Aharonov-Barki R, Beker T, Ruppin E
Center for Computational Neuroscience, The Hebrew University, Jerusalem, Israel.
Neural Comput. 2001 Mar;13(3):691-716. doi: 10.1162/089976601300014529.
Using evolutionary simulations, we develop autonomous agents controlled by artificial neural networks (ANNs). In simple lifelike tasks of foraging and navigation, high performance levels are attained by agents equipped with fully recurrent ANN controllers. In a set of experiments sharing the same behavioral task but differing in the sensory input available to the agents, we find a common structure of a command neuron switching the dynamics of the network between radically different behavioral modes. When sensory position information is available, the command neuron reflects a map of the environment, acting as a location-dependent cell sensitive to the location and orientation of the agent. When such information is unavailable, the command neuron's activity is based on a spontaneously evolving short-term memory mechanism, which underlies its apparent place-sensitive activity. A two-parameter stochastic model for this memory mechanism is proposed. We show that the parameter values emerging from the evolutionary simulations are near optimal; evolution takes advantage of seemingly harmful features of the environment to maximize the agent's foraging efficiency. The accessibility of evolved ANNs for a detailed inspection, together with the resemblance of some of the results to known findings from neurobiology, places evolved ANNs as an excellent candidate model for the study of structure and function relationship in complex nervous systems.
通过进化模拟，我们开发了由人工神经网络（ANNs）控制的自主智能体。在觅食和导航等简单的逼真任务中，配备全递归ANN控制器的智能体能够达到高性能水平。在一组共享相同行为任务但智能体可用感官输入不同的实验中，我们发现了一个命令神经元的共同结构，它在截然不同的行为模式之间切换网络动态。当有感官位置信息时，命令神经元反映环境地图，充当对智能体位置和方向敏感的位置依赖细胞。当此类信息不可用时，命令神经元的活动基于一种自发演化的短期记忆机制，这是其明显的位置敏感活动的基础。为此记忆机制提出了一个双参数随机模型。我们表明，从进化模拟中出现的参数值接近最优值；进化利用环境中看似有害的特征来最大化智能体的觅食效率。进化后的ANNs便于进行详细检查，以及一些结果与神经生物学已知发现的相似性，使进化后的ANNs成为研究复杂神经系统结构与功能关系的优秀候选模型。
