仓鸮的周期性神经编码与声音定位
Periodic Neural Codes and Sound Localization in Barn Owls
摘要：
受仓鸮的声音定位系统的启发，我们定义了一类新的神经代码，称为周期码，并研究了它们的基本性质。周期码是具有特殊图案形式的二进制码，其反映刺激的周期性。由于这些代码可以使用的猫头鹰本地化的角度在一个凸集的声音，我们调查他们是否是凸代码的例子，这是以前研究海马位置细胞。我们发现，周期码通常不是凸的，但可以完成凸码的存在噪声。引入凸闭包和汉明距离完备化作为增加码字使码凸化的方法，并描述了周期码的凸闭包。我们还发现，概率的凸封闭产生随机的稀疏码更大。最后，我们提供了一个代数方法使用的神经理想来检测如果一个代码是周期性的。我们发现，周期代码的属性有助于解释几个方面的行为观察到的声音定位系统的仓鸮，包括常见的错误，在本地化的纯音。
英文摘要：
Inspired by the sound localization system of the barn owl, we define a new class of neural codes, called periodic codes, and study their basic properties. Periodic codes are binary codes with a special patterned form that reflects the periodicity of the stimulus. Because these codes can be used by the owl to localize sounds within a convex set of angles, we investigate whether they are examples of convex codes, which have previously been studied for hippocampal place cells. We find that periodic codes are typically not convex, but can be completed to convex codes in the presence of noise. We introduce the convex closure and Hamming distance completion as ways of adding codewords to make a code convex, and describe the convex closure of a periodic code. We also find that the probability of the convex closure arising stochastically is greater for sparser codes. Finally, we provide an algebraic method using the neural ideal to detect if a code is periodic. We find that properties of periodic codes help to explain several aspects of the behavior observed in the sound localization system of the barn owl, including common errors in localizing pure tones.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
