Institute of Intelligent Systems and Robotics, Sorbonne Université, CNRS, F-75005 Paris, France.
Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, Institute of Bioengineering and School of Engineering, EPFL, Geneva, Switzerland.
PLoS Comput Biol. 2018 Apr 9;14(4):e1006092. doi: 10.1371/journal.pcbi.1006092. eCollection 2018 Apr.
We present a computational model of spatial navigation comprising different learning mechanisms in mammals, i.e., associative, cognitive mapping and parallel systems. This model is able to reproduce a large number of experimental results in different variants of the Morris water maze task, including standard associative phenomena (spatial generalization gradient and blocking), as well as navigation based on cognitive mapping. Furthermore, we show that competitive and cooperative patterns between different navigation strategies in the model allow to explain previous apparently contradictory results supporting either associative or cognitive mechanisms for spatial learning. The key computational mechanism to reconcile experimental results showing different influences of distal and proximal cues on the behavior, different learning times, and different abilities of individuals to alternatively perform spatial and response strategies, relies in the dynamic coordination of navigation strategies, whose performance is evaluated online with a common currency through a modular approach. We provide a set of concrete experimental predictions to further test the computational model. Overall, this computational work sheds new light on inter-individual differences in navigation learning, and provides a formal and mechanistic approach to test various theories of spatial cognition in mammals.
我们提出了一个包含哺乳动物不同学习机制的空间导航计算模型，即联想、认知映射和并行系统。该模型能够再现 Morris 水迷宫任务不同变体中的大量实验结果，包括标准联想现象（空间泛化梯度和阻断），以及基于认知映射的导航。此外，我们表明，模型中不同导航策略之间的竞争和合作模式可以解释先前支持空间学习的联想或认知机制的明显矛盾的结果。协调表现出不同距离和近端线索对行为的不同影响、不同的学习时间以及个体替代执行空间和响应策略的不同能力的实验结果的关键计算机制，依赖于导航策略的动态协调，其性能通过模块化方法使用共同货币在线进行评估。我们提供了一系列具体的实验预测，以进一步测试计算模型。总的来说，这项计算工作为导航学习中的个体差异提供了新的见解，并为测试哺乳动物空间认知的各种理论提供了一种形式化和机制化的方法。
