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Modeling of Grid Cell Activity Demonstrates in Vivo Entorhinal ‘Look-Ahead’ Properties

机译:网格单元活动的建模展示了体内Entorhinal先行特性

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摘要

Recent in vivo data show ensemble activity in medial entorhinal neurons that demonstrates ‘look-ahead’ activity, decoding spatially to reward locations ahead of a rat deliberating at a choice point while performing a cued, appetitive T-Maze task. To model this experiment's look-ahead results, we adapted previous work that produced a model where scans along equally probable directions activated place cells, associated reward cells, grid cells, and persistent spiking cells along those trajectories. Such look-ahead activity may be a function of animals performing scans to reduce ambiguity while making decisions. In our updated model, look-ahead scans at the choice point can activate goal-associated reward and place cells, which indicate the direction the virtual rat should turn at the choice point. Hebbian associations between stimulus and reward cell layers are learned during training trials, and the reward and place layers are then used during testing to retrieve goal-associated cells based on cue presentation. This system creates representations of location and associated reward information based on only two inputs of heading and speed information which activate grid cell and place cell layers. We present spatial and temporal decoding of grid cell ensembles as rats are tested with perfect and imperfect stimuli. Here, the virtual rat reliably learns goal locations through training sessions and performs both biased and unbiased look-ahead scans at the choice point. Spatial and temporal decoding of simulated medial entorhinal activity indicates that ensembles are representing forward reward locations when the animal deliberates at the choice point, emulating in vivo results.
机译:最近的体内数据显示,内侧内嗅神经元具有合奏活动,表现出“超前”活动,可以在空间上进行解码,以奖励老鼠在选择点思考的位置,同时执行提示性的,令人上瘾的T型迷宫任务。为了模拟该实验的超前结果,我们改编了先前的工作,该工作产生了一个模型,该模型沿相同的可能方向进行扫描,从而沿这些轨迹激活了位置单元格,关联的奖励单元格,网格单元格和持久峰值单元格。这种超前活动可能是动物进行扫描以减少歧义并做出决定时的功能。在我们更新的模型中,在选择点进行的超前扫描可以激活与目标相关的奖励和放置单元格,这表明虚拟大鼠应在选择点处转向。刺激和奖励细胞层之间的赫比族关联是在训练试验中学习的,然后在测试过程中使用奖励和放置层,以根据提示的表现来检索与目标相关的细胞。该系统仅基于激活网格单元和放置单元层​​的航向和速度信息的两个输入来创建位置和相关奖励信息的表示。当大鼠用完美和不完美的刺激进行测试时,我们提出了网格细胞集合体的时空解码。在这里,虚拟老鼠通过训练可靠地学习了目标位置,并在选择点执行了有偏和无偏的超前扫描。模拟的内侧内嗅神经活动的时空解码表明,当动物在选择点进行模拟(模仿体内结果)时,整体代表正向奖励位置。

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