To understand NeuroChain and its technical characteristics, two central concepts have to be explained and detailed: the distributed bots and the communication layer.
The bots:
The bots represent the nodes in the distributed system. They are constituted by two levels of abstraction (similar to cerebellum and brains): the first level is represented by the basic communication between the different bots and elaborated algorithms (with analysis)constitute the second level, but not only. Indeed, different algorithms can be used for different tasks in the context of intelligent applications, social interactions or smart business
applications. In this case, each bot acts as an intelligent agent in the network depending on its role and its interaction with its peers. The algorithms are issued from the machine learning and artificial intelligence. The pertinent algorithm for analysis and interpretation is clearly specified in the transaction via the interpreter.
The communication layer:
Another innovation within NeuroChain concerns the adaptive communication system of the bots (based on recommendation engine). In fact, flexible and scalable/evolving communication is available depending on the task and resources required in terms of time and resources for the job. Three communication channels are available as standard (this could evolve as needed), based on the TCP / IP model SMTP, HTTPS, IPFS.
The architecture of the bot and the adaptive communication layer presented above represent the complementarity scheme to achieve three main characteristics of the NeuroChain network: security, flexibility/scalability and traceability. In terms of communication, the particularity of NeuroChain, is
its adaptability, according to the performances and the security required, to carry out a particular task.
As mentioned above, bots are performing with different algorithms that allow a certain level of autonomy, in order to execute elaborated operations such as intelligent applications or value creation
(like crypto-value, transparency or certification) in the network. The smart interaction between the bots characterizes the social property of NeuroChain.
Active research is actually initiated in NeuroChain Lab to realize an artificial neuronal network with the bots. The idea is to have an adaptive connections between the Bots according to the required analysis.
It’s a kind of distributed deep learning.
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