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S I Amari

Publications and source records attributed to S I Amari.

4 recordsLinked to original sources

Attention modulation of neural tuning through peak and base rate in correlated firing.

The present study investigates the influence of attention modulation on neural tuning functions under a Gaussian correlation structure. Recent experiments have shown that attention modulates the tuning curve via its height and base rate. Inspired by this experimental finding, we previously showed the effective size of attention modulation (i.e. the critical length) on the neural population that enhances encoding accuracy. The previous result, however, was obtained under the assumption of uncorrelated firing, i.e. stimulus-conditional independence of neural responses. A question still remains whether the above findings can be applied to correlated firing. It is important to investigate this issue partly because neural firings are usually correlated but even more so because common attentional inputs may cause correlated firings. The present study first provides the general framework of attention modulation in relation to an attended stimulus and an actual stimulus and then shows the existence of a critical length under a Gaussian correlation structure. In order to improve encoding accuracy, measured by the Fisher information, the height and the base rate should be increased when the attended stimulus is in the critical length from the peak of the tuning curve and decreased otherwise. Furthermore, we confirm that a similar nature of the critical length also holds even when the neural decoder uses an uncorrelated unfaithful model. Thus, the existence of the critical length seems to be a ubiquitous phenomenon in attention modulation, and so its implications are discussed.

Attention↗

Adaptive natural gradient learning algorithms for various stochastic models.

The natural gradient method has an ideal dynamic behavior which resolves the slow learning speed of the standard gradient descent method caused by plateaus. However, it is required to calculate the Fisher information matrix and its inverse, which makes the implementation of the natural gradient almost impossible. To solve this problem, a preliminary study has been proposed concerning an adaptive method of calculating an estimate of the inverse of the Fisher information matrix, which is called the adaptive natural gradient learning method. In this paper, we show that the adaptive natural gradient method can be extended to be applicable to a wide class of stochastic models: regression with an arbitrary noise model and classification with an arbitrary number of classes. We give explicit forms of the adaptive natural gradient for these models. We confirm the practical advantage of the proposed algorithms through computational experiments on benchmark problems.

Algorithms↗