Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis.
Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis.
复制标题
使用非阴性主成分分析从位置细胞输入中提取网格细胞特征。
DOI：
10.7554/elife.10094
复制
发表时间：
2016-03-08
期刊：
影响因子：
7.7
通讯作者：
Derdikman D
中科院分区：
文献类型：
--
作者：
Dordek Y;Soudry D;Meir R;Derdikman D
最近的许多模型研究了从网格单元到位置单元的下游投影，而最近的数据指出了反馈投影的重要性。因此，我们询问网格单元如何受到位置单元输入的性质的影响。我们提出了一种具有前馈权重的单层神经网络，将类似位置的输入单元连接到网格单元输出。位置到网格权重是通过广义赫布规则学习的。该网络的架构与用于执行主成分分析 (PCA) 的神经网络高度相似。数值结果和分析考虑都表明，如果前馈神经网络的分量非负，则输出收敛到六角形格子。如果没有非负约束，输出会收敛到方格。与实验一致，前两个连续模块之间的网格间距比为-1.4。我们的结果表达了位置细胞与网格细胞相互作用和主成分分析之间可能的联系。 DOI：http://dx.doi.org/10.7554/eLife.10094.001 早在 GPS 系统发明之前，船舶就使用一种称为航位推算的技术在海上航行。通过跟踪船舶远离起点的速度和运动方向，船员可以在任何给定时间估计他们的位置。许多人认为，一些动物，包括老鼠和人类，可以在没有外部地标的情况下使用类似的过程进行导航。这个过程被称为“路径整合”。人们普遍认为，大脑的导航系统是基于两个关键区域的路径整合：内嗅皮层和海马体。大多数导航模型都假设内嗅皮层中的网格细胞网络处理有关动物运动速度和方向的信息。网格细胞网络估计动物未来的位置，并将此信息传递给海马体中称为位置细胞的细胞。每当动物到达特定位置时，各个位置细胞就会放电。然而，最近的研究表明，信息也从位置单元流回网格单元。进一步的实验表明，位置细胞先于网格细胞发育。此外，失活位置细胞会消除通常出现在网格细胞活动中的六边形图案。 Dordek、Soudry 等人使用计算模型。现在表明，位置细胞活动原则上可以触发网格细胞网络的形成，而不是相反。这是通过类似于称为主成分分析 (PCA) 的常见统计算法的过程来实现的。然而，只有当位置细胞只刺激网格细胞而不抑制它们的活动时，这才有效，类似于从这些大脑区域的解剖学中了解到的情况。在这种情况下，该模型显示了网格细胞活动中出现的六边形图案，其特性与实验观察到的图案相似。这些结果表明，导航可能不仅仅依赖于网格单元处理有关运动速度和方向的信息，如路径集成模型所假设的那样。相反，网格单元可能依赖于来自位置单元的基于位置的输入。下一步是创建一个单一模型，该模型结合了从位置单元到网格单元的信息流，反之亦然。 DOI：http://dx.doi.org/10.7554/eLife.10094.002
Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA. DOI: http://dx.doi.org/10.7554/eLife.10094.001 Long before the invention of GPS systems, ships used a technique called dead reckoning to navigate at sea. By tracking the ship’s speed and direction of movement away from a starting point, the crew could estimate their position at any given time. Many believe that some animals, including rats and humans, can use a similar process to navigate in the absence of external landmarks. This process is referred to as “path integration”. It is commonly believed that the brain’s navigation system is based on such path integration in two key regions: the entorhinal cortex and the hippocampus. Most models of navigation assume that a network of grid cells in the entorhinal cortex processes information about an animal’s speed and direction of movement. The grid cell network estimates the animal’s future position and relays this information to cells in the hippocampus called place cells. Individual place cells then fire whenever the animal reaches a specific location. However, recent work has shown that information also flows from place cells back to grid cells. Further experiments have suggested that place cells develop before grid cells. Also, inactivating place cells eliminates the hexagonal patterns that normally appear in the activity of the grid cells. Using a computational model, Dordek, Soudry et al. now show that place cell activity could in principle trigger the formation of the grid cell network, rather than vice versa. This is achieved using a process that resembles a common statistical algorithm called principal component analysis (PCA). However, this only works if place cells only excite grid cells and never inhibit their activity, similar to what is known from the anatomy of these brain regions. Under these circumstances, the model shows hexagonal patterns emerging in the activity of the grid cells, with similar properties to those patterns observed experimentally. These results suggest that navigation may not depend solely on grid cells processing information about speed and direction of movement, as assumed by path integration models. Instead grid cells may rely on position-based input from place cells. The next step is to create a single model that combines the flow of information from place cells to grid cells and vice versa. DOI: http://dx.doi.org/10.7554/eLife.10094.002