录的实时行为动物的网格场的发射率。我们的模拟还重建了这些属性的不同尺度，例如，又小又大,沿着内侧内嗅皮层的背腹轴发现。对神经元和突触模型参数的计算探索表明，在模拟中，广泛的神经特性会产生网格场。这些结果表明，网格细胞的连续吸引子网络模型与来自海马组织的数据驱动的生物物理和解剖参数的尖峰神经网络实现兼容。该软件（版本1.0）作为开源发布，以实现广泛的社区重用并鼓励新颖的应用程序。
