Moreover, considering dendrites as spatially extended excitable media, enhancement of dynamic range is suggested to be the main functional role of active dendritic conductances 16. Dendrites have been shown to exhibit excitability through the expression of a variety of voltage-gated ion channels, and are suggested to associate with computational functions such as the learning capacity 15. Dendritic branches may support clustered inputs, connection specificity, and dendritic computation 14. The complex morphological structure of dendritic trees is likely associated with the function 12, 13. Moreover, besides intrinsic mechanisms, branching morphogenesis may also be controlled externally 9, 10, 11. It has been suggested that dendrites grow to fill a target space in an optimal manner, using the least amount of wiring to reach all synaptic contacts. Optimization models have also been developed to understand the intrinsic mechanisms underling branching 6, 7, 8. Computational models have been developed and proposed to describe complex dendritical structures 1, 2, 3, 4, 5. These suggest that actual brain-wide dendritic morphology is near optimal in terms of both dynamic range and information transmission.ĭendritic trees of neurons receive, process and transfer information from other neurons to the soma. With brain-wide neuron digital reconstructions of the pyramidal cells, 90% of neurons have no more than 10 dendrites. beyond 10 somatic branches) has limited ability to improve the transmission efficiency. This indicates that further increasing the number of somatic branches (e.g. Moreover, our simulated data suggest that there is an exponential association (decay resp.) of overall relative energy consumption (dynamic range resp.) in relation to the number of somatic branches. We show that for dendritic tress of the same number of nodes, the dynamic range increases with the number of somatic branches and decreases with the asymmetry of dendrites, and the information transmission is more efficient for dendrites with more somatic branches. It has been known that larger dendritic trees have a higher dynamic range. We model dendrites in a neuron as multiple excitable binary trees connected to the soma where each node in a tree can be excited by external stimulus or by receiving signals transmitted from adjacent excited nodes. We investigate the dynamic range and information transmission efficiency of dendrites in relation to dendritic morphology. The range of signal intensities that can be robustly distinguished by dendrites is quantified by the dynamic range. Dendrites receive and process signals from other neurons.
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