Using Grid Cells for Navigation.
Using Grid Cells for Navigation.
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DOI：
10.1016/j.neuron.2015.07.006
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发表时间：
2015-08-05
期刊：
影响因子：
16.2
通讯作者：
Burgess N
中科院分区：
文献类型：
--
作者：
Bush D;Barry C;Manson D;Burgess N
哺乳动物能够通过可能穿越以前未去过的地形的直接路线导航到隐藏的目标位置。经验证据表明，这种“矢量导航”依赖于海马结构提供的空间内部表示。海马结构中网格细胞的周期性空间放电模式为大尺度空间内的定位提供了紧凑的组合代码。在这里，我们考虑的计算问题，如何确定由发射的网格单元编码的开始和目标位置之间的矢量时，这个矢量可能比最大的网格尺度长得多。首先，我们提出了一个算法的解决方案的问题，傅立叶变换定理的启发。其次，我们描述了几个潜在的神经网络实现这种解决方案，联合收割机搜索效率和生物相容性。最后，我们讨论了这些实现的经验预测和海马结构的解剖学和电生理学的关系。网格单元（GC）被认为提供了放置单元的路径整合输入然而，GC也为大规模空间提供了强大的上下文无关度量因此，我们展示了如何将GC用于任意位置之间的向量导航我们模拟了各种神经实现并进行了可测试的实验预测网格单元被认为支持路径整合，而且还为大规模空间提供了与上下文无关的度量。在这里，布什等人。展示了网格单元如何用于矢量导航，并探索了几种潜在的神经实现的预测。
Mammals are able to navigate to hidden goal locations by direct routes that may traverse previously unvisited terrain. Empirical evidence suggests that this “vector navigation” relies on an internal representation of space provided by the hippocampal formation. The periodic spatial firing patterns of grid cells in the hippocampal formation offer a compact combinatorial code for location within large-scale space. Here, we consider the computational problem of how to determine the vector between start and goal locations encoded by the firing of grid cells when this vector may be much longer than the largest grid scale. First, we present an algorithmic solution to the problem, inspired by the Fourier shift theorem. Second, we describe several potential neural network implementations of this solution that combine efficiency of search and biological plausibility. Finally, we discuss the empirical predictions of these implementations and their relationship to the anatomy and electrophysiology of the hippocampal formation. Grid cells (GCs) are believed to provide a path integration input to place cells However, GCs also provide a powerful context-independent metric for large-scale space Hence, we show how GCs can be used for vector navigation between arbitrary locations We simulate various neural implementations and make testable experimental predictions Grid cells are thought to support path integration, but also provide a context-independent metric for large-scale space. Here, Bush et al. show how grid cells could be used for vector navigation and explore the predictions of several potential neural implementations.