Frank Steven A
Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697-2525, USA.
Entropy (Basel). 2023 Aug 3;25(8):1162. doi: 10.3390/e25081162.
Organisms perceive their environment and respond. The origin of perception-response traits presents a puzzle. Perception provides no value without response. Response requires perception. Recent advances in machine learning may provide a solution. A randomly connected network creates a reservoir of perceptive information about the recent history of environmental states. In each time step, a relatively small number of inputs drives the dynamics of the relatively large network. Over time, the internal network states retain a memory of past inputs. To achieve a functional response to past states or to predict future states, a system must learn only how to match states of the reservoir to the target response. In the same way, a random biochemical or neural network of an organism can provide an initial perceptive basis. With a solution for one side of the two-step perception-response challenge, evolving an adaptive response may not be so difficult. Two broader themes emerge. First, organisms may often achieve precise traits from sloppy components. Second, evolutionary puzzles often follow the same outlines as the challenges of machine learning. In each case, the basic problem is how to learn, either by artificial computational methods or by natural selection.
生物体感知其环境并做出反应。感知 - 反应特性的起源是一个谜题。没有反应，感知就没有价值。反应需要感知。机器学习的最新进展可能提供一个解决方案。一个随机连接的网络创建了一个关于环境状态近期历史的感知信息库。在每个时间步，相对较少的输入驱动相对较大网络的动态变化。随着时间的推移，内部网络状态保留了过去输入的记忆。为了对过去的状态实现功能性反应或预测未来的状态，系统只需学习如何将信息库的状态与目标反应相匹配。同样，生物体的随机生化或神经网络可以提供一个初始的感知基础。解决了感知 - 反应两步挑战中的一个方面后，进化出适应性反应可能就不那么困难了。出现了两个更广泛的主题。首先，生物体常常能从不精确的组件中获得精确的特性。其次，进化谜题往往与机器学习的挑战遵循相同的轮廓。在每种情况下，基本问题都是如何学习，无论是通过人工计算方法还是自然选择。
