Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Connectionist learning procedures
Artificial Intelligence
Learning invariance from transformation sequences
Neural Computation
How to make computers that work like the brain
Proceedings of the 46th Annual Design Automation Conference
Seeing, Second Edition: The Computational Approach to Biological Vision
Seeing, Second Edition: The Computational Approach to Biological Vision
Optimizing hierarchical temporal memory for multivariable time series
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Robust character recognition using a hierarchical bayesian network
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Biomimetic tactile target acquisition, tracking and capture
Robotics and Autonomous Systems
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The ease and efficiency with which biological systems deal with several real world problems, that have been persistently challenging to implement in artificial systems, is a key motivation in biomimetic robotics. In interacting with its environment, the first challenge any agent faces is to extract meaningful patterns in the inputs from its sensors. This problem of pattern recognition has been characterized as an inference problem in cortical computation. The work presented here implements the hierarchical temporal memory (HTM) model of cortical computation using inputs from an array of artificial tactile sensors to recognize simple Braille patterns. Although the current work has been implemented using a small array of robot whiskers, the architecture can be extended to larger arrays of sensors of any arbitrary modality.