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Estimation of spectro-temporal receptive fields based on linear support vector machine classification
BMC Neuroscience volume 10, Article number: P147 (2009)
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
The SVM is trained using real spike data from anesthetized gerbils  and zebra finches . The parts of the stimulus spectrogram preceding a spike are labeled as class 1, whereas the remaining (non spike-evoking) parts are labeled as class 0. We used 80% of the data for training and 20% for prediction. See figure 1
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.
Theunissen FE, Sen K, Doupe AJ: Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci. 2000, 20: 2315-2331.
Happel MFK, Müller SG, Anemüller J, Ohl FW: Predictability of STRFs in auditory cortex neurons depends on stimulus class. Interspeech. 2008, 670.
STRFPAK Matlab Toolbox. [http://strfpak.berkeley.edu/]
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Meyer, A.F., Happel, M.F., Ohl, F.W. et al. Estimation of spectro-temporal receptive fields based on linear support vector machine classification. BMC Neurosci 10, P147 (2009). https://doi.org/10.1186/1471-2202-10-S1-P147
- Support Vector Machine
- Binary Classification
- Zebra Finch
- Stimulus Pattern
- Linear Support Vector Machine