Centre for Neuroscience, Indian Institute of Science, Bangalore, India.
J Neurophysiol. 2022 Apr 1;127(4):869-884. doi: 10.1152/jn.00160.2021. Epub 2022 Feb 23.
Reading distorted letters is easy for us but so challenging for the machine vision that it is used on websites as CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart). How does our brain solve this problem? One solution is to have neurons selective for letter combinations but invariant to distortions. Another is for neurons to encode letter distortions and longer strings to enable separable decoding. Here, we provide evidence for the latter possibility using neural recordings in the monkey inferior temporal (IT) cortex. Neural responses to distorted strings were explained better as a product (but not sum) of shape and distortion tuning, whereas by contrast, responses to letter combinations were explained better as a sum (but not product) of letters. These two rules were sufficient for perfect CAPTCHA decoding and were also emergent in neural networks trained for word recognition. Thus, a separable neural code enables efficient letter recognition. Many websites ask us to recognize distorted letters to deny access to malicious computer programs. Why is this task easy for our brains but hard for the computers? Here, we show that, in the monkey inferior temporal cortex, an area critical for recognition, single neurons encode distorted letter strings according to highly systematic rules that enable perfect distorted letter decoding. Remarkably, the same rules were present in neural networks trained for text recognition.
阅读扭曲的字母对我们来说很容易，但机器视觉却很难做到，因此它被用于网站上的验证码（完全自动化的公共图灵测试，以区分计算机和人类）。我们的大脑是如何解决这个问题的呢？一种解决方案是让神经元对字母组合具有选择性，但对扭曲不变。另一种方法是让神经元对字母扭曲和更长的字符串进行编码，以实现可分离的解码。在这里，我们使用猴子下颞叶（IT）皮层的神经记录提供了后者可能性的证据。扭曲字符串的神经反应可以更好地解释为形状和扭曲调谐的乘积（而不是和），相比之下，字母组合的反应可以更好地解释为字母的和（而不是积）。这两个规则足以实现完美的验证码解码，并且在为单词识别而训练的神经网络中也出现了这种情况。因此，可分离的神经代码能够实现高效的字母识别。许多网站要求我们识别扭曲的字母，以拒绝恶意计算机程序的访问。为什么这个任务对我们的大脑来说很容易，但对计算机来说却很难？在这里，我们表明，在猴子的下颞叶皮层中，一个对识别至关重要的区域，单个神经元根据高度系统的规则对扭曲的字母串进行编码，从而实现完美的扭曲字母解码。值得注意的是，相同的规则也存在于为文本识别而训练的神经网络中。
