کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408590 | 679036 | 2007 | 7 صفحه PDF | دانلود رایگان |

This work presents three kernel functions that can be used as inner product operators on non-binned spike trains, allowing the use of state-of-the-art classification techniques. One of the main advantages is that this approach does not require the spike trains to be binned. Thus a high temporal resolution is preserved which is needed when temporal coding is used. The kernels are closely related to several recent and often-used spike train metrics which take into account the biological variability of spike trains. It follows that the different existing metrics are unified by the spike train kernels presented.As a test of the classification potential of the new kernel functions, a jittered spike train template classification problem is solved.
Journal: Neurocomputing - Volume 70, Issues 7–9, March 2007, Pages 1247–1253