Abstract: Disclosed herein are embodiments of methods for encoding, decoding, and matching patterns in collections of signals. These methods use weighting functions to scale the signals. This scaling enables the use of signals of arbitrary duration, wherein the signals may include discrete sequences and spike trains. In the most general case, the signals can be represented using functionals, which extends the expressive power of the methods. Further disclosed herein are embodiments of a system that performs these methods.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.

Abstract: Embodiments of the present disclosure describe an efficient O(n log n) method that implements the Inverse Chirp Z-Transform (ICZT). This transform is the inverse of the well-known forward Chirp Z-Transform (CZT), which generalizes the fast Fourier transform (FFT) by allowing the sampling points to fall on a logarithmic spiral contour instead of the unit circle. Thus, the ICZT can be viewed as a generalization of the inverse fast Fourier transform (IFFT).

Abstract: Embodiments of the present disclosure describe an efficient O(n log n) method that implements the Inverse Chirp Z-Transform (ICZT). This transform is the inverse of the well-known forward Chirp Z-Transform (CZT), which generalizes the fast Fourier transform (FFT) by allowing the sampling points to fall on a logarithmic spiral contour instead of the unit circle. Thus, the ICZT can be viewed as a generalization of the inverse fast Fourier transform (IFFT).

Abstract: Disclosed herein are embodiments of methods for encoding, decoding, and matching patterns in collections of signals. These methods use weighting functions to scale the signals. This scaling enables the use of signals of arbitrary duration, wherein the signals may include discrete sequences and spike trains. In the most general case, the signals can be represented using functionals, which extends the expressive power of the methods. Further disclosed herein are embodiments of a system that performs these methods.

Abstract: Embodiments of the present disclosure describe an efficient O(n log n) method that implements the Inverse Chirp Z-Transform (ICZT). This transform is the inverse of the well-known forward Chirp Z-Transform (CZT), which generalizes the fast Fourier transform (FFT) by allowing the sampling points to fall on a logarithmic spiral contour instead of the unit circle. Thus, the ICZT can be viewed as a generalization of the inverse fast Fourier transform (IFFT).

Abstract: Disclosed herein are embodiments of methods for encoding, decoding, and matching patterns in collections of signals. These methods use weighting functions to scale the signals. This scaling enables the use of signals of arbitrary duration, wherein the signals may include discrete sequences and spike trains. In the most general case, the signals can be represented using functionals, which extends the expressive power of the methods. Further disclosed herein are embodiments of a system that performs these methods.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.

Abstract: A biologically-inspired model for sequence representation, method of construction and application of such models, and systems incorporating same are provided. The model captures the statistical nature of sequences and uses that for sequence encoding, recognition, and recall. The model can be trained in real time, has few tunable parameters, and is highly parallelizable, which ensures that it can scale up to very large problems. Applications of the model to word and speech recognition, machine leaning, robotics, computational bioinformatics, genetics datasets, and other sequence processing pipelines are provided.