scholarly journals Enhancing the darkside: Asymmetric gain of cone photoreceptors underpins discrimination of visual scenes based on their skewness

2021 ◽  
Author(s):  
Matthew Yedutenko ◽  
Marcus H.C. Howlett ◽  
Maarten Kamermans

Psychophysical data indicates humans can discriminate visual scenes based on their skewness - the ratio of dark and bright patches within a visual scene. It was also shown that on a phenomenological level this skew discrimination is described by the so-called Blackshot mechanism, which accentuates strong negative contrasts within a scene. Here we demonstrate that the neuronal correlate of the Blackshot mechanism is the asymmetric gain of the cone phototransduction cascade, which is higher for strong negative contrasts than for strong positive contrasts. We recorded from goldfish cone photoreceptors and found that the asymmetry in the phototransduction gain leads to higher amplitude of the responses to negatively than to positively skewed light stimuli. This asymmetry in the amplitude was present in the photocurrent, voltage response and cone synaptic output. Additionally, we found that stimulus skewness leads to a subtle change in photoreceptor kinetics. For negatively skewed stimuli, the cone's impulse response functions peak later than for positively skewed stimulus. However, stimulus skewness does not affect the cone's overall integration time.

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.


Author(s):  
Jan Prüser ◽  
Christoph Hanck

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.


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