Santa Fe, New Mexico (PRWEB) February 12, 2014 -- M. Alexander Nugent Consulting of Santa Fe, New Mexico, a private R&D company announces the publication of Alex Nugent and Timothy Molter's PLOS ONE paper "AHaH Computing - FromMetastable Switches to Attractors to Machine Learning". The paper describes a new form of computing based on the attractor dynamics of dissipative systems and details a path from memristor-based circuits to foundational machine learning functions.
A new form of computing based on the attractor dynamics of dissipative systems has been shown to lead to solutions in machine learning and universal logic. In the newly published PLOS ONE paper “AHaH Computing—From Metastable Switches to Attractors to Machine Learning”, authors Alex Nugent and Timothy Molter detail a path from memristor-based circuits to foundational machine learning functions such as pattern classification, prediction, clustering, combinatorial optimization and robotic arm actuation. The main aim of the research is to better understand how nature utilizes the laws of thermodynamics and self-organization to compute. This knowledge is being directed toward the creation of a new type of adaptive neural processing unit (NPU) called “Thermodynamic RAM”.
Nugent and Molter demonstrate that the AHaH Node is a computationally universal building block that can serve as the foundation for a new adaptive computing substrate that meshes memory and processing. This has some big implications in terms of power and space efficiency by addressing the von Neumann Bottleneck