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Multineuron spike train analysis with R-convolution linear combination kernel
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DOI：10.1016/j.neunet.2018.02.013.png)
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A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neurons. However, most of these multineuron extensions were carried out in a kernel-specific manner. In this paper, a general framework is proposed for extending any single-neuron spike train kernel to multineuron spike trains, based on the R-convolution kernel. Special subclasses of the proposed R-convolution linear combination kernel are explored. These subclasses have a smaller number of parameters and make optimization tractable when the size of data is limited. The proposed kernel was evaluated using Gaussian process regression for multineuron spike trains recorded from an animal brain. It was compared with the sum kernel and the population Spikernel, which are existing ways of decoding multineuron spike trains using kernels. The results showed that the proposed approach performs better than these kernels and also other commonly used neural decoding methods. A novel way of extending any single spike train kernel to multineuron spike trains is proposed. Subclasses of a kernel having fewer parameters are introduced, making optimization more feasible. Gaussian process regression with the kernel outperformed other existing decoding methods. (c) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Spike trains
Neural coding
Kernel methods
Gaussian process regression
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