类脑导航中基于差分Hebbian学习的网格细胞构建模型

网格细胞是动物大脑中与空间认知和导航有关的重要神经元,具有拓展至整个空间的六边形放电野。位置细胞是网格细胞重要的信息源,可以通过Hebbian学习生成网格细胞,但是现有模型对Hebbian学习的脉冲自适应函数或学习窗函数做了预先的墨西哥帽模型假设。针对该问题提出一种基于差分Hebbian学习的位置细胞至网格细胞模型,利用细胞放电率的变化自发产生墨西哥帽模型的输入关联,然后通过对位置细胞至网格细胞的突触权重进行竞争性非线性限制,生成具有六边形放电野分布的网格细胞。仿真结果表明,该模型可以为无人运行体类脑导航系统的构建提供借鉴。

Model of generating grid cell based on difference Hebbian learning in brain-inspired navigation

The grid cell is a kind of important neuron cell related to spatial cognition and navigation in animal brain. It has hexagonal firing field extending to the whole two-dimensional space. The place cell is one of the main information sources of grid cells. The place cells can generate the grid cell through Hebbian learning, but the existing learning methods have assumed the Mexican hat model in advance for the adaptive function or learning window function of Hebbian learning. A place-to-grid cell model based on difference Hebbian learning is proposed. The input correlation with the Mexican hat model is generated spontaneously by using the difference of cells firing rates, and then the grid cell with the hexagonal firing field is generated through competitive nonlinear constraint of synaptic weights from place cells to the grid cell. The simulation results show that the difference Hebbian learning model can provide reference for the construction of the brain-inspired navigation system of unmanned platform.