Blair Hugh T, Gupta Kishan, Zhang Kechen
Psychology Department, University of California, Los Angeles, California 90095-1563, USA.
Hippocampus. 2008;18(12):1239-55. doi: 10.1002/hipo.20509.
As a rat navigates through a familiar environment, its position in space is encoded by firing rates of place cells and grid cells. Oscillatory interference models propose that this positional firing rate code is derived from a phase code, which stores the rat's position as a pattern of phase angles between velocity-modulated theta oscillations. Here we describe a three-stage network model, which formalizes the computational steps that are necessary for converting phase-coded position signals (represented by theta oscillations) into rate-coded position signals (represented by grid cells and place cells). The first stage of the model proposes that the phase-coded position signal is stored and updated by a bank of ring attractors, like those that have previously been hypothesized to perform angular path integration in the head-direction cell system. We show analytically how ring attractors can serve as central pattern generators for producing velocity-modulated theta oscillations, and we propose that such ring attractors may reside in subcortical areas where hippocampal theta rhythm is known to originate. In the second stage of the model, grid fields are formed by oscillatory interference between theta cells residing in different (but not the same) ring attractors. The model's third stage assumes that hippocampal neurons generate Gaussian place fields by computing weighted sums of inputs from a basis set of many grid fields. Here we show that under this assumption, the spatial frequency spectrum of the Gaussian place field defines the vertex spacings of grid cells that must provide input to the place cell. This analysis generates a testable prediction that grid cells with large vertex spacings should send projections to the entire hippocampus, whereas grid cells with smaller vertex spacings may project more selectively to the dorsal hippocampus, where place fields are smallest.
当一只大鼠在熟悉的环境中导航时，其在空间中的位置由位置细胞和网格细胞的放电频率编码。振荡干扰模型提出，这种位置放电频率编码源自相位编码，相位编码将大鼠的位置存储为速度调制的θ振荡之间的相位角模式。在这里，我们描述了一个三阶段网络模型，该模型将把相位编码的位置信号（由θ振荡表示）转换为频率编码的位置信号（由网格细胞和位置细胞表示）所需的计算步骤形式化。模型的第一阶段提出，相位编码的位置信号由一组环形吸引子存储和更新，就像之前假设在头部方向细胞系统中执行角路径积分的那些吸引子一样。我们通过分析表明环形吸引子如何作为中央模式发生器来产生速度调制的θ振荡，并且我们提出这种环形吸引子可能存在于已知海马θ节律起源的皮层下区域。在模型的第二阶段，网格场由位于不同（但不是相同）环形吸引子中的θ细胞之间的振荡干扰形成。模型的第三阶段假设海马神经元通过计算来自许多网格场基集的输入的加权和来生成高斯位置场。在这里我们表明，在这个假设下，高斯位置场的空间频率谱定义了必须向位置细胞提供输入的网格细胞的顶点间距。这一分析产生了一个可测试的预测，即具有大顶点间距的网格细胞应该向整个海马体发送投射，而具有较小顶点间距的网格细胞可能更有选择性地投射到背侧海马体，那里的位置场最小。
