Testing a deep-neural network model of cognitive map development

大脑包含一个空间的“认知地图”，基于海马体中神经元的放电，海马体可以根据动物的位置和方向来调整，它可以记住目标分配并向它们导航。基于深度学习网络的人工导航代理表现出类似的空间调谐神经元，然而，目前还不知道这些网络如何随着代理的训练而发展，以及这是否反映了生物学上的现实学习和网络发展。该项目将深度神经网络建模与青少年的体内电生理记录相结合，旨在测试深度网络是否可以模拟神经网络的生物发育以进行导航。

The brain contains a 'cognitive map' of space, based on the firing of neurons in thehippocampus tuned to an animal's position and orientation, which can remember goallocations and navigate towards them. Artificial navigating agents based on deeplearningnetworks exhibit similar spatially-tuned neurons, however, it is not known howthese networks develop as the agents are trained, and whether this reflectsbiologically realistic learning and network development. This project combines deepneuralnetwork modelling with in vivo electrophysiological recording from juvenilerodents, aiming to test whether deep networks can model the biological developmentof a neural network for navigation.