Neurons are sophisticated input-output devices: their response is driven by the complex interplay between the currents directly evoked by the synaptic stimulus and intrinsic currents controlled by the internal state (e.g. voltage or Ca2+) of the cell. Moreover, the spatially extended dendritic tree of the neurons makes the state variables local. Recruiting local intrinsic currents can fundamentally change the form of input-integration. However, network models ignore the state dependence of neuronal input integration partly because of the lack of information on how intrinsic currents contribute to neuronal output under in vivo conditions. Here we developed a computational method that is able to measure the contributions of individual membrane currents to the somatic response of the neuron under in vivo-like synaptic input conditions in biophysical models. Our approach relies on the recursive decomposition of the axial current flowing between neighboring compartments in proportion of the incoming or outgoing membrane currents, depending on the axial current direction. We applied this method to a hippocampal pyramidal neuron model with realistic morphology and naturalistic inputs driving place cell-like activity. The model exhibited complex spike bursts within the place field, displaying global Ca2+ spikes invading all apical dendrites. Our decomposition technique revealed that perisomatic inhibition often prevented dendritic Na+ or Ca2+ spikes to propagate to the soma. Ca2+ spikes could be triggered in the proximal apical dendrites when local excitation overcame inhibition, but the distal tuft region initially remained functionally isolated. Later, backpropagating action potential reached the tuft recruiting more distal Ca2+ channels leading to burst firing. Our method provides compact and intuitive summary of the complex biophysical processes governing the neuronal activity under in vivo-like conditions. It will be a useful tool for testing biophysical models under realistic input regimes and to quantify the contribution of intrinsic currents to neuronal output and network dynamics.