Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

MOTIVATION: Synaptic connections underlie learning and memory in the brain and are dynamically formed and eliminated during development and in response to stimuli. Quantifying changes in overall density and strength of synapses is an important pre-requisite for studying connectivity and plasticity in these cases or in diseased conditions. Unfortunately, most techniques to detect such changes are either low-throughput (e.g. electrophysiology), prone to error and difficult to automate (e.g. standard electron microscopy) or too coarse (e.g. magnetic resonance imaging) to provide accurate and large-scale measurements. RESULTS: To facilitate high-throughput analyses, we used a 50-year-old experimental technique to selectively stain for synapses in electron microscopy images, and we developed a machine-learning framework to automatically detect synapses in these images. To validate our method, we experimentally imaged brain tissue of the somatosensory cortex in six mice. We detected thousands of synapses in these images and demonstrate the accuracy of our approach using cross-validation with manually labeled data and by comparing against existing algorithms and against tools that process standard electron microscopy images. We also used a semi-supervised algorithm that leverages unlabeled data to overcome sample heterogeneity and improve performance. Our algorithms are highly efficient and scalable and are freely available for others to use. AVAILABILITY: Code is available at http://www.cs.cmu.edu/∼saketn/detect_synapses/