Guazzelli A, Bota M, Arbib M A
University of Southern California Brain Project, Los Angeles, USA.
Hippocampus. 2001;11(3):216-39. doi: 10.1002/hipo.1039.
The hippocampus has long been thought essential for implementing a cognitive map of the environment. However, almost 30 years since place cells were found in rodent hippocampal field CA1, it is still unclear how such an allocentric representation arises from an ego-centrically perceived world. By means of a competitive Hebbian learning rule responsible for coding visual and path integration cues, our model is able to explain the diversity of place cell responses observed in a large set of electrophysiological experiments with a single fixed set of parameters. Experiments included changes observed in place fields due to exploration of a new environment, darkness, retrosplenial cortex inactivation, and removal, rotation, and permutation of landmarks. To code for visual cues for each landmark, we defined two perceptual schemas representing landmark bearing and distance information over a linear array of cells. The information conveyed by the perceptual schemas is further processed through a network of adaptive layers which ultimately modulate the resulting activity of our simulated place cells. In path integration terms, our system is able to dynamically remap a bump of activity coding for the displacement of the animal in relation to an environmental anchor. We hypothesize that path integration information is computed in the rodent posterior parietal cortex and conveyed to the hippocampus where, together with visual information, it modulates place cell activity. The resulting network yields a more direct treatment of partial remapping of place fields than other models. In so doing, it makes new predictions regarding the nature of the interaction between visual and path integration cues during new learning and when the system is challenged with environmental changes.
长期以来，海马体一直被认为对于构建环境认知地图至关重要。然而，自啮齿动物海马体CA1区发现位置细胞以来，近30年过去了，仍不清楚这种以自我为中心感知的世界是如何产生以客体为中心的表征的。通过负责编码视觉和路径整合线索的竞争性赫布学习规则，我们的模型能够用单一固定参数集解释在大量电生理实验中观察到的位置细胞反应的多样性。这些实验包括因探索新环境、黑暗、 retrosplenial皮层失活以及地标移除、旋转和排列而在位置野中观察到的变化。为了对每个地标的视觉线索进行编码，我们定义了两种感知模式，分别表示细胞线性阵列上的地标方位和距离信息。感知模式传达的信息通过自适应层网络进一步处理，最终调节我们模拟的位置细胞的最终活动。从路径整合的角度来看，我们的系统能够动态地重新映射编码动物相对于环境锚点位移的活动峰值。我们假设路径整合信息在啮齿动物后顶叶皮层中计算，并传递到海马体，在那里它与视觉信息一起调节位置细胞活动。与其他模型相比，由此产生的网络对位置野的部分重映射进行了更直接的处理。这样做时，它对新学习期间以及系统面临环境变化时视觉和路径整合线索之间相互作用的性质做出了新的预测。
