A biologically inspired hierarchical goal directed navigation model.
A biologically inspired hierarchical goal directed navigation model.
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DOI：
10.1016/j.jphysparis.2013.07.002
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发表时间：
2014-02
影响因子：
--
通讯作者：
Hasselmo, Michael E.
中科院分区：
文献类型：
--
作者：
Erdem, Ugur M.;Hasselmo, Michael E.
我们提出了一个扩展版本的目标导向导航模型，该模型基于对头部方向单元、持续尖峰单元、网格单元和位置单元网络中的轨迹的前向规划。在我们的原著中，Animat通过对新环境的随机探索，逐渐创建了一个地点细胞地图。在探索阶段之后，Animat通过在几个候选方向上探测线性前瞻轨迹，同时静止并挑选代表目标位置的一个激活的位置单元，来决定其下一个朝向目标的移动方向。在这项工作中，我们提出了对以前模型的几个改进。我们通过在位置细胞图上施加一种层次结构，显著地改善了线性前瞻探测的范围，这与实验结果一致，即沿内嗅皮层背侧到腹轴的不同位置记录的网格细胞的激发野大小和间距存在差异。新模型通过不同激发野大小的模拟海马区细胞群来表示不同尺度上的环境。除其他优点外，该模型还允许以不同的比例同时进行恒定持续时间的线性前瞻探测，同时显著扩展每个探测范围。线性前瞻探测范围的扩展，同时保持其持续时间恒定，也限制了网络中噪声累积的降级影响。我们使用Animat演示了扩展模型在大型开阔场地环境中的性能。
We propose an extended version of our previous goal directed navigation model based on forward planning of trajectories in a network of head direction cells, persistent spiking cells, grid cells, and place cells. In our original work the animat incrementally creates a place cell map by random exploration of a novel environment. After the exploration phase, the animat decides on its next movement direction towards a goal by probing linear look-ahead trajectories in several candidate directions while stationary and picking the one activating place cells representing the goal location. In this work we present several improvements over our previous model. We improve the range of linear look-ahead probes significantly by imposing a hierarchical structure on the place cell map consistent with the experimental findings of differences in the firing field size and spacing of grid cells recorded at different positions along the dorsal to ventral axis of entorhinal cortex. The new model represents the environment at different scales by populations of simulated hippocampal place cells with changing firing field sizes. Among other advantages this model allows simultaneous constant duration linear look-ahead probes at different scales while significantly extending each probe range. The extension of the linear look-ahead probe range while keeping its duration constant also limits the degrading effects of noise accumulation in the network. We show the extended model’s performance using an animat in a large open field environment.
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影响因子：
7.8
作者：
Eustice, Ryan M.;Singh, Hanumant;Leonard, John J.
通讯作者：
Leonard, John J.
影响因子：
3.5
作者：
Hasselmo, Michael E.
通讯作者：
Hasselmo, Michael E.
影响因子：
--
作者：
Brown, Michael F.
通讯作者：
Brown, Michael F.
影响因子：
4.1
作者：
Fallon, Maurice F.;Folkesson, John;Leonard, John J.
通讯作者：
Leonard, John J.
DOI：
10.1111/j.1460-9568.2012.08015.x
发表时间：
2012-03
期刊：
The European journal of neuroscience
影响因子：
--
作者：
Erdem UM;Hasselmo M
通讯作者：
Hasselmo M