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Abstract:
We investigated whether it is possible to
infer spike trains solely on the basis of the underlying local field
potentials (LFPs). Using support vector machines and linear regression
models, we found that in the primary visual cortex (V1) of
monkeys, spikes can indeed be inferred from LFPs, at least with
moderate success. Although there is a considerable degree of variation
across electrodes, the low-frequency structure in spike trains (in the
100-ms range) can be inferred with reasonable accuracy, whereas
exact spike positions are not reliably predicted. Two kinds of features
of the LFP are exploited for prediction: the frequency power of bands
in the high gamma-range (40amp;amp;amp;amp;amp;8211;90 Hz) and information contained in lowfrequency
oscillations ( 10 Hz), where both phase and power modulations
are informative. Information analysis revealed that both
features code (mainly) independent aspects of the spike-to-LFP relationship,
with the low-frequency LFP phase coding for temporally
clustered spiking activity. Although both features and prediction
quality are similar during seminatural movie stimuli and spontaneous
activity, prediction performance during spontaneous activity degrades
much more slowly with increasing electrode distance. The general
trend of data obtained with anesthetized animals is qualitatively
mirrored in that of a more limited data set recorded in V1 of non-anesthetized
monkeys. In contrast to the cortical field potentials, thalamic LFPs
(e.g., LFPs derived from recordings in the dorsal lateral geniculate
nucleus) hold no useful information for predicting spiking activity.
