Application
- Machine learning is the most important technological development of the century, with countless applications.
- Modern machine learning algorithms utilize neural network architectures (deep learning).
- Today’s neural networks do not operate like biological brains, and do not provide sufficient performance.
Our Innovation
- A new innovative neural network architecture based on a new scientific theory of brain function, and simulates how real biological brains work.
- Each basic computational unit has subunits corresponding to cortical layers, allowing simultaneous flow in all directions, including bottom-up and top-down (`predictions’, feedback).
- Has potential to show human-like intelligence
Technology
- The network finds a pairing between system inputs (digital sensory inputs – e.g. pictures, sounds, data of any kind) and system outputs (e.g., object identification, robot motor commands, etc.).
- The architecture organizes neurons into units called nodes and organizes inter-neuron connections into different types of networks.
- Its simulation utilizes a process (the R process) that has several different stages (R modes) and combines nodes, networks, R modes, agents and particles in novel ways.
- It allows neurons to directly modulate sensory inputs and presents new learning (training) algorithms. It uses auxiliary and specific structures to assist the simulation.
Opportunity
- The RPNN has the potential to be utilized in a large number of applications world-wide.
- Specific areas include computer vision, natural language understanding, motor control, autonomous vehicles and robots of all kinds.
PATENT STATUS
Published US-2020-0410346-A1
