Department of Brain Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.
Faculty of Electrical Engineering, Holon Institute of Technology, Holon 5810201, Israel.
Proc Natl Acad Sci U S A. 2024 Oct 8;121(41):e2319709121. doi: 10.1073/pnas.2319709121. Epub 2024 Oct 2.
Central nervous system neurons manifest a rich diversity of selectivity profiles-whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution-well established in biology-to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.
中枢神经系统神经元表现出丰富多样的选择性模式——其确切作用仍知之甚少。在人工神经网络取得惊人成功之后，出现了一场关于它们在解释神经元特性方面的有用性的大辩论。在这里，我们提出，在人工网络和神经元网络之间找到相似之处是有启发性的，正是因为这些系统彼此之间有很大的不同。我们的论点基于将生物中已经确立的趋同进化概念扩展到人工系统领域。将这一概念应用于皮质层次的不同领域和层次，可以成为阐明众所周知的皮质选择性的功能作用的有力工具。重要的是，我们通过表明内嗅皮层中的网格细胞可以被建模为在有损表示（如著名的 JPEG 压缩）中作为一组基函数来工作，进一步证明了这种相似性可以揭示新的功能。因此，与普遍的直觉相反，在这里我们说明，与人工系统找到相似之处提供了新颖而有启发性的见解，特别是在那些远离现实脑生物学的情况下。
