Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics

To generate adaptive behavior, the nervous system is coupled to the environment. The coupling constrains the dynamical properties that the nervous system and the environment must have relative to each other if adaptive behavior is to be produced. In previous computational studies, such constraints have been used to evolve controllers or artificial agents to perform a behavioral task in a given environment. Often, however, we already know the controller, the real nervous system, and its dynamics. Here we propose that the constraints can also be used to solve the inverse problem—to predict from the dynamics of the nervous system the environment to which they are adapted, and so reconstruct the production of the adaptive behavior by the entire coupled system. We illustrate how this can be done in the feeding system of the sea slug Aplysia. At the core of this system is a central pattern generator (CPG) that, with dynamics on both fast and slow time scales, integrates incoming sensory stimuli to produce ingestive and egestive motor programs. We run models embodying these CPG dynamics—in effect, autonomous Aplysia agents—in various feeding environments and analyze the performance of the entire system in a realistic feeding task. We find that the dynamics of the system are tuned for optimal performance in a narrow range of environments that correspond well to those that Aplysia encounter in the wild. In these environments, the slow CPG dynamics implement efficient ingestion of edible seaweed strips with minimal sensory information about them. The fast dynamics then implement a switch to a different behavioral mode in which the system ignores the sensory information completely and follows an internal “goal,” emergent from the dynamics, to egest again a strip that proves to be inedible. Key predictions of this reconstruction are confirmed in real feeding animals.

为产生适应性行为，神经系统与环境形成耦合。若要生成适应性行为，这种耦合会约束神经系统与环境各自所需具备的、相对于彼此的动力学特性。既往的计算研究中，这类约束常被用于演化控制器或人工智能体以在特定环境中完成行为任务。然而在多数情形下，我们已然明确该控制器——即真实的神经系统——及其动力学特性。在此我们提出，此类约束亦可用于解决逆问题：即从神经系统的动力学特性出发，预测其适配的环境，进而重构整个耦合系统产生适应性行为的完整过程。我们以海兔（Aplysia）的摄食系统为示例，展示了该方法的实现路径。该系统的核心为中枢模式发生器（CPG），其兼具快、慢两种时间尺度的动力学特性，可通过整合传入的感觉刺激生成摄食性与排遗性运动程序。我们在多种摄食环境中运行嵌入该CPG动力学特性的模型——本质上为自主海兔智能体——并分析整个系统在真实摄食任务中的表现。研究结果显示，该系统的动力学经过优化调谐，仅在一类狭窄的环境范围内可实现最优性能，且此类环境与海兔在野外自然环境中遭遇的摄食环境高度契合。在这类环境中，慢时程的CPG动力学可实现对可食用海藻条的高效摄取，仅需极少量关于食物的感觉信息；而快时程的动力学则会触发行为模式切换：此时系统会完全忽略感觉输入，转而遵循由动力学自发涌现的内部"目标"，将被证实不可食用的海藻条排出。我们通过真实海兔的摄食实验，验证了该重构方法得出的关键预测。