Neuron arbor geometry is sensitive to the limited-range fractal properties of their dendrites - Rowland, Conor; Smith, Julian H.; Moslehi, Saba; Harland, Bruce; Dalrymple-Alford, John; Taylor, Richard P. (Frontiers in Network Physiology, 2023)
Fractal geometry is a well-known model for capturing the multi-scaled complexity of many natural objects. By analyzing three-dimensional images of pyramidal neurons in the rat hippocampus CA1 region, we examine how the individual dendrites within the neuron arbor relate to the fractal properties of the arbor as a whole. We find that the dendrites reveal unexpectedly mild fractal characteristics quantified by a low fractal dimension. This is confirmed by comparing two fractal methods – a traditional ‘coastline’ method and a novel method that examines the dendrites’ tortuosity across multiple scales. This comparison also allows the dendrites’ fractal geometry to be related to more traditional measures of their complexity. In contrast, the arbor’s fractal characteristics are quantified by a much higher fractal dimension. Employing distorted neuron models that modify the dendritic patterns, deviations from natural dendrite behavior are found to induce large systematic changes in the arbor’s structure and its connectivity within a neural network. We discuss how this sensitivity to dendrite fractality impacts neuron functionality in terms of balancing neuron connectivity with its operating costs. We also consider implications for applications focusing on deviations from natural behavior, including pathological conditions and investigations of neuron interactions with artificial surfaces in human implants.