Artificial Neural Networks - ICANN 2008
Artificial Neural Networks - ICANN 2008
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人工神经网络 - ICANN 2008
DOI：
10.1007/978-3-540-87559-8_57
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Yin H
中科院分区：
文献类型：
--
作者：
Yin H
在本文中，使用拓扑聚类解码人口神经元的反应和减少刺激功能。离散的尖峰列车，记录在大鼠体感皮层中的正弦振动刺激，其特征在于不同的频率和幅度，首先解释为连续的时间活动，通过卷积与衰减指数滤波器。然后利用自组织映射对连续响应进行聚类。结果是相对于刺激的响应的拓扑有序聚类。这种聚类主要是沿着刺激的幅度和频率的乘积形成的。这种分组与先前基于尖峰计数和互信息获得的能量编码结果一致。为了进一步研究聚类如何保留信息，计算了所得刺激分组和响应之间的互信息。聚类的累积互信息与能量分组的累积互信息非常相似。这表明，拓扑聚类可以自然地发现潜在的刺激-反应模式，并保留集群之间的信息。
In this paper the use of topological clustering for decoding population neuronal responses and reducing stimulus features is described. The discrete spike trains, recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterised by different frequencies and amplitudes, are first interpreted to continuous temporal activities by convolving with a decaying exponential filter. Then the self-organising map is utilised to cluster the continuous responses. The result is a topologically ordered clustering of the responses with respect to the stimuli. The clustering is formed mainly along the product of amplitude and frequency of the stimuli. Such grouping agrees with the energy coding result obtained previously based on spike counts and mutual information. To further investigate how the clustering preserves information, the mutual information between resulting stimulus grouping and responses has been calculated. The cumulative mutual information of the clustering resembles closely that of the energy grouping. It suggests that topological clustering can naturally find underlying stimulus-response patterns and preserve information among the clusters.