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Seele’s neural consensus algorithm was inspired by the inner workings of the human brain. It asynchronously processes and sorts data in a large-scale decentralized environment, using ε-differential agreements to allow all nodes within a network to reach consensus. Compared to existing consensus algorithms, it ensures greater fault-tolerance and is significantly more efficient. Its performance also scales linearly as network size increases, allowing it to be widely adaptable to many different use cases.