Context-Dependent Spatial Representations in the Hippocampus using Place Cell Dendritic Computation

被引:0
作者
Alabi, Adedapo [1 ]
Vanderelst, Dieter [1 ]
Minai, Ali A. [1 ]
机构
[1] Univ Cincinnati, Dept Elect Engn & Comp Sci, Cincinnati, OH 45221 USA
来源
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2022年
关键词
Hippocampus; Place cells; Spatial Cognition; Attractor Networks; Context; Dendritic Computation; Synaptic Modulation; ACTION-POTENTIAL BACKPROPAGATION; FIBER SYNAPTIC-TRANSMISSION; THETA PHASE PRECESSION; GRID CELLS; CHOLINERGIC SUPPRESSION; SELECTIVE SUPPRESSION; LATENT ATTRACTORS; PATH-INTEGRATION; PYRAMIDAL NEURON; MODEL;
D O I
10.1109/IJCNN55064.2022.9892401
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The hippocampus in rodents encodes physical space using place cells that show maximal firing in specific regions of space - their place fields. These place cells are reused across different contexts and environments with uncorrelated place fields. Though place fields are known to depend on distal sensory cues, even identical environments can have completely different place fields if the contexts are different. We propose a novel place cell network model for this feature using two frequently overlooked aspects of neural computation - dendritic morphology and the spatial co-location of spatiotemporally co-active afferent synapses - and show that these enable the reuse of place cells to encode different maps for environments with identical sensory cues.
引用
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页数:8
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