An Optimized Structure-Function Design Principle Underlies Efficient Signaling Dynamics in Neurons

Abstract

Dynamic signaling on branching axons is critical for rapid and efficient communication between neurons in the brain. Efficient signaling in axon arbors depends on a trade-off between the time it takes action potentials to reach synaptic terminals (temporal cost) and the amount of cellular material associated with the wiring path length of the neuron’s morphology (material cost). However, where the balance between structural and dynamical considerations for achieving signaling efficiency is, and the design principle that neurons optimize to preserve this balance, is still elusive. In this work, we introduce a novel analysis that compares morphology and signaling dynamics in axonal networks to address this open problem. We show that in Basket cell neurons the design principle being optimized is the ratio between the refractory period of the membrane, and action potential latencies between the initial segment and the synaptic terminals. Our results suggest that the convoluted paths taken by axons reflect a design compensation by the neuron to slow down signaling latencies in order to optimize this ratio. Deviations in this ratio may result in a breakdown of signaling efficiency in the cell. These results pave the way to new approaches for investigating more complex neurophysiological phenomena that involve considerations of neuronal structure-function relationships.

Super-Selective Reconstruction of Causal and Direct Connectivity With Application to in vitro iPSC Neuronal Networks

“…While physiologically many variables contribute to neuronal delays, a rough indication of the delay in synaptically connected cortical neurons was estimated to be 6-14 ms (Gonzalez-Burgos, 2000). We estimated similar conduction velocities and latency values in a previous study performed on basket and pyramidal neurons from the rat neocortex (Puppo et al, 2018). Given that monolayer networks of hiPSC-derived neurons may have temporal properties that differ from those observed in intact brain networks, we decided to avoid neglecting correlations that could negatively bias the inference performance by considering a larger temporal window (−22, 22) ms.…”

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

“…While physiologically many variables contribute to neuronal delays, a rough indication of the delay in synaptically connected cortical neurons was estimated to be 6 -14 ms [20]. We estimated similar conduction velocities and latency values in a previous study performed on basket and pyramidal neurons from the rat neocortex [50]. Given that monolayer networks of human iPSC-derived neurons may have temporal properties that differ from those observed in intact brain networks, we decided to avoid neglecting correlations that could negatively bias the inference performance We used a graph-based connectivity analysis to estimate developing connections between neurons.…”

“…Importantly, spatio-temporal information is implicitly contained in the estimated connectivity and delay map too; we expect therefore that, when used in combination with novel computational methodologies [66,58,67], our method will help reveal more fundamental network properties crucial to the understanding of the relationships between network topology, dynamic signaling and network functions in healthy and disease models.…”

The Effect of Signaling Latencies and Node Refractory States on the Dynamics of Networks

“…Using high resolution morphological reconstructions of axon shapes, we recently showed that the refraction ratio reflects a design principle that biological neurons optimize. It reflects a balance between the wiring lengths of axons (material cellular costs) versus signaling speeds (temporal costs) [12].…”