Modelling of Human Flicker Detection at Various Light Levels

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 129-129
Author(s):  
J Rovamo ◽  
A Raninen
Keyword(s):  
Lateral Inhibition ◽  
Goodness Of Fit ◽  
Matched Filter ◽  
Detection Efficiency ◽  
Temporal Frequency ◽  
Dark Light ◽  
Temporal Filtering ◽  
Neural Noise ◽  
Temporal Noise ◽  
Low Pass

Root-mean-square flicker sensitivity was measured within 0.2 – 2500 phot td and 0.5 – 30 Hz for a small spot with an equiluminous surround using computer graphics and a 2AFC method. In temporal noise, flicker sensitivity as a function of temporal frequency had a low-pass shape at all illuminances. Without temporal noise, the flicker sensitivity functions showed a band-pass shape at high illuminances, but changed to a low-pass shape at low illuminances. Our data are well described (goodness of fit 88%) by a model comprising: (i) low-pass temporal filtering by the modulation transfer function (MTF) of the photoreceptors ( R), (ii) high-pass temporal filtering by the MTF of the neural visual pathways ( P) resulting from lateral inhibition, (iii) addition of the temporal equivalent ( Nit) of internal neural noise, and (iv) detection by a temporal matched filter, provided that we take into account the fact that receptor responses become weaker and slower with decreasing illuminance despite adaptation. In our model detection efficiency was \eta( f)=0.148 f−0.568, Nit=46.3 μs and P( f) equal to f, where f is flicker frequency. In addition, R= R0[1+( f/ fc)2]−3, where R0=(1+24.1/ I)−0.5, fc=6.33 I0.172, and I is retinal illuminance. The model indicates that for the detection of a flickering spot in cone vision (i) the strength of lateral inhibition is independent of light level and (ii) quantal noise and dark light always remain insignificant sources of noise. (Mathematical expressions may not appear as intended)

Flicker Sensitivity as a Function of Eccentricity

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 327-327 ◽  
Author(s):  
A N Raninen ◽  
J Rovamo
Keyword(s):  
Matched Filter ◽  
Temporal Frequency ◽  
Internal Noise ◽  
Modulation Transfer ◽  
Low Frequencies ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Neural Noise ◽  
Low Pass ◽  
High Pass

We determined DeLange curves with and without external temporal noise at eccentricities of 0 – 70 deg by measuring flicker sensitivity at 1 – 45 Hz for sharp-edged M-scaled spots with an equiluminous surround. Without noise, flicker sensitivity at high frequencies increased with eccentricity but remained unchanged at low frequencies. In strong noise, flicker sensitivity was independent of eccentricity. The only exception was 70 deg where sensitivity was reduced at 1 – 3 Hz with and without noise. The data at each eccentricity are well described by our flicker-sensitivity model (Rovamo et al, 1996 Vision Research36 3767 – 3774) comprising (i) low-pass filtering by the modulation transfer function (MTF) of the retina, (ii) filtering in direct proportion to temporal frequency by the high-pass MTF of the retina and subsequent neural visual pathways, (iii) addition of white internal neural noise, and (iv) detection by a temporal matched filter. When interpreted in the context of the model, our results mean that while the high-pass filter and the magnitude of internal noise remained unchanged across eccentricities, the bandwith of the low-pass filter increased with eccentricity and at 70 deg eccentricity the efficiency of the detecting mechanism in the brain was reduced at 1 – 3 Hz. The increase in the bandwidth of the low-pass filter is in agreement with the eccentricity-dependent changes in the retinal function as revealed by the electroretinogram (ERG).

scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1681
Author(s):  
Catherine Morgan ◽  
D. Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Visual Areas ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

2019 ◽  
Author(s):  
Catherine Morgan ◽  
D Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Time Course ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

AbstractPopulation receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Here, we show that pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries are very similar. As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Moreover, we tested the effect of low-pass filtering of the time series on pRF estimates. Unsurprisingly, filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.PrecisPopulation Receptive Field mapping performed with similar protocols at two different sites, a 1.5T MRI scanner in London, and a 3T scanner in Auckland, yielded comparable results. Temporal filtering of the fMRI time course increased concordance of modelled pRFs, but introduced a bias in pRF size.

scholarly journals Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1681
Author(s):  
Catherine Morgan ◽  
D. Samuel Schwarzkopf
Keyword(s):  
Receptive Field ◽  
Goodness Of Fit ◽  
Signal To Noise Ratio ◽  
3 Tesla ◽  
Frequency Noise ◽  
Temporal Filtering ◽  
Visual Areas ◽  
Low Pass ◽  
Cortical Magnification ◽  
Tesla Scanner

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

Dynamics of neurons in the cat lateral geniculate nucleus: in vivo electrophysiology and computational modeling

1995 ◽  
Vol 74 (3) ◽  
pp. 1222-1243 ◽  
Author(s):  
P. Mukherjee ◽  
E. Kaplan
Keyword(s):  
Computational Modeling ◽  
Spike Train ◽  
Lateral Geniculate Nucleus ◽  
Transfer Functions ◽  
Temporal Frequency ◽  
Relay Cell ◽  
Lateral Geniculate ◽  
Low Pass ◽  
Band Pass

1. We investigated the time domain transformation that thalamocortical relay cells of the cat lateral geniculate nucleus (LGN) perform on their retinal input, and used computational modeling to explore the biophysical properties that determine the dynamics of the LGN relay cells in vivo. 2. We recorded simultaneously the input (S potentials) and output (action potentials) of 50 cat LGN relay cells stimulated by drifting sinusoidal gratings of varying temporal frequency. The temporal modulation transfer functions (TMTFs) of the neurons were derived from these data. The burstiness of the LGN spike trains was also assessed using objective criteria. 3. We found that the form of the TMTF was quite variable among cells, ranging from low-pass to strongly band-pass. The optimal temporal frequency of band-pass neurons was between 2 and 8 Hz. In addition, the TMTF of some cells was nonstationary: their temporal tuning changed with time. 4. The temporal tuning of a cell was directly related to the degree of burstiness of its spike train. Tonically firing relay cells had low-pass TMTFs, whereas the most bursty neurons exhibited the most sharply band-pass transfer functions. This was also true for single cells that altered their temporal tuning: a shift to more band-pass tuning was associated with increased burstiness of the spike train, and vice versa. 5. We constructed a computer simulation of the LGN relay cell. The model was a simplified five-channel version of the thalamocortical neuron model of McCormick and Huguenard. It incorporated the quantitative kinetics of the Ca2+ T channel, as well as the Hodgkin-Huxley Na+ and K+ channels, as the only active membrane currents. To simulate the in vivo dynamics of the relay cell, the input to the model consisted of trains of synaptic potentials, recorded as S potentials in our physiological experiments. 6. When the resting membrane potential of the model neuron was relatively depolarized, the model's TMTF was low-pass, with no bursting evident in the simulated spike train. At hyperpolarized resting membrane potentials, however, the modeled TMTF was band-pass, with frequent burst discharges. Thus the biophysical model reproduced not only the range of dynamics seen in real LGN relay cells, but also the dependence of the overall dynamics on the burstiness of the spike train. However, neither of these phenomena could be simulated without the T channel. Thus the simulations demonstrated that the T-type Ca2+ channel was necessary and sufficient to explain the LGN dynamics observed in physiological experiments.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL

2016 ◽  
Vol III-7 ◽  
pp. 3-8 ◽  
Author(s):  
M. Matsuoka ◽  
M. Takagi ◽  
S. Akatsuka ◽  
R. Honda ◽  
A. Nonomura ◽  
...  
Keyword(s):  
Surface Properties ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Near Infrared ◽  
Temporal Frequency ◽  
Atmospheric Correction ◽  
Time Of Day ◽  
Coefficient Of Determination ◽  
Near Infrared Reflectance ◽  
Bidirectional Reflectance

Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.

Contribution of arterial Windkessel in low-frequency cerebral hemodynamics during transient changes in blood pressure

2011 ◽  
Vol 110 (4) ◽  
pp. 917-925 ◽  
Author(s):  
Gregory S. H. Chan ◽  
Philip N. Ainslie ◽  
Chris K. Willie ◽  
Chloe E. Taylor ◽  
Greg Atkinson ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Goodness Of Fit ◽  
Low Frequency ◽  
Pressure Rise ◽  
Windkessel Model ◽  
Arterial Blood ◽  
Low Pass ◽  
Acute Changes

The Windkessel properties of the vasculature are known to play a significant role in buffering arterial pulsations, but their potential importance in dampening low-frequency fluctuations in cerebral blood flow has not been clearly examined. In this study, we quantitatively assessed the contribution of arterial Windkessel (peripheral compliance and resistance) in the dynamic cerebral blood flow response to relatively large and acute changes in blood pressure. Middle cerebral artery flow velocity (MCAV; transcranial Doppler) and arterial blood pressure were recorded from 14 healthy subjects. Low-pass-filtered pressure-flow responses (<0.15 Hz) during transient hypertension (intravenous phenylephrine) and hypotension (intravenous sodium nitroprusside) were fitted to a two-element Windkessel model. The Windkessel model was found to provide a superior goodness of fit to the MCAV responses during both hypertension and hypotension ( R2 = 0.89 ± 0.03 and 0.85 ± 0.05, respectively), with a significant improvement in adjusted coefficients of determination ( P < 0.005) compared with the single-resistance model ( R2 = 0.62 ± 0.06 and 0.61 ± 0.08, respectively). No differences were found between the two interventions in the Windkessel capacitive and resistive gains, suggesting similar vascular properties during pressure rise and fall episodes. The results highlight that low-frequency cerebral hemodynamic responses to transient hypertension and hypotension may include a significant contribution from the mechanical properties of vasculature and, thus, cannot solely be attributed to the active control of vascular tone by cerebral autoregulation. The arterial Windkessel should be regarded as an important element of dynamic cerebral blood flow modulation during large and acute blood pressure perturbation.

Temporal and Spatial Frequency Tuning of the Flicker Motion Aftereffect

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 12-12
Author(s):  
P J Bex ◽  
F A J Verstraten ◽  
I Mareschal
Keyword(s):  
Spatial Frequency ◽  
Frequency Tuning ◽  
Temporal Frequency ◽  
Motion Aftereffect ◽  
Sine Wave ◽  
Test Grating ◽  
Spatial Frequency Tuning ◽  
Spatial Frequencies ◽  
Low Pass ◽  
Sine Wave Gratings

The motion aftereffect (MAE) was used to study the temporal-frequency and spatial-frequency selectivity of the visual system at suprathreshold contrasts. Observers adapted to drifting sine-wave gratings of a range of spatial and temporal frequencies. The magnitude of the MAE induced by the adaptation was measured with counterphasing test gratings of a variety of spatial and temporal frequencies. Independently of the spatial or temporal frequency of the adapting grating, the largest MAE was found with slowly counterphasing test gratings (∼0.125 – 0.25 Hz). For slowly counterphasing test gratings (<∼2 Hz), the largest MAEs were found when the test grating was of similar spatial frequency to that of the adapting grating, even at very low spatial frequencies (0.125 cycle deg−1). However, such narrow spatial frequency tuning was lost when the temporal frequency of the test grating was increased. The data suggest that MAEs are dominated by a single, low-pass temporal-frequency mechanism and by a series of band-pass spatial-frequency mechanisms at low temporal frequencies. At higher test temporal frequencies, the loss of spatial-frequency tuning implicates separate mechanisms with broader spatial frequency tuning.

scholarly journals A matched filter approach for blind joint detection of galaxy clusters in X-ray and SZ surveys

2018 ◽  
Vol 614 ◽  
pp. A82 ◽  
Author(s):  
P. Tarrío ◽  
J.-B. Melin ◽  
M. Arnaud
Keyword(s):  
Galaxy Clusters ◽  
Matched Filter ◽  
Detection Efficiency ◽  
Blind Detection ◽  
Joint Detection ◽  
Ionized Gas ◽  
X Ray ◽  
Sky Survey ◽  
Filter Approach ◽  
Using Data

The combination of X-ray and Sunyaev–Zeldovich (SZ) observations can potentially improve the cluster detection efficiency, when compared to using only one of these probes, since both probe the same medium, the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray–SZ extraction method and allows the blind detection of clusters, that is finding new clusters without knowing their position, size, or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency, and better position accuracy than its predecessor Planck MMF, which is based on SZ maps alone. For a purity of 85%, the X-ray–SZ method detects 141 confirmed clusters in the SPT region; to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 ×1014 M⊙ and redshifts between 0.01 and 1.2.

scholarly journals Synthesis of matched filters.

2021 ◽  
Author(s):  
A.N. Degtyaryov ◽  
◽  
I.L. Afonin ◽  
A.L. Polyakov ◽  
A.S. Kozhemyakin ◽  
...  
Keyword(s):  
Fourier Series ◽  
Impulse Response ◽  
Correlation Functions ◽  
Matched Filter ◽  
Matched Filters ◽  
Low Pass ◽  
Pulse Characteristics ◽  
Low Pass Filters ◽  
Direct Use ◽  
Complex Transmission

Methods for approximating the impulse response of a matched, physically realizable filter with the minimum required number of functional nodes are in thei focus of the paper. Methods for approximating the pulse characteristics of a matched filter are proposed, namely: approximation by causal physically realizable functions, which are the correlation functions of the pulse characteristics of low-pass filters (LPF) Butterworth; using the Fourier series to describe the complex transmission coefficient of the filter; direct use of the Fourier series to approximate the impulse response of a matched filter. As a result, the number of elements of the matched filter is significantly reduced.

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