The formation and use of hierarchical cognitive maps in the brain: A neural network model

被引:5
|
作者
Jordan, Henry O. C. [1 ]
Navarro, Daniel M. [1 ]
Stringer, Simon M. [1 ]
机构
[1] Univ Oxford, Oxford Ctr Theoret Neurosci & Artificial Intellig, Dept Expt Psychol, New Radcliffe House,Woodstock Rd, Oxford OX2 6HG, England
关键词
Neural Network models; motor control; model-based behaviour; neural development; self-organization; population coding; PREFRONTAL CORTEX; ORBITOFRONTAL CORTEX; DECISION-MAKING; ENCODE; NOISE; TASK; PATH; ABSTRACTION; COMPUTATION; BEHAVIOR;
D O I
10.1080/0954898X.2020.1798531
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Many researchers have tried to model how environmental knowledge is learned by the brain and used in the form of cognitive maps. However, previous work was limited in various important ways: there was little consensus on how these cognitive maps were formed and represented, the planning mechanism was inherently limited to performing relatively simple tasks, and there was little consideration of how these mechanisms would scale up. This paper makes several significant advances. Firstly, the planning mechanism used by the majority of previous work propagates a decaying signal through the network to create a gradient that points towards the goal. However, this decaying signal limited the scale and complexity of tasks that can be solved in this manner. Here we propose several ways in which a network can can self-organize a novel planning mechanism that does not require decaying activity. We also extend this model with a hierarchical planning mechanism: a layer of cells that identify frequently-used sequences of actions and reuse them to significantly increase the efficiency of planning. We speculate that our results may explain the apparent ability of humans and animals to perform model-based planning on both small and large scales without a noticeable loss of efficiency.
引用
收藏
页码:37 / 141
页数:105
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