datasets

The architecture of the two-hemispheric neuromorphic network is inspired by the neuroscience of the brain in the processing of patterns in the thalamus and the two hemispheres of the brain and the role of the corpus callosum in the exchange of information between two separate processes that play a key role in the integrated output and parallel processing and exactly the main heart of data processing. With parallel processing, separate, coordinated, simultaneous and integrated output architecture that simulates the corpus callosum of the brain, and the output layer is integrated in this architecture from the adaptive to the generated voltage and synaptic plasticity designed using intelligent programs and by integrating other sensors using intelligent programs. It can be the network with optimal energy consumption and the lowest error rate, interpretation of reception, excellent performance with naturalness and high adaptability is in the first rank of neural networks of intelligence, and has potential users in industry, robotics, agriculture, medicine, financial market forecasting, user security. Real-time decision making Adaptability High generalizability High user security Adaptive learning mechanism with energy Adaptation with low data Functional partitioning Multitask learning Parallel processing in complex decision making Analysis of high-bandwidth signals Application in noisy conditions High speed in training and reduction of training time Natural language processing Self-supervising system to maintain coherence and adherence to reality

Built With

• makefile
• python