Walters D M, Stringer S M
Department of Experimental Psychology, Oxford Centre for Theoretical Neuroscience and Artificial Intelligence, Oxford OX1 3UD, UK.
Biol Cybern. 2010 Jul;103(1):21-41. doi: 10.1007/s00422-009-0355-0. Epub 2010 May 26.
A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.
理解路径整合神经基础的一个关键问题是，单个具有空间响应能力的神经元如何自组织成网络，通过学习整合速度信号，以更新环境中位置的连续表征。至关重要的是，这种位置的内部表征要以正确的速度实时更新，以准确反映动物的运动。在本文中，我们提出了一个关于头部方向速度路径整合的生物学上合理的模型，该模型可以利用神经元时间常数产生自然时间延迟来解决这个问题，通过联想赫布学习规则可以在这些时间延迟上学习关联。该模型由一个相连的连续吸引子网络和竞争网络组成。在模拟中，我们表明，当使用两个不同的时间常数实现时，同一个模型能够学习两种不同的旋转速度，且无需改变任何其他模型参数。所提出的模型可以扩展到环境中位置的路径整合以及空间视图的路径整合。
