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首页> 外文会议>Annual Computational Neuroscience Meeting(CNS'02); 20020721-20020725; Chicago,IL; US >How does the information-geometric measure depend on underlying neural mechanisms?
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How does the information-geometric measure depend on underlying neural mechanisms?

机译：信息几何度量如何依赖于潜在的神经机制？

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摘要

To analyze how information is represented among neuron groups, a recently presented information-geometric method (IGM) has attracted growing attention. However the detailed properties underlying the information-geometric measure have not yet been elucidated because of the ill-posed nature of the problem. Here the underlying neural mechanism of the information-geometric measure is investigated with an isolated pair of model neurons. For the symmetric network, the information-geometric measure is solely dependent on the underlying anatomical connections between the recorded neurons. For the asymmetric network, however, the information-geometric measure is dependent both on the intrinsic connections and on the external inputs to it. In other words, there are multiple neural mechanisms corresponding to the same information-geometric measure. In addition, the relation between IGM and conventional cross-correlation is also investigated.
机译：为了分析信息在神经元组中的表示方式，最近提出的信息几何方法（IGM）引起了越来越多的关注。但是，由于问题的不适定性，尚未阐明信息几何度量基础的详细属性。在这里，使用一对孤立的模型神经元来研究信息几何度量的潜在神经机制。对于对称网络，信息几何度量仅取决于记录的神经元之间的基础解剖连接。但是，对于不对称网络，信息几何度量既取决于内部连接，也取决于其外部输入。换句话说，存在与同一信息几何量度相对应的多种神经机制。此外，还研究了IGM与常规互相关之间的关系。

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