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The role of competitive learning in the generation of DG fields from EC inputs

机译：竞争性学习在通过EC投入产生DG领域中的作用

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We follow up on a suggestion by Rolls and co-workers, that the effects of competitive learning should be assessed on the shape and number of spatial fields that dentate gyrus (DG) granule cells may form when receiving input from medial entorhinal cortex (mEC) grid units. We consider a simple non-dynamical model where DG units are described by a threshold-linear transfer function, and receive feedforward inputs from 1,000 mEC model grid units of various spacing, orientation and spatial phase. Feedforward weights are updated according to a Hebbian rule as the virtual rodent follows a long simulated trajectory through a single environment. Dentate activity is constrained to be very sparse. We find that indeed competitive Hebbian learning tends to result in a few active DG units with a single place field each, rounded in shape and made larger by iterative weight changes. These effects are more pronounced when produced with thousands of DG units and inputs per DG unit, which the realistic system has available, than with fewer units and inputs, in which case several DG units persists with multiple fields. The emergence of single-field units with learning is in contrast, however, to recent data indicating that most active DG units do have multiple fields. We show how multiple irregularly arranged fields can be produced by the addition of non-space selective lateral entorhinal cortex (lEC) units, which are modelled as simply providing an additional effective input specific to each DG unit. The mean number of such multiple DG fields is enhanced, in particular, when lEC and mEC inputs have overall similar variance across DG units. Finally, we show that in a restricted environment the mean size of the fields is unaltered, while their mean number is scaled down with the area of the environment.
机译：我们遵循罗尔斯（Rolls）和他的同事的建议，即当接受来自内侧内嗅皮层（mEC）的输入时，应该根据齿状回（DG）颗粒细胞可能形成的空间场的形状和数量来评估竞争性学习的效果。网格单元。我们考虑一个简单的非动力学模型，其中DG单元由阈值线性传递函数描述，并从1,000个mEC模型网格单元接收前馈输入，这些网格单元具有不同的间距，方向和空间相位。当虚拟啮齿动物在单个环境中遵循较长的模拟轨迹时，前馈权重会根据Hebbian规则进行更新。牙齿活动被限制为非常稀疏。我们发现确实有竞争性的Hebbian学习趋向于导致一些活跃的DG单位，每个单位具有单个位置场，形状呈圆形并通过迭代权重变化而变大。当用现实系统可用的数千个DG单位和每个DG单位的输入来生产时，与更少的单位和输入相比，这些效果更加明显，在这种情况下，几个DG单位在多个场中持续存在。然而，具有学习功能的单场单元的出现与最近的数据形成对比，后者表明大多数活动的DG单元确实具有多个场。我们展示了如何通过添加非空间选择性侧向内嗅皮层（lEC）单元来产生多个不规则排列的场，这些单元被建模为简单地为每个DG单元提供额外的有效输入。尤其当lEC和mEC输入在DG单位之间总体上具有相似的方差时，此类多个DG字段的平均数量会增加。最后，我们表明，在受限的环境中，字段的平均大小不会改变，而字段的平均数会随环境面积的减小而缩小。

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