A neural network method, comprising: modeling an environment; implementing a policy based on the modeled environment, to perform an action by an agent within the environment, having at least one estimated dynamic parameter; receiving an observation and a temporally-associated cost or reward based on operation of the agent in the environment controlled according to the policy; and updating the policy, dependent on the received observation and the temporally-associated cost or reward, to improve the policy to optimize an expected future cumulative cost or reward. The policy may represent a set of parameters defining an artificial neural network having a plurality of hierarchical layers and having at least one layer which receives inputs representing aspects of the received observation indirectly from other neurons, and produce outputs to other neurons which indirectly implement the policy, the plurality of hierarchical layers being trained according to respectfully distinct training criteria.

Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.

A method of processing information is provided. The method involves receiving a message; processing the message with a trained artificial neural network based processor, having at least one set of outputs which represent information in a non-arbitrary organization of actions based on an architecture of the artificial neural network based processor and the training; representing as a noise vector at least one data pattern in the message which is incompletely represented in the non-arbitrary organization of actions; and analyzing the noise vector distinctly from the trained artificial neural network.

A method of processing information is provided. The method involves receiving a message; processing the message with a trained artificial neural network based processor, having at least one set of outputs which represent information in a non-arbitrary organization of actions based on an architecture of the artificial neural network based processor and the training; representing as a noise vector at least one data pattern in the message which is incompletely represented in the non-arbitrary organization of actions; analyzing the noise vector distinctly from the trained artificial neural network; searching at least one database; and generating an output in dependence on said analyzing and said searching.

Described is a system including a sensor and a processing arrangement. The sensor measures data corresponding to a patient's breathing patterns. The processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of a REM sleep state. In another embodiment, the processing arrangement analyzes the breathing patterns to determine whether the breathing patterns are indicative of one of the following states: (i) a wake state and (ii) a sleep state. In another embodiment, a neural network analyzes the data to determine whether the breathing patterns are indicative of one of the following states: (i) a REM sleep state, (ii) a wake state and (iii) a sleep state. In another embodiment, the processing arrangement analyzes the data to determine whether the breathing pattern is indicative of an arousal.