Strösslin Thomas, Sheynikhovich Denis, Chavarriaga Ricardo, Gerstner Wulfram
Laboratory of Computational Neuroscience, Brain and Mind Centre, EPFL, 1015 Lausanne, Switzerland.
Neural Netw. 2005 Nov;18(9):1125-40. doi: 10.1016/j.neunet.2005.08.012. Epub 2005 Nov 2.
A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat.
提出了一种空间学习中海马体功能的计算模型。在探索过程中逐步获取空间表征。视觉和自身运动信息被输入到一个速率编码神经元网络中。通过位置细胞和头部方向细胞之间的无监督赫布学习，出现了一致且稳定的位置编码。基于这种表征，通过在海马体和伏隔核之间应用基于奖励的学习机制来学习目标导向导航。该模型在真实和模拟机器人上得到验证，通过使用视觉输入重新校准其路径积分器成功实现自我定位。经过约20次试验后学习到导航地图，这与大鼠在水迷宫中的情况相当。与先前的工作不同，该系统处理真实的视觉输入。定位不需要指南针，基于奖励的学习机制将离散导航模型扩展到连续空间。该模型再现了实验结果，并提出了大鼠的几种神经生理学和行为学预测。
