Estimation of spectro-temporal receptive fields based on linear support vector machine classification
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KeywordsSupport Vector Machine Binary Classification Zebra Finch Stimulus Pattern Linear Support Vector Machine
The spectro-temporal receptive field (STRF) of a neuron is defined as the linear filter that, when convolved with the spectro-temporal representation of an arbitrary stimulus, gives a linear estimate of the evoked firing rate . A common method for STRF estimation uses the spike-triggered average (STA) to compute the mean stimulus pattern preceding every spike.
Here, we present a method that not only considers stimulus patterns that evoke spikes but also those after which no spikes occur. This results in a binary classification problem. We show that the STRF model is equivalent to the structure of a linear support vector machine (SVM) and propose the use of SVMs for the estimation of the STRF. Based on this approach, we demonstrate that the obtained STRFs are a better predictor for spiking and non-spiking behavior of a neuron.
Methods and results
In comparison to classic STA estimation, the method proposed here is characterized by a notably finer structure in the temporal evolution of spike rate prediction. In particular the non spike-eliciting time intervals are better captured by the novel approach. This behavior is likely a result of the learning procedure employed that is based on a binary classification paradigm with a linear classifier. The averaging approach of the STA results in smoother estimates for the neuronal receptive field (due to the temporal low-pass envelope characteristics of natural stimuli), consequently producing less-detailed spike rate predictions.
This article is published under license to BioMed Central Ltd.