Center for Adaptive Systems, Department of Cognitive and Neural Systems, and Center of Excellence for Learning in Education, Boston University, Boston, MA 02215, USA.
Hippocampus. 2012 Dec;22(12):2219-37. doi: 10.1002/hipo.22040. Epub 2012 Jun 18.
Effective navigation depends upon reliable estimates of head direction (HD). Visual, vestibular, and outflow motor signals combine for this purpose in a brain system that includes dorsal tegmental nucleus, lateral mammillary nuclei, anterior dorsal thalamic nucleus, and the postsubiculum. Learning is needed to combine such different cues to provide reliable estimates of HD. A neural model is developed to explain how these three types of signals combine adaptively within the above brain regions to generate a consistent and reliable HD estimate, in both light and darkness, which explains the following experimental facts. Each HD cell is tuned to a preferred head direction. The cell's firing rate is maximal at the preferred direction and decreases as the head turns from the preferred direction. The HD estimate is controlled by the vestibular system when visual cues are not available. A well-established visual cue anchors the cell's preferred direction when the cue is in the animal's field of view. Distal visual cues are more effective than proximal cues for anchoring the preferred direction. The introduction of novel cues in either a novel or familiar environment can gain control over a cell's preferred direction within minutes. Turning out the lights or removing all familiar cues does not change the cell's firing activity, but it may accumulate a drift in the cell's preferred direction. The anticipated time interval (ATI) of the HD estimate is greater in early processing stages of the HD system than at later stages. The model contributes to an emerging unified neural model of how multiple processing stages in spatial navigation, including postsubiculum head direction cells, entorhinal grid cells, and hippocampal place cells, are calibrated through learning in response to multiple types of signals as an animal navigates in the world.
有效导航依赖于对头方向（HD）的可靠估计。视觉、前庭和输出运动信号在包括背侧被盖核、外侧乳状核、前背丘脑核和 Postsubiculum 的大脑系统中结合用于此目的。需要学习如何将这些不同的线索结合起来，以便对头方向做出可靠的估计。为了解释上述三种类型的信号如何在上述大脑区域内自适应地结合，以在光明和黑暗中产生一致且可靠的 HD 估计，开发了一种神经模型，该模型解释了以下实验事实。每个 HD 细胞都调谐到一个首选的头方向。当头部从首选方向转动时，细胞的发射率在首选方向上最大，并减小。当视觉线索不可用时，HD 估计由前庭系统控制。当线索在动物的视野内时，已建立的视觉线索会固定细胞的首选方向。远端视觉线索比近端线索更有效地固定首选方向。在新环境或熟悉的环境中引入新线索可以在几分钟内控制细胞的首选方向。关灯或移除所有熟悉的线索不会改变细胞的发射活动，但可能会导致细胞首选方向的漂移。在 HD 系统的早期处理阶段，HD 估计的预期时间间隔（ATI）大于后期阶段。该模型有助于形成一个新兴的统一神经模型，说明在动物在世界中导航时，空间导航的多个处理阶段（包括 Postsubiculum 头方向细胞、内嗅网格细胞和海马位置细胞）如何通过学习来响应多种类型的信号进行校准。
