Spiking Laguerre Volterra networks - predicting neuronal activity from local field potentials.

ABSTRACT

OBJECTIVE: Understanding the generative mechanism between Local Field Potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. APPROACH: Here, we fill this gap by proposing novel spiking Laguerre-Volterra Network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. MAIN RESULTS: The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease (PD) patients during Deep Brain Stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. SIGNIFICANCE: These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.