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Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems

机译:学习神经系统中冗余信号的对比度不变消除

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摘要

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.
机译:冗余信息的取消是感觉系统的高度期望的特征,因为它可能导致更有效地检测新信息。然而,造成这种选择性消除的生物学上合理的机制,尤其是那些对冗余信号强度的实际变化具有鲁棒性的机制,在大多数情况下是未知的。在这项工作中,我们通过体内实验记录和计算模型研究了弱电鱼中小脑样电路的行为,该电路已知可以消除多余的刺激。我们实验观察到对比度不变性在这种系统中消除时空冗余刺激。我们的模型结合了异质延迟反馈,爆发动力学和爆发诱导的STDP,与我们的体内观察结果一致。此外,该模型可以洞悉参与反馈途径的颗粒细胞和平行纤维的活性,并为平行纤维增强时间尺度提供强有力的预测。最后,我们的模型预测最佳学习对比度的存在约为15%的对比度水平,这是鱼互动所通常经历的。

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