A method for determining photoreceptor signal-to-noise ratio in the time and frequency domains with a pseudorandom stimulus

1994 ◽  
Vol 11 (6) ◽  
pp. 1221-1225 ◽  
Author(s):  
Eero Kouvalainen ◽  
Matti Weckström ◽  
Mikko Juusola
Keyword(s):  
Frequency Response ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Repeated Sequences ◽  
Signal To Noise ◽  
Frequency Domains ◽  
Noise Ratio ◽  
Time And Frequency Domains

AbstractWe have developed a method that utilizes repeated sequences of pseudorandomly modulated stimuli for calculation of the SNR either in the time or frequency domains. The method has the advantage that the distribution of SNR over relevant frequencies is readily observed. In addition, a SNR value, calculated as the ratio of the corresponding variances, is an estimate of the true SNR because it has been weighted by the cell's frequency response. The procedure offers significant advantages when studying signal transmission in nonspiking cells like photoreceptors.

NOISE-ENHANCED PROPAGATION IN A DISSIPATIVE CHAIN OF TRIGGERS

2002 ◽  
Vol 12 (03) ◽  
pp. 629-633 ◽  
Author(s):  
S. MORFU ◽  
J. C. COMTE ◽  
J. M. BILBAULT ◽  
P. MARQUIÉ
Keyword(s):  
Numerical Simulation ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Active Role ◽  
Signal To Noise ◽  
Optimum Amount ◽  
Propagation Length ◽  
Noise Ratio ◽  
Spatiotemporal Noise

We study the influence of spatiotemporal noise on the propagation of square waves in an electrical dissipative chain of triggers. By numerical simulation, we show that noise plays an active role in improving signal transmission. Using the Signal to Noise Ratio at each cell, we estimate the propagation length. It appears that there is an optimum amount of noise that maximizes this length. This specific case of stochastic resonance shows that noise enhances propagation.

THE ELUSIVE FIRST ARRIVAL

Geophysics ◽  
1964 ◽  
Vol 29 (5) ◽  
pp. 806-813 ◽  
Author(s):  
J. G. Hagedoorn
Keyword(s):  
Frequency Response ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Recording System ◽  
Transmission Curve ◽  
Frequency Noise ◽  
Signal To Noise ◽  
First Arrival ◽  
Noise Ratio

In first‐arrival refraction work, the initial deflection of the first loop of the signal arriving from the shotpoint must be readily recognised against a background of seismic disturbances due to sources other than the explosion. The accuracy of timing a first arrival is determined by this signal‐to‐noise ratio. It depends primarily on the location of shot and receivers, the size of the charge, and the existing ground unrest at the time of registration. Experiments carried out with these variables kept constant, by recording at the same location from the same shot, show how much the signal‐to‐noise ratio also depends on the characteristics of the recording equipment used. The best signal‐to‐noise ratio is certainly not obtained when the transmission curve of the entire system, comprising geophone, amplifier, and galvanometer, peaks at the apparent dominant frequency of the refraction signal. Practical examples show that the signal‐to‐noise ratio can be improved considerably by using recording systems that transmit a band of frequencies extending many octaves below the observed dominant frequency. The inception of an oscillatory signal was found to be particularly sensitive to the characteristics of a recording system. A seismometer, for example, will transform a starting sine wave with a frequency equal to the natural frequency of the seismometer into a signal with a first loop that is about half as high and half as long as the succeeding loops, the latter moreover being advanced by one‐quarter period. This relative constriction of the initial part of a signal is called the “cramping” effect. Such an effect will weaken a refraction first arrival relative to simultaneously arriving later parts of noise signals. This explains why a cramping effect will impair the signal‐to‐noise ratio. A cramping effect can, of course, be avoided by using a recording system with a flat frequency response. The opposite effect, which can be expected to improve the signal‐to‐noise ratio, could obviously be achieved by using systems with relatively increased low‐frequency response. The practical limit to this improvement would be set by the low‐frequency noise that is enhanced by this procedure.

scholarly journals Signal transmission through the dark-adapted retina of the toad (Bufo marinus). Gain, convergence, and signal/noise.

1990 ◽  
Vol 95 (4) ◽  
pp. 717-732 ◽  
Author(s):  
D R Copenhagen ◽  
S Hemilä ◽  
T Reuter
Keyword(s):  
Ganglion Cells ◽  
Signal To Noise Ratio ◽  
Signal Transmission ◽  
Absolute Threshold ◽  
Spatial Summation ◽  
Bipolar Cells ◽  
Horizontal Cells ◽  
Signal To Noise ◽  
Order Of Magnitude ◽  
Noise Ratio

Responses to light were recorded from rods, horizontal cells, and ganglion cells in dark-adapted toad eyecups. Sensitivity was defined as response amplitude per isomerization per rod for dim flashes covering the excitatory receptive field centers. Both sensitivity and spatial summation were found to increase by one order of magnitude between rods and horizontal cells, and by two orders of magnitude between rods and ganglion cells. Recordings from two hyperpolarizing bipolar cells showed a 20 times response increase between rods and bipolars. At absolute threshold for ganglion cells (Copenhagen, D.R., K. Donner, and T. Reuter. 1987. J. Physiol. 393:667-680) the dim flashes produce 10-50-microV responses in the rods. The cumulative gain exhibited at each subsequent synaptic transfer from the rods to the ganglion cells serves to boost these small amplitude signals to the level required for initiation of action potentials in the ganglion cells. The convergence of rod signals through increasing spatial summation serves to decrease the variation of responses to dim flashes, thereby increasing the signal-to-noise ratio. Thus, at absolute threshold for ganglion cells, the convergence typically increases the maximal signal-to-noise ratio from 0.6 in rods to 4.6 in ganglion cells.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

Information capacity and bandwidth limitations in the SEM

1989 ◽  
Vol 47 ◽  
pp. 84-85
Author(s):  
D. C. Joy ◽  
R. D. Bunn
Keyword(s):  
Signal To Noise Ratio ◽  
Critical Dimension ◽  
Information Capacity ◽  
Acquisition Time ◽  
Signal To Noise ◽  
Sem Image ◽  
Probe Size ◽  
Minimum Number ◽  
Noise Ratio ◽  
Signal Channel

The information available from an SEM image is limited both by the inherent signal to noise ratio that characterizes the image and as a result of the transformations that it may undergo as it is passed through the amplifying circuits of the instrument. In applications such as Critical Dimension Metrology it is necessary to be able to quantify these limitations in order to be able to assess the likely precision of any measurement made with the microscope.The information capacity of an SEM signal, defined as the minimum number of bits needed to encode the output signal, depends on the signal to noise ratio of the image - which in turn depends on the probe size and source brightness and acquisition time per pixel - and on the efficiency of the specimen in producing the signal that is being observed. A detailed analysis of the secondary electron case shows that the information capacity C (bits/pixel) of the SEM signal channel could be written as :

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