Yu Gene J, Bouteiller Jean-Marie C, Song Dong, Berger Theodore W
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:6137-6140. doi: 10.1109/EMBC.2018.8513576.
Spatial information is encoded by the hippocampus, and the factors that contribute to the amount of information that can be encoded and the transformation of spatial information through the trisynaptic circuit remain an important issue. A large-scale neuronal network model of the rat entorhinal-dentate system was developed with multicompartmental representations of the neurons within the dentate gyrus. Spatial information was introduced to the network via grid cell activity, and the spatial information encoding capabilities of the network were assessed using a recursive decoding algorithm to estimate the position of a virtual rat using the dentate activity. To obtain a measure for the information that the network could convey, decoding error was calculated for different decoding population sizes. Decoding error decreased exponentially as a function of population size. Therefore, the time constant and the asymptote of the error curve could be used as metrics to compare the changes in encoding performance. In conjunction with the large-scale model, this paradigm can be used to characterize how neural properties, network composition, and the interactions between different subfields affect spatial information encoding.
空间信息由海马体编码，而影响可编码信息量以及空间信息通过三突触回路转换的因素仍然是一个重要问题。利用齿状回内神经元的多房室表征，构建了大鼠内嗅-齿状系统的大规模神经元网络模型。通过网格细胞活动将空间信息引入网络，并使用递归解码算法评估网络的空间信息编码能力，该算法利用齿状活动估计虚拟大鼠的位置。为了获得网络能够传递的信息的度量，针对不同的解码群体规模计算解码误差。解码误差随群体规模呈指数下降。因此，误差曲线的时间常数和渐近线可作为比较编码性能变化的指标。结合大规模模型，该范式可用于描述神经特性、网络组成以及不同子场之间的相互作用如何影响空间信息编码。
