Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A system is provided for performing a predetermined function within a total area of operation, wherein the system includes a plurality of autonomous agents. Each autonomous agent is able to detect respective local parameters. Each autonomous agent uses a Kalman filter component to establish an environment state based a plurality of state measurements over time. The output of the Kalman filter component within a respective agent is applied to reinforcement learning by an actor-critic task controller, within the respective agent, to determine a subsequent action to be performed by the respective agent in accordance with a reward function. Each agent includes a Kalman consensus filter that addresses errors of the plurality of state measurements over time.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

Abstract: An asynchronous convolutional neural network (CNN) can interpret a sequence of input data. An input value representing a sample of the sequence of input data is received by a computational unit (CU) in a layer of the asynchronous CNN. The CU calculates a dot product of the input value and a weight assigned to the CU to produce an activation value. A change detector (CD) associated with the CU detects a difference between the activation value and previous activation values. The CD determines whether the detected difference is significant, indicating that the sample of the sequence of input data includes a significant change. If the detected difference is significant, the activation value is supplied to at least one subsequent CU included in a subsequent layer of the asynchronous CNN.

Abstract: A system is provided for performing a predetermined function within a total area of operation, wherein the system includes a plurality of autonomous agents. Each autonomous agent is able to detect respective local parameters. Each autonomous agent uses a Kalman filter component to establish an environment state based a plurality of state measurements over time. The output of the Kalman filter component within a respective agent is applied to reinforcement learning by an actor-critic task controller, within the respective agent, to determine a subsequent action to be performed by the respective agent in accordance with a reward function. Each agent includes a Kalman consensus filter that addresses errors of the plurality of state measurements over time.

Abstract: An asynchronous convolutional neural network (CNN) can interpret a sequence of input data. An input value representing a sample of the sequence of input data is received by a computational unit (CU) in a layer of the asynchronous CNN. The CU calculates a dot product of the input value and a weight assigned to the CU to produce an activation value. A change detector (CD) associated with the CU detects a difference between the activation value and previous activation values. The CD determines whether the detected difference is significant, indicating that the sample of the sequence of input data includes a significant change. If the detected difference is significant, the activation value is supplied to at least one subsequent CU included in a subsequent layer of the asynchronous CNN.

Abstract: Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the features associated with the noise. The neural network predicts denoised time-varying output signals that correspond to the time-varying input signals based on the recognized features of the noisy time-varying input signals that at least partially match at least some of the features associated with the noise.

Abstract: Class types of input signals having unknown class types are automatically classified using a neural network. The neural network learns features associated with a plurality of different observed signals having respective different known class types. The neural network then recognizes features of the input signals having unknown class types that at least partially match at least some of the features associated with the plurality of different observed signals having respective different known class types. The neural network determines probabilities that each of the input signals has each of the known class types based on strengths of the matches between the recognized features of the input signals and the features associated with plurality of different observed signals. The neural network classifies each of the input signals as having one of the respective different known class types based on a highest determined probability.