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Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts but only moderate accuracies for individual spikes. Our aim was to utilize a data-driven approach, without relying on feature selection, to predict individual spike times. Methods: The relationship between LFPs and multi-unit spike trains in monkey early visual cortex during passive viewing of grating stimuli was analyzed using a variant of the general Volterra approach (Laguerre-Volterra network). Network parameters were trained based on a hybrid Genetic Algorithm ? Interior Point optimization method, and model selection was achieved via cross-validation. The Matthews Correlation Coefficient (-1 Results: Single trial MCCs ranged from 0.45 to 0.66 (median=0.60). Superior performance of 2nd order relative to 1st order models indicated a nonlinear relationship between LFPs and spikes in visual cortex. Consistent with other studies, the PDMs of the identified system exhibited low-pass (theta frequency) and high-pass (gamma frequency) characteristics. Conclusions: We successfully predicted multi-unit spike times from local LFPs with reasonable accuracy and without selection of a-priori features. Our approach enhances our understanding of spike precision and spike timing, and of the network principles underlying the neural code