New Neural Network Architecture Simulates the Human Brain

Rappoport Ari, HUJI, School of Computer Science and Engineering, Computer Science


Machine Learning, Neural Network, Deep Learning

Current development stage

TRL2  Technology Concept Formulated  


  • 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


  • 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.


  • 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.


Contact for more information:

Aviv Shoher
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