Intellectual Property Law

US Patent 7412428 - Nanotechnology based neural network for applying Hebbian learning

This patent is one in a series of patents from Alex Nugent who has come up with a variety of techniques to use nanoparticle based interconnections to simulate physical neural networks. One of the limitations of current artificial intelligence approaches is the reliance on software solutions which have an intrinsic delay required for the transfer of information between memory and a processor. On the other hand physical neural networks have the potential to integrate memory with processing. This latest patent teaches a system for applying a Hebbian learning process to a physical neutral network formed from nanoparticles, nanowires, or nanotubes.

1. A system, comprising:

a physical neural network configured utilizing nanotechnology and integrated with feedback circuitry, wherein said physical neural network comprises a plurality of nanoconductors comprising at least one of nanotubes, nanowires, or nanoparticles, suspended and free to move about in a dielectric medium and which form neural connections between pre-synaptic and post-synaptic components of said physical neural network; and

a learning mechanism for applying Hebbian learning to said physical neural network.

Full post as published by TinytechIP on August 18, 2008 (boomark / email).