Abstract：
:Sensory neurons encode external information by a series of times of action potentials, which is called a spike train. However, since it is a point process, it is hard to analyze. Here we propose a method for converting a spike train into a real-valued time series with a fixed sampling interval under the assumption of temporal codes. The proposed method yields time series that represent encoded signals. Especially when the method is applied to spike trains generated using integrate-and-fire models, the yielded time series look very similar to those of encoded information. The method works robustly even when a spike train is contaminated with noise. Since unlike filters it does not use its original signals for the conversion, the proposed method can be widely used for investigating spike train data in the real world.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Hirata Y,Aihara Kdoi
10.1016/j.jneumeth.2009.06.030subject
Has Abstractpub_date
2009-10-15 00:00:00pages
277-86issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(09)00351-3journal_volume
183pub_type
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