Active dendrites reduce location-dependent variability of synaptic input trains.

Abstract

We examined the hypothesis that dendritic voltage-gated channels can reduce the effect synaptic location has on somatic depolarization in response to patterns of short synaptic trains (referred to as location-dependent variability). Three computer models of a reconstructed hippocampal CA1 cell, each of increasing realism and complexity, were used. For each model, the goal was to identify the dendritic composition that best reduced the location-dependent variability. The first model was linear and a single parameter, dendritic membrane conductance (GDm, where Rm = 1/GDm), was varied. Surprisingly, a negative GDm minimized the location-dependent variability. Superposition of the synaptic inputs showed that, compared with passive dendrites, active dendrites increase the mean of the individual responses while decreasing the variance between synapses at different locations. Active dendrites compensate the three components of passive cable signal interference that increase with distance from the soma: the accumulation of charge on dendritic membrane capacitance, the escape of charge across synaptic and nonsynaptic dendritic membrane conductances, and the reduction in synaptic charge entry due to increased depolarization of dendrites located farther from the soma. We also found that the entire active dendritic tree contributes charge to any one active synapse. The second model contained an artificial voltage-dependent current (Iboost) added to passive apical dendrites. The optimal amount of Iboost that minimized location-dependent variability was found to be independent of the strength of individual synaptic inputs but inversely related to the synaptic duration. In the third model, realistic T-type Ca2+ and persistent Na+ channel models were added to passive dendrites and numerically fit to reproduce the effects of Iboost. Both realistic currents minimized synaptic variability. The densities for the realistic dendritic currents were not uniform but showed subtle variations and a slight reduction with distance from the soma. A heteroassociative memory network also was modeled to demonstrate the important relationship between location-dependent variability and memory recall performance. Compared with passive dendrites, active dendrites increased memory storage by reducing recall errors. These simulations demonstrate that active dendrites can minimize the cable properties of passive dendrites and enhance the soma's ability to determine the strength of the synaptic input. These models predict dendrites that minimize location-dependent variability will have an overall negative slope conductance I-V relationship that is tuned precisely.