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'Congruent' and 'Opposite' Neurons: Sisters for Multisensory Integration and Segregation

机译:“同等”和“相反”的神经元:多感觉整合和分离的姐妹

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Experiments reveal that in the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas, where visual and vestibular cues are integrated to infer heading direction, there are two types of neurons with roughly the same number. One is "congruent" cells, whose preferred heading directions are similar in response to visual and vestibular cues; and the other is "opposite" cells, whose preferred heading directions are nearly "opposite" (with an offset of 180°) in response to visual vs. vestibular cues. Congruent neurons are known to be responsible for cue integration, but the computational role of opposite neurons remains largely unknown. Here, we propose that opposite neurons may serve to encode the disparity information between cues necessary for multisensory segregation. We build a computational model composed of two reciprocally coupled modules, MSTd and VIP, and each module consists of groups of congruent and opposite neurons. In the model, congruent neurons in two modules are reciprocally connected with each other in the congruent manner, whereas opposite neurons are reciprocally connected in the opposite manner. Mimicking the experimental protocol, our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation. This study sheds light on our understanding of how the brain implements optimal multisensory integration and segregation concurrently in a distributed manner.
机译:实验表明,在视觉上和前庭提示相结合以推断航向的背内侧颞上(MSTd)和腹侧顶内(VIP)区域中,存在两种类型的神经元,它们的数量大致相同。一种是“全等”细胞,其优选的前进方向在视觉和前庭提示的响应上是相似的。另一个是“相反”的单元格,响应视觉和前庭提示,它们的首选航向几乎是“相反”(偏移180°)。已知一致的神经元负责提示整合,但是相反的神经元的计算作用仍然未知。在这里,我们建议相反的神经元可以用来编码多感官隔离所必需的线索之间的差异信息。我们建立了一个计算模型,该模型由两个相互耦合的模块MSTd和VIP组成,每个模块由全同和相反的神经元组成。在该模型中,两个模块中的全等神经元以全等方式相互连接,而相对的神经元则以相反的方式相互连接。模仿实验方案,我们的模型再现了全同和相反神经元的特征,并证明在每个模块中,全同和相反神经元的姐妹可以共同实现最佳的多感官信息集成和隔离。这项研究揭示了我们对大脑如何以分布式方式同时实现最佳的多感官整合和隔离的理解。

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