Inferring spike trains from local field potentials
M. J. Rasch, A. Gretton, Y. Murayama, W. Maass, and N. K. Logothetis
We investigated whether it is possible to infer spike trains solely on the
basis of the underling local field potentials (LFPs). Employing 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
-range (40-90 Hz), and information contained in low-frequency
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 semi-natural
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
awake monkeys. In contrast to the cortical field potentials, thalamic LFPs
(e.g. LFPs derived from recordings in dLGN) hold no useful information
for predicting spiking activity.
Reference: M. J. Rasch, A. Gretton, Y. Murayama, W. Maass, and
N. K. Logothetis.
Inferring spike trains from local field potentials.
Journal of Neurophysiology, 99:1461-1476, 2008.