Many machine learning algorithms, especially neural networks that are so popular now, borrow ideas for their effective work from the knowledge of neurophysiologists about how our brain works. It turns out that among the vast amount of information about genes, neurons, synapses and neurotransmitters, one can single out quite specific principles of how our brain learns and makes decisions. These principles include hierarchy, feedback, layer-by-layer learning, lateral suppression, etc. To combine these ideas and implement them in a new algorithm for controlling a technical device (as they say, to build a neuromorphic algorithm) is the task of this project.

One of the key technologies that we use in this direction is the hierarchical temporary memory framework - HTM. This developing area is not yet adapted to dynamical systems and does not support the idea of reinforcement learning. Expanding HTM in this direction is one of the key topics.