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通过电感受神经网络中的稀疏编码进行高效计算。

Efficient computation via sparse coding in electrosensory neural networks.

机构信息

Department of Physiology, McGill University, Canada.

出版信息

Curr Opin Neurobiol. 2011 Oct;21(5):752-60. doi: 10.1016/j.conb.2011.05.016. Epub 2011 Jun 16.

Abstract

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process.

摘要

电感觉将视觉和触觉的空间方面与听觉的时间特征结合在一起。其易于接近的神经结构与哺乳动物感觉系统具有相似之处,并且可以进行来自连续脑区的记录,以检验有关神经编码的假设。此外,在猎物捕获、导航和通信过程中遇到的电感觉刺激可以在实验室中轻松合成。这些特性使能够分析揭示编码和解码一般原理的神经回路,例如将信息分离成单独的流和神经响应稀疏化。通过体外和体内分析揭示的细胞分化和网络架构之间的联系,产生系统水平的理解,而计算模型则揭示了单个细胞动力学和连接如何塑造稀疏化过程。

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