Parallel Readout of Pathway-Specific Inputs to Laminated Brain Structures

Local field potentials (LFPs) capture the electrical activity produced by principal cells during integration of converging synaptic inputs from multiple neuronal populations. However, since synaptic currents mix in the extracellular volume, LFPs have complex spatiotemporal structure, making them hard to exploit. Here we propose a biophysical framework to identify and separate LFP-generators. First we use a computational multineuronal model that scales up single cell electrogenesis driven by several synaptic inputs to realistic aggregate LFPs. This approach relies on the fixed but distinct locations of synaptic inputs from different presynaptic populations targeting a laminated brain structure. Thus the LFPs are contributed by several pathway-specific LFP-generators, whose electrical activity is defined by the spatial distribution of synaptic terminals and the time course of synaptic currents initiated in target cells by the corresponding presynaptic population. Then we explore the efficacy of independent component analysis to blindly separate converging sources and reconstruct pathway-specific LFP-generators. This approach can optimally locate synaptic inputs with subcellular accuracy while the reconstructed time course of pathway-specific LFP-generators is reliable in the millisecond scale. We also describe few cases where the non-linear intracellular interaction of strongly overlapping LFP-generators may lead to a significant cross-contamination and the appearance of derivative generators. We show that the approach reliably disentangle ongoing LFPs in the hippocampus into contribution of several LFP-generators. We were able to readout in parallel the pathway-specific presynaptic activity of projection cells in the entorhinal cortex and pyramidal cells in the ipsilateral and contralateral CA3. Thus we provide formal mathematical and experimental support for parallel readout of the activity of converging presynaptic populations in working neuronal circuits from common LFPs