Grid cells generate an analog error-correcting code for singularly precise neural computation
Grid cells generate an analog error-correcting code for singularly precise neural computation
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DOI：
10.1038/nn.2901
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发表时间：
2011-10-01
影响因子：
25
通讯作者：
Fiete, Ila
中科院分区：
文献类型：
--
作者：
Sreenivasan, Sameet;Fiete, Ila
哺乳动物的内嗅网格细胞作为动物位置的函数放电，具有空间周期性的响应模式。这种位置的非局部周期性表示，即局部变量，与其他神经编码不同。没有理论解释为什么这样的代码应该存在。我们研究了带有噪声神经元的网格代码如何精确地允许理想的观察者估计位置，并发现这种代码是一种以前未知的种群代码类型，具有前所未有的对噪声的鲁棒性。特别是，网格细胞在编码范围内获得的表征准确性与观察到的感觉和运动群体编码在质量上不同。我们发现一个简单的神经网络可以有效地校正网格码。据我们所知，这些结果是第一次证明大脑包含并可能利用模拟变量的强大纠错代码。
Entorhinal grid cells in mammals fire as a function of animal location, with spatially periodic response patterns. This nonlocal periodic representation of location, a local variable, is unlike other neural codes. There is no theoretical explanation for why such a code should exist. We examined how accurately the grid code with noisy neurons allows an ideal observer to estimate location and found this code to be a previously unknown type of population code with unprecedented robustness to noise. In particular, the representational accuracy attained by grid cells over the coding range was in a qualitatively different class from what is possible with observed sensory and motor population codes. We found that a simple neural network can effectively correct the grid code. To the best of our knowledge, these results are the first demonstration that the brain contains, and may exploit, powerful error-correcting codes for analog variables.