In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method. Abstract In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed -integration as a principled way of blending multiple positive measures (e.g., stochastic models in the form of probability distributions), enabling an optimal integration in the sense of minimizing the -divergence. It also encompasses existing integration methods as its special case, for example, a weighted average and an exponential mixture. The parameter determines integration characteristics, and the weight vector assigns the degree of importance to each measure. In most work, however, and are given in advance rather than learned. In this letter, we present a parameter learning algorithm for learning and from data when multiple integrated target values are available. Numerical experiments on synthetic as well as real-world data demonstrate the effectiveness of the proposed method.

A stationary display of white discs positioned on intersecting gray bars on a dark background gives rise to a striking scintillating effect—the scintillating grid illusion. The spatial and temporal properties of the illusion are well known, but a neuronal-level explanation of the mechanism has not been fully investigated. Motivated by the neurophysiology of the Limulus retina, we propose disinhibition and self-inhibition as possible neural mechanisms that may give rise to the illusion. In this letter, a spatiotemporal model of the early visual pathway is derived that explicitly accounts for these two mechanisms. The model successfully predicted the change of strength in the illusion under various stimulus conditions, indicating that low-level mechanisms may well explain the scintillating effect in the illusion.