Borisyuk Roman, Merrison-Hort Robert, Soffe Steve R, Koutsikou Stella, Li Wen-Chang
School of Computing, Electronics and Mathematics, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK; Institute of Mathematical Problems of Biology, The Branch of Keldysh Institute of Applied Mathematics of Russian Academy of Sciences, Pushchino, 142290, Russia.
School of Computing, Electronics and Mathematics, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, UK.
Biosystems. 2017 Nov;161:3-14. doi: 10.1016/j.biosystems.2017.07.004. Epub 2017 Jul 15.
We present a detailed computational model of interacting neuronal populations that mimic the hatchling Xenopus tadpole nervous system. The model includes four sensory pathways, integrators of sensory information, and a central pattern generator (CPG) network. Sensory pathways of different modalities receive inputs from an "environment"; these inputs are then processed and integrated to select the most appropriate locomotor action. The CPG populations execute the selected action, generating output in motor neuron populations. Thus, the model describes a detailed and biologically plausible chain of information processing from external signals to sensors, sensory pathways, integration and decision-making, action selection and execution and finally, generation of appropriate motor activity and behaviour. We show how the model produces appropriate behaviours in response to a selected scenario, which consists of a sequence of "environmental" signals. These behaviours might be relatively complex due to noisy sensory pathways and the possibility of spontaneous actions.
我们提出了一个详细的相互作用神经元群体计算模型,该模型模拟了孵化后的非洲爪蟾蝌蚪神经系统。该模型包括四条感觉通路、感觉信息整合器和一个中枢模式发生器(CPG)网络。不同模态的感觉通路从“环境”接收输入;然后这些输入被处理和整合,以选择最合适的运动动作。CPG群体执行选定的动作,在运动神经元群体中产生输出。因此,该模型描述了一个从外部信号到传感器、感觉通路、整合与决策、动作选择与执行,最后到产生适当运动活动和行为的详细且生物学上合理的信息处理链。我们展示了该模型如何响应由一系列“环境”信号组成的选定场景产生适当的行为。由于感觉通路存在噪声以及自发动作的可能性,这些行为可能相对复杂。