Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex

Simulations of large-scale neural activity are an increasingly important tool for investigating neural network activity. Calculating measurable brain signals, like the local field potential (LFP) from such simulations is crucial because it bridges the gap between model predictions and experimental observations and helps us better understand the information content of such signals. Accurately simulating LFPs from large-scale neural network models has, however, required highly biologically detailed models, which pose significant computational challenges, and have limited their practical application. In this study, we demonstrate that a kernel-based method can accurately and efficiently estimate the LFPs simulated in a highly detailed multicompartmental network model of the mouse primary visual cortex (V1), in response to both drifting gratings and full-field flashes of light. Beyond enabling computationally efficient and accurate LFP estimation, the kernel method also aids analysis by disentangling the contributions of individual neuronal populations to the LFP. Leveraging this capability, we found that the LFP in the mouse V1 model was dominated by external synaptic inputs: feedback from lateromedial visual areas in the upper visual layers, and thalamic afferents in layer 4. In contrast, local synaptic activity from V1 neuronal families contributed only marginally to the LFP. We further demonstrated how correlations between external and local neural activity could mask this insight in experimental data. Our findings demonstrate the kernel method as an accurate tool for LFP estimation in state-of-the-art large-scale network models. Moreover, we highlight its potential to reveal novel insights into the neural mechanisms that shape measurable brain signals.