Nonlinear models of the first synapse in the light-adapted fly retina

1995 ◽  
Vol 74 (6) ◽  
pp. 2538-2547 ◽  
Author(s):  
M. Juusola ◽  
M. Weckstrom ◽  
R. O. Uusitalo ◽  
M. J. Korenberg ◽  
A. S. French
Keyword(s):  
Membrane Potential ◽  
Synaptic Input ◽  
Light Adaptation ◽  
Volterra Series ◽  
Dynamic Properties ◽  
Second Order ◽  
Synaptic Function ◽  
Input Output ◽  
Light Stimuli ◽  
Synaptic Gain

1. Randomly modulated light stimuli were used to characterize the nonlinear dynamic properties of the synapse between photoreceptors and large monopolar neurons (LMC) in the fly retina. Membrane potential fluctuations produced by constant variance contrast stimuli were recorded at eight different levels of background light intensity. 2. Representation of the photoreceptor-LMC input-output data in the form of traditional characteristic curves indicated that synaptic gain was reduced by light adaptation. However, this representation did not include the time-dependent properties of the synaptic function, which are known to be nonlinear. Therefore nonlinear systems analysis was used to characterize the synapse. 3. The responses of photoreceptors and LMCs to random light fluctuations were characterized by second-order Volterra series, with kernel estimation by the parallel cascade method. Photoreceptor responses were approximately linear, but LMC responses were clearly nonlinear. 4. Synaptic input-output relationships were measured by passing the light stimuli to LMCs through the measured photoreceptor characteristics to obtain an estimate of the synaptic input. The resulting nonlinear synaptic functions were well characterized by second-order Volterra series. They could not be modeled by a linear-nonlinear-linear cascade but were better approximated by a nonlinear-linear-nonlinear cascade. 5. These results support two possible structural models of the synapse, the first having two parallel paths for signal flow between the photoreceptor and LMC, and the second having two distinct nonlinear operations, occurring before and after chemical transmission. 6. The two models were cach used to calculate the synaptic gain to a brief change in photoreceptor membrane potential. Both models predicted that synaptic gain is reduced by light adaptation.

Synaptic limitations to contrast coding in the retina of the blowfly Calliphora

1987 ◽  
Vol 231 (1265) ◽  
pp. 437-467 ◽  
Keyword(s):  
Synaptic Transmission ◽  
Visual Processing ◽  
Light Adaptation ◽  
Characteristic Curve ◽  
Second Order ◽  
Noise Power ◽  
Early Visual Processing ◽  
Synaptic Noise ◽  
Order Neuron ◽  
Synaptic Gain

We investigate the effects of synaptic transmission on early visual processing by examining the passage of signals from photoreceptors to second order neurons (LMCS). We concentrate on the roles played by three properties of synaptic transmission: (1) the shape of the characteristic curve, relating pre- and postsynaptic signal amplitudes, (2) the dynamics of synaptic transmission and (3) the noise introduced during transmission. The characteristic curve is sigmoidal and follows a simple model of synaptic transmission (Appendix) in which transmitter release rises exponentially with presynaptic potential. According to this model a presynaptic depolarization of 1.50–1.86 mV produces an e-fold increase in postsynaptic conductance. The characteristic curve generates a sigmoidal relation between postsynaptic (LMC) response amplitude and stimulus contrast. The shape and slope of the characteristic curve is unaffected by the state of light adaptation. Retinal antagonism adjusts the characteristic curve to keep it centred on the mean level of receptor response generated by the background. Thus the photoreceptor synapses operate in the mid-region of the curve, where the slope or gain is highest and equals approximately 6. The dynamics of transmission of a signal from photoreceptor to second-order neuron approximates to the sum of two processes with exponential time courses. A momentary receptor depolarization generates a postsynaptic hyperpolarization of time constant 0.5–1.0 ms, followed by a slower and weaker depolarization. Light adaptation increases the relative amplitude of the depolarizing process and reduces its time constant from 80 ms to 1.5 ms. The hyperpolarizing process is too rapid to bandlimit receptor signals. The noise introduced during the passage of the signal from receptor to second-order neuron is measured by comparing signal: noise ratios and noise power spectra in the two cell types. Under daylight conditions from 50 to 70% of the total noise power is generated by events associated with the transmission of photoreceptor signals and the generation of LMC responses. According to the exponential model of transmitter release, the effects of synaptic noise are minimized when synaptic gain is maximized. Moreover, both retinal antagonism and the sigmoidal shape of the characteristic curve promote synaptic gain. We conclude that retinal antagonism and nonlinear synaptic amplification act in concert to protect receptor signals from contamination by synaptic noise. This action may explain the widespread occurrence of these processes in early visual processing.

Receptor and Interneuron Light-adaptation in the Dragonfly Visual System

1975 ◽  
Vol 30 (3-4) ◽  
pp. 306-308 ◽  
Author(s):  
Simon B. Laughlin
Keyword(s):  
Membrane Potential ◽  
Visual System ◽  
Light Adaptation ◽  
Compound Eye ◽  
Second Order ◽  
Neural Mechanisms ◽  
Background Intensity ◽  
Intracellular Recordings ◽  
Set Up

Abstract Intracellular recordings show that the receptors and second-order interneurons of the dragonfly compound eye change their sensitivity in response to maintained illumination. Comparison of receptor with interneuron shows that neural mechanisms act to ensure that the modulation of interneuron membrane potential that is set up by contrast changes is independent of background intensity.

Statistical Analysis of Second-Order Response of Marine Structures

1985 ◽  
Vol 29 (04) ◽  
pp. 270-284 ◽  
Author(s):  
Arvid Naess
Keyword(s):  
Volterra Series ◽  
Theoretical Method ◽  
Problem Formulation ◽  
Second Order ◽  
Quadratic Term ◽  
Closed Form Solutions ◽  
Marine Structure ◽  
Asymptotic Expressions ◽  
The Mean ◽  
Extreme Responses

A theoretical method is presented for estimating the response statistics of a marine structure that can be modeled as a second-order dynamic system subjected to a stationary, Gaussian sea. The method is particularly suitable for predicting extreme responses. The problem formulation expresses the response in terms of a second-order Volterra series, that is, including a linear and a quadratic term. For this response process the mean upcrossing frequency is found and asymptotic expressions are established that can be used to obtain closed-form solutions to the extreme-value problem.

Postsynaptic Dorsal Column and Cuneate Correlations in the Raccoon: A Re-evaluation by Parallel-Cascade Analysis

2002 ◽  
Vol 88 (6) ◽  
pp. 3372-3376
Author(s):  
Andrew S. French ◽  
Susan H. Dick ◽  
Douglas D. Rasmusson
Keyword(s):  
Cross Correlation ◽  
Volterra Series ◽  
Receptive Fields ◽  
Dorsal Column ◽  
Second Order ◽  
Input Signals ◽  
Cascade Analysis ◽  
Postsynaptic Dorsal Column ◽  
Positive Correlations ◽  
Cascade Method

In a previous study, we reported evidence for correlations between the firing of postsynaptic dorsal column (PSDC) neurons and cuneate neurons with overlapping receptive fields on the glabrous skin of the raccoon forepaw. The evidence was based on cross-correlation and frequency response analyses of spontaneously firing neurons. However, cross-correlation without white noise Gaussian analog inputs or Poisson distributed pulse train inputs is difficult to interpret because of the inherent convolution with the autocorrelation of the unknown input signals. While the data suggested positive correlations in the spinocuneate direction for most neuron pairs, we could not estimate the temporal characteristics of these putative connections. We have now re-analyzed these data using a parallel-cascade method to estimate the first- and second-order kernels of a Volterra series approximation to the spinocuneate system. This unbiased analysis suggests that a positive correlation occurs after about 5 ms, probably followed by a negative correlation at about 12 ms. Second-order kernels also had repeatable structure, indicating dual pathways with time separations of at least 10 ms.

Spontaneous Subthreshold Membrane Potential Fluctuations and Action Potential Variability of Rat Corticostriatal and Striatal Neurons In Vivo

1997 ◽  
Vol 77 (4) ◽  
pp. 1697-1715 ◽  
Author(s):  
Edward A. Stern ◽  
Anthony E. Kincaid ◽  
Charles J. Wilson
Keyword(s):  
Membrane Potential ◽  
Synaptic Input ◽  
High Frequency ◽  
Action Potentials ◽  
State Transitions ◽  
Striatal Neurons ◽  
Interspike Intervals ◽  
Potential Fluctuations ◽  
Membrane Potential Fluctuations

Stern, Edward A., Anthony E. Kincaid, and Charles J. Wilson. Spontaneous subthreshold membrane potential fluctuations and action potential variability of rat corticostriatal and striatal neurons in vivo. J. Neurophysiol. 77: 1697–1715, 1997. We measured the timing of spontaneous membrane potential fluctuations and action potentials of medial and lateral agranular corticostriatal and striatal neurons with the use of in vivo intracellular recordings in urethan-anesthetized rats. All neurons showed spontaneous subthreshold membrane potential shifts from 7 to 32 mV in amplitude, fluctuating between a hyperpolarized down state and depolarized up state. Action potentials arose only during the up state. The membrane potential state transitions showed a weak periodicity with a peak frequency near 1 Hz. The peak of the frequency spectra was broad in all neurons, indicating that the membrane potential fluctuations were not dominated by a single periodic function. At frequencies >1 Hz, the log of magnitude decreased linearly with the log of frequency in all neurons. No serial dependence was found for up and down state durations, or for the time between successive up or down state transitions, showing that the up and down state transitions are not due to superimposition of noisy inputs onto a single frequency. Monte Carlo simulations of stochastic synaptic inputs to a uniform finite cylinder showed that the Fourier spectra obtained for corticostriatal and striatal neurons are inconsistent with a Poisson-like synaptic input, demonstrating that the up state is not due to an increase in the strength of an unpatterned synaptic input. Frequency components arising from state transitions were separated from those arising from the smaller membrane potential fluctuations within each state. A larger proportion of the total signal was represented by the fluctuations within states, especially in the up state, than was predicted by the simulations. The individual state spectra did not correspond to those of random synaptic inputs, but reproduced the spectra of the up and down state transitions. This suggests that the process causing the state transitions and the process responsible for synaptic input may be the same. A high-frequency periodic component in the up states was found in the majority of the corticostriatal cells in the sample. The average size of the component was not different between neurons injected with QX-314 and control neurons. The high-frequency component was not seen in any of our sample of striatal cells. Corticostriatal and striatal neurons' coefficients of variation of interspike intervals ranged from 1.0 to 1.9. When interspike intervals including a down state were subtracted from the calculation, the coefficient of variation ranged from 0.4 to 1.1, indicating that a substantial proportion of spike interval variance was due to the subthreshold membrane potential fluctuations.

Light adaptation and color opponency of horizontal cells in the turtle retina

2003 ◽  
Vol 20 (4) ◽  
pp. 437-452 ◽  
Author(s):  
GILAD TWIG ◽  
HANNA LEVY ◽  
ELITE WEINER ◽  
IDO PERLMAN
Keyword(s):  
Light Adaptation ◽  
Light Sensitivity ◽  
Horizontal Cells ◽  
Background Illumination ◽  
Voltage Range ◽  
Background Adaptation ◽  
Color Opponency ◽  
Light Stimuli ◽  
Type C

Chromaticity-type (C-type) horizontal cells of the turtle retina receive antagonistic inputs from cones of different spectral types, and therefore their response to background illumination is expected to reflect light adaptation of the cones and the interactions between their antagonistic inputs. Our goal was to study the behavior of C-type horizontal cells during background illumination and to evaluate the role of wavelength in background adaptation. The photoresponses of C-type horizontal cells were recorded intracellularly in the everted eyecup preparation of the turtleMauremys caspicaduring chromatic background illuminations. The voltage range of operation was either reduced or augmented, depending upon the wavelengths of the background and of the light stimuli, while the sensitivity to light was decreased by any background. The response–intensity curves were shifted to brighter intensities and became steeper as the background lights were made brighter regardless of wavelength. Comparing the effects of cone iso-luminant backgrounds on the Red/Green C-type horizontal cells indicated that background desensitization in these cells could not solely reflect background adaptation of cones but also depend upon response compression/expansion and changes in synaptic transmission. This leads to wavelength dependency of background adaptation in C-type horizontal cells, that is expressed as increased light sensitivity (smaller threshold elevation) and improved suprathreshold contrast detection when the wavelengths of the background and light stimuli were chosen to exert opponent effects on membrane potential.

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