Authors: Liang Yi
This paper introduces a new neural network that works quite different from current neural network model. The RBNN model is based on the concept of conditioned reflex, which widely exists in real creatures. In RBNN, all learning procedure are executed by the neural network itself, which makes it not a complex mathematic model but simple enough to be implemented in real brain. In RBNN, information is organized in a clear way, which makes the whole network a white box rather than a black box, so we can teach the network knowledge easily and fast. This paper shows the power of conditioned reflex as a search tool which can be used as states transfer function in state machine. Using combinations of neurons as symbols which plays the role of letters in traditional state machine, a RBNN can be treated as a state machine with small number of memory unit but huge number of letters.
Comments: 17 Pages.
[v1] 2018-04-24 04:00:45
Unique-IP document downloads: 34 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.