Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
On Intelligence
How the brain might work: a hierarchical and temporal model for learning and recognition
How the brain might work: a hierarchical and temporal model for learning and recognition
Sensing with artificial tactile sensors: an investigation of spatio-temporal inference
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
| Hi-index | 0.00 |
By using neuroanatomy and neurophysiology as a set of constraints, we believe that we have started to uncover how the brain uses hierarchy and time to create a model of the world, and to recognize novel patterns as part of that model. Hierarchically organized memory is fundamentally different than the linear memory used in current computers, and therefore offers the potential for new computer architectures. Today, we are exploring and advancing this technology by using traditional computer architectures (benefited by multiple CPU cores) to emulate the hierarchical structure of the neocortex. Exploiting the hierarchical temporal structure of the neocortex to build intelligent machines could open up many opportunities to rethink how integrated circuits and systems can play a leading role.