Population coding and decoding in a neural field: A computational study
Population coding and decoding in a neural field: A computational study
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DOI：
10.1162/089976602753633367
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发表时间：
2002-05-01
影响因子：
2.9
通讯作者：
Nakahara, H
中科院分区：
文献类型：
--
作者：
Wu, S;Amari, S;Nakahara, H
本研究使用神经场模型，调查计算方面的人口编码和解码时，刺激是一个单一的变量。提出了一个一般的编码过程的原型模型，其中神经反应是相关的，与强度指定的高斯函数的差异，在首选的刺激。基于该模型，我们研究了相关性对Fisher信息的影响，比较了三种编码信息量不同的解码方法的性能，并研究了使用递归网络实现这三种方法。该研究不仅统一地重新发现了已有文献中的主要结果，而且揭示了重要的新特征，特别是当神经元之间的相关性较强时。随着神经元放电的相关性变大，Fisher信息急剧减少。我们确认，随着相关性的宽度增加，Fisher信息饱和，不再增加神经元的数量成比例。然而，我们证明，随着宽度进一步增加--比根宽2倍转向函数的有效宽度--费舍尔信息再次增加，并且它与神经元的数量成比例地无限制地增加。此外，我们澄清的最大似然推断（MLI）类型的相关神经信号的解码方法的渐近效率。结果表明，当相关覆盖非局部总体范围时（均匀相关和噪声极小时除外），译码误差满足Cauchy型分布的MLI型方法不是渐近有效的。这意味着方差不再足以测量解码精度。
This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further-wider than root2 times the effective width of the turning function-the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.