Today we would like to inform our investors about the latest technological advances and our technological breakthrough in our research approach.
In the past few months we have examined over 752 scientific articles from the fields of neuromorphic computing, neuromorphic engineering, digital neuroscience, blockchain, robotics, cognitive neuroscience, neuromorphic behaving systems, machine learning, deep learning, and artificial neural networks, future emerging devices, smart systems and materials science, brain science, Neuromorphic Behaving Systems, artificial intelligence and neural medicine. By studying the research results in these research fields, we were able to build a solid basis for our research. In addition, our experts have also studied the areas of neuromorphic, approximate, parallel, inmemory, and quantum computing and have also included them in our research. Particular attention was paid to the research results of the scientific papers that deal with the topics of synapse-based neuromorphic computing, event-based neuromorphic computing, memristor-based neuromorphic computing and spintronics-based neuromorphic computing. Taking into account all work, we have aligned and continued our research in the planned direction of blockchain-based neuromorphic computing technology.
Now we can announce that we have achieved our first major research successes.
Blockchain as the basis for Synapses
For our very special functional area of blockchain-based neuromorphic chips, we initially carried out the production of artificial synapses with external support. A chip was used which contains synapses made of silicon germanium. The use was only made possible by the research successes of other researchers in 2018. With the help of these chips, we were able to precisely control the electronic charges flowing through the synapses. To do this, we documented these charges using a blockchain-based networking technology (initially using simple synapse units; in the future this will be carried out using quantum electronics). This made it possible for us to store the electronic charges of the synapses in the blockhain-like structure and thus to analyze the movements and paths. Using a simulation based on this, we were able to visualize the innumerable synapse movements and electrical flows and thus represent them graphically. Despite the still existing error rate in the context of the experiments carried out, this is immense progress and strengthens our vision of being able to offer very high computing power through this technology in the future. We will soon publish further information and details on our blog or as a scientific article.