Active dendrites enhance neuronal dynamic range

Abstract

Since the first experimental evidences of active conductances in dendrites, most neurons have been shown to exhibit dendritic excitability through the expression of a variety of voltage-gated ion channels. However, despite experimental and theoretical efforts undertaken in the last decades, the role of this excitability for some kind of dendritic computation has remained elusive. Here we show that, owing to very general properties of excitable media, the average output of a model of active dendritic trees is a highly non-linear function of their afferent rate, attaining extremely large dynamic ranges (above 50 dB). Moreover, the model yields double-sigmoid response functions as experimentally observed in retinal ganglion cells. We claim that enhancement of dynamic range is the primary functional role of active dendritic conductances. We predict that neurons with larger dendritic trees should have larger dynamic range and that blocking of active conductances should lead to a decrease of dynamic range.

20 pages, 6 figures

References in corpus (5)

• Optimal Dynamical Range of Excitable Networks at Criticality
• Dynamic range of hypercubic stochastic excitable media
• Response of electrically coupled spiking neurons: a cellular automaton approach
• Excitable Scale Free Networks
• Deterministic excitable media under Poisson drive: power law responses, spiral waves and dynamic range

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