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首页> 外文期刊>The Journal of Neuroscience: The Official Journal of the Society for Neuroscience >Anticipatory Neural Activity Improves the Decoding Accuracy for Dynamic Head-Direction Signals
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Anticipatory Neural Activity Improves the Decoding Accuracy for Dynamic Head-Direction Signals

机译:预期神经活动可提高动态头向信号的解码精度

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摘要

Insects and vertebrates harbor specific neurons that encode the animal's head direction (HD) and provide an internal compass for spatial navigation. Each HD cell fires most strongly in one preferred direction. As the animal turns its head, however, HD cells in rat anterodorsal thalamic nucleus (ADN) and other brain areas fire already before their preferred direction is reached, as if the neurons anticipated the future HD. This phenomenon has been explained at a mechanistic level, but a functional interpretation is still missing. To close this gap, we use a computational approach based on the movement statistics of male rats and a simple model for the neural responses within the ADN HD network. Network activity is read out using population vectors in a biologically plausible manner, so that only past spikes are taken into account. We find that anticipatory firing improves the representation of the present HD by reducing the motion-induced temporal bias inherent in causal decoding. The amount of anticipation observed in ADN enhances the precision of the HD compass read-out by up to 40%. More generally, our theoretical framework predicts that neural integration times not only reflect biophysical constraints, but also the statistics of behaviorally relevant stimuli; in particular, anticipatory tuning should be found wherever neurons encode sensory signals that change gradually in time.
机译:昆虫和脊椎动物涉及编码动物的头部方向(HD)的特异性神经元,并为空间导航提供内部指南针。每个高清细胞在一个优选方向上最强烈地触发。然而,随着动物转动其头部,在达到其优选方向之前,大鼠亚脑菌核(ADN)和其他脑区域的HD细胞和其他脑区域发生火灾,就像神经元预期未来的高清一样。这种现象已经在机械层面解释,但仍缺少功能解释。为了缩短这种差距,我们使用基于雄性大鼠的运动统计的计算方法和ADN高清网络内神经响应的简单模型。在生物合理的方式中使用人口向量读出网络活动,因此仅考虑过去的尖峰。我们发现,预期发射通过减少因果解码中固有的运动诱导的时间偏差来改善本HD的表示。 ADN中观察到的预期金额增强了HD Compass的精度读出高达40%。更一般地,我们的理论框架预测神经整合时间不仅反映了生物物理限制,而且是行为相关刺激的统计数据;特别地,应该在神经元编码随时间逐渐变化的感觉信号的情况下发现预期调谐。

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