So far, we have shown that ELL pyramidal neurons can efficiently process natural stimuli through temporal decorrelation because of fractional differentiation, which ensures that the neural tuning increases as a power law with exponent α neuron that is precisely related to the power law exponent of the stimulus α stim.

Abstract: The decorrelation performance of LAMBDA algorithm, LLL algorithm and Seysen algorithm are analyzed with evaluation indexes, i.e., condition number, orthogonal defect and S(A).Moreover, relationships between decorrelation performance of the above algorithms and ambiguity search efficiency are evaluated using theoretical and practical validation, respectively.

We tested the central prediction of the theory and found that the spike trains of retinal ganglion cells were indeed decorrelated compared with the visual input. to which a transform is optimal, we define two measures of optimality: decorrelation efficiency ( the traditional measure ) is the proportional reduction in off-diagonal energy ( sum of ak)solute values of covariances ) from the data domain to the transform domain4, and relative remaining energy is So far, we have shown that ELL pyramidal neurons can efficiently process natural stimuli through temporal decorrelation because of fractional differentiation, which ensures that the neural tuning increases as a power law with exponent α neuron that is precisely related to the power law exponent of the stimulus α stim. 2019-11-19 · Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning.

Decorrelation efficiency

A power transfer function of a receiver is given by Q = α log β P where Q is the output and P the input and α Specifically, efficient neural coding can be achieved by ensuring that the neural tuning function is inversely proportional to stimulus intensity as a function of frequency, thereby achieving a neural response that is decorrelated in the temporal domain or, equivalently, whose amplitude is independent of frequency9. Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible. @ Decorrelation shall be accomplished by linear transformation, and the same trans- formation must be used on all blocks. Traditional Efficiency vs. Correlation Relative Remaining Energy vs. Correlation Figure 4 : Decorrelation Efficiency vs.

high-contrast images, this decorrelation would enhance coding efficiency in optic nerve fibers of limited capacity. We tested the central prediction of the theory and found that the spike trains of retinal ganglion cells were indeed decorrelated compared with the visual input.

due to collisions between particles). Based on the nonlinear description with added phase decorrelation, a quasilinear version of the model has been developed, where the phase decorrelation has been replaced by a quasilinear diffusion coefficient in particle energy. This paper presents another, independent from the previous, approach to increase the efficiency of the MAFA method. It is based on the application of the integer decorrelation matrix to transform observation equations into equivalent, but better conditioned, observation equations.

2020-04-01 · As shown in Table 2, the computational efficiency of the traditional correlation algorithm is much higher than that of the RPT method. A rotation angle greater than 10° will not work with the traditional correlation algorithm, and this phenomenon is called decorrelation.

A rotation angle greater than 10° will not work with the traditional correlation algorithm, and this phenomenon is called decorrelation. Statistics - Correlation Co-efficient - A correlation coefficient is a statistical measure of the degree to which changes to the value of one variable predict change to the value of another.

A decorrelation transform can potentially bring a significant performance gain by boosting the energy diversity in signal representation. Effect of input signal decorrelation on the efficiency of adaptive spatial filtering algorithms with small teaching sample size @inproceedings{Borodacheva1995EffectOI, title={Effect of input signal decorrelation on the efficiency of adaptive spatial filtering algorithms with small teaching sample size}, author={T.
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Decorrelation efficiency is the metric used to determine transforms that give highly-uncorrelated outputs. Scalar quantizers are applied to transform outputs to extract uniformly distributed bit sequences to which secret keys are bound.

errors and surgical site infections, decreased efficiencies and throughputs, ments of 2D motion as it both gives an increased amount of decorrelation of the hedge funds is similar to the performance of their Nordic peers. fund strategies: diversification, decorrelation, and solid risk-adjusted returns.
Domain protein quaternary structure

Decorrelation efficiency: • Energy especially Markov sources with high correlation coefficient Advantage: very efficient multiplier-less implementation.

Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible.