Deforming the hippocampal map.
Deforming the hippocampal map.
复制标题
使海马图变形。
DOI：
10.1002/hipo.20029
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Muller,RobertU
中科院分区：
文献类型：
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作者：
Touretzky,DavidS;Weisman,WendyE;Fuhs,MarkC;Skaggs,WilliamE;Fenton,AndreA;Muller,RobertU
为了研究联合刺激对位置细胞的控制，芬顿等。（J Gen Physiol 116：191-209，2000 a）记录，同时大鼠在墙上有45张黑色和白色提示卡的圆筒中觅食。卡中心相距135。在探针试验中，卡片被旋转到一起或分开25。在这些试验中，激发场中心发生了变化，认知地图也随之拉伸和收缩。芬顿等人（2000 b）用一个特别的向量场方程描述了这种变形。我们考虑什么样的神经网络机制可能能够解释他们的观察结果。在一个抽象的，最大似然公式，大鼠的位置估计的联合概率密度函数的地标位置。在吸引子神经网络模型中，循环连接在二维细胞阵列上产生活动的凸块;凸块的位置受到距离或方位等地标特征的影响。如果以适当的谨慎选择特征，则吸引子网络和最大似然模型产生类似的结果，这与先前的证明雅阁，即递归神经网络可以有效地实现最大似然计算（Pouget等人，Neural Comput 10：373-401，1998; Deneve等人，Nat Neurosci 4：826-831，2001）。
To investigate conjoint stimulus control over place cells, Fenton et al.(J Gen Physiol 116: 191–209, 2000a) recorded while rats foraged in a cylinder with 45 black and white cue cards on the wall. Card centers were 135 apart. In probe trials, the cards were rotated together or apart by 25. Firing field centers shifted during these trials, stretching and shrinking the cognitive map. Fenton et al.(2000b) described this deformation with an ad hoc vector field equation. We consider what sorts of neural network mechanisms might be capable of accounting for their observations. In an abstract, maximum likelihood formulation, the rat's location is estimated by a conjoint probability density function of landmark positions. In an attractor neural network model, recurrent connections produce a bump of activity over a two-dimensional array of cells; the bump's position is influenced by landmark features such as distances or bearings. If features are chosen with appropriate care, the attractor network and maximum likelihood models yield similar results, in accord with previous demonstrations that recurrent neural networks can efficiently implement maximum likelihood computations (Pouget et al. Neural Comput 10: 373–401, 1998; Deneve et al. Nat Neurosci 4: 826–831, 2001).© 2004 Wiley-Liss, Inc.