Model-based signal processing enables bidirectional inferring between local field potential and spikes evoked by...

Recording spontaneous and evoked activities by means of unitary extracellular recordings and local field potential (LFP) are key understanding the mechanisms of neural coding. The LFP is one of the most popular and easy methods to measure the activity of a population of neurons. LFP is also a composite signal known to be difficult to interpret and model. There is a growing need to highlight the relationship between spiking activity and LFP. Here, we hypothesized that LFP could be inferred from spikes under evoked noxious conditions.

Recording was performed from the medullary dorsal horn (MDH) in deeply anesthetized rats. We detail a process to highlight the C-fiber (nociceptive) evoked activity, by removing the A-fiber evoked activity using a model-based approach. Then, we applied the convolution kernel theory and optimization algorithms to infer the C-fiber LFP from the single cell spikes. Finally, we used a probability density function and an optimization algorithm to infer the spikes distribution from the LFP.

We successfully extracted C-fiber LFP in all data recordings. We observed that C-fibers spikes preceded the C-fiber LFP and were rather correlated to the LFP derivative. Finally, we inferred LFP from spikes with excellent correlation coefficient (r = 0.9) and reverse generated the spikes distribution from LFP with good correlation coefficients (r = 0.7) on spikes number.

We introduced the kernel convolution theory to successfully infer the LFP from spikes, and we demonstrated that we could generate the spikes distribution from the LFP.