Dendritic Integration of Synaptic Inputs in Biological Neurons

A single neuron may receive and integrate thousands of excitatory (E) and inhibitory (I) synaptic inputs from its dendritic tree. In order to understand how neurons process information efficiently, we have provided a simple point-neuron model that can account for dendritic integration effects as observed from our collaborator’s laboratory.

We further build an idealized neuron model with an unbranched dendritic cable to reveal the underlying biophysical mechanism of a pair of excitatory and inhibitory synaptic input integration phenomena observed in experiment. By constructing its Green’s function and carrying out an asymptotic analysis to obtain its solutions, we extend the spatial dependence of E-I integration to multi-branched case and confirm our results by running realistic neuron simulation.

We derive a spatiotemporal dendritic integration rule for rat hippocampal CA1 neurons with all synaptic-input types, including excitation-inhibition, excitation-excitation, inhibition-inhibition, and multiple excitatory and inhibitory inputs. We then validate our dendritic integration rule through both realistic neuron modeling and electrophysiological experiments. This rule leads to a graph representation of dendritic integration that exhibits functionally sparse properties.