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首页> 美国卫生研究院文献>Frontiers in Neurorobotics >From grid cells and visual place cells to multimodal place cell: a new robotic architecture
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From grid cells and visual place cells to multimodal place cell: a new robotic architecture

机译：从网格单元和视觉位置单元到多模式位置单元：新的机器人体系结构

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In the present study, a new architecture for the generation of grid cells (GC) was implemented on a real robot. In order to test this model a simple place cell (PC) model merging visual PC activity and GC was developed. GC were first built from a simple “several to one” projection (similar to a modulo operation) performed on a neural field coding for path integration (PI). Robotics experiments raised several practical and theoretical issues. To limit the important angular drift of PI, head direction information was introduced in addition to the robot proprioceptive signal coming from the wheel rotation. Next, a simple associative learning between visual place cells and the neural field coding for the PI has been used to recalibrate the PI and to limit its drift. Finally, the parameters controlling the shape of the PC built from the GC have been studied. Increasing the number of GC obviously improves the shape of the resulting place field. Yet, other parameters such as the discretization factor of PI or the lateral interactions between GC can have an important impact on the place field quality and avoid the need of a very large number of GC. In conclusion, our results show our GC model based on the compression of PI is congruent with neurobiological studies made on rodent. GC firing patterns can be the result of a modulo transformation of PI information. We argue that such a transformation may be a general property of the connectivity from the cortex to the entorhinal cortex. Our model predicts that the effect of similar transformations on other kinds of sensory information (visual, tactile, auditory, etc…) in the entorhinal cortex should be observed. Consequently, a given EC cell should react to non-contiguous input configurations in non-spatial conditions according to the projection from its different inputs.
机译：在本研究中，用于生成网格单元（GC）的新体系结构是在真实的机器人上实现的。为了测试此模型，开发了一个将可视PC活动和GC合并的简单位置单元（PC）模型。 GC首先是根据对路径积分（PI）的神经场编码执行的简单的“几个到一个”投影（类似于模运算）构建的。机器人实验提出了一些实践和理论问题。为了限制PI的重要角度漂移，除了来自车轮旋转的机器人本体感受信号之外，还引入了头部方向信息。接下来，视觉位置单元与PI的神经场编码之间的简单关联学习已用于重新校准PI并限制其漂移。最后，研究了控制由GC建造的PC形状的参数。增加GC的数量显然可以改善所得放置场的形状。但是，其他参数（例如PI的离散因子或GC之间的横向相互作用）可能会对场所场质量产生重要影响，并避免了需要大量GC的麻烦。总之，我们的结果表明，基于PI压缩的GC模型与对啮齿类动物的神经生物学研究是一致的。 GC触发模式可能是PI信息进行模转换的结果。我们认为，这种转变可能是从皮质到内嗅皮质的连通性的一般属性。我们的模型预测应该观察到类似转化对内嗅皮层中其他种类的感觉信息（视觉，触觉，听觉等）的影响。因此，给定的EC单元应根据其不同输入的投影，在非空间条件下对非连续输入配置做出反应。

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