By Douglas Zhou, Shanghai Jiao Tong University

A neuron receives thousands of synaptic inputs from its dendrite and integrates them to process information. Many experimental results demonstrate the dendritic integration could be highly nonlinear, yet few theoretical analyses have been performed to obtain a precise quantitative characterization analytically. Based on asymptotic analysis of a passive cable model, we derive a bilinear spatiotemporal dendritic integration rule for a pair of time-dependent synaptic inputs. Surprisingly, the above rule, which is obtained from idealized models, can be verified both in simulations of a realistic pyramidal neuron model and in electrophysiological experiments of rat hippocampal CA1 neurons. Our results demonstrate that the integration of multiple synaptic inputs can be decomposed into the sum of all possible pairwise integration with each paired integration obeying a bilinear rule.