Processing of Color- and Noncolor-Coded Signals in the Gourami Retina. II. Amacrine Cells

1997 ◽  
Vol 78 (4) ◽  
pp. 2018-2033 ◽  
Author(s):  
Hiroko M. Sakai ◽  
Hildred Machuca ◽  
Ken-Ichi Naka
Keyword(s):  
White Noise ◽  
Amacrine Cell ◽  
Amacrine Cells ◽  
Second Order ◽  
Order Component ◽  
Response Dynamics ◽  
Dendritic Field ◽  
State Response ◽  
Order Components ◽  
Nonlinear Components

Sakai, Hiroko M., Hildred Machuca, and Ken-Ichi Naka. Processing of color- and noncolor-coded signals in the gourami retina. II. Amacrine cells. J. Neurophysiol. 78: 2018–2033, 1997. The same set of stimuli and analytic methods that was used to study the dynamics of horizontal cells ( Sakai et al. 1997a ) was applied to a study of the response dynamics and signal processing in amacrine cells in the retina of the kissing gourami, Helostoma rudolfi. The retina contains two major classes of amacrine cells that could be identified from their morphology: C and N amacrine cells. C amacrine cells had a two-layered dendritic field, whereas N cells had a monolayered dendritic field. Both types of amacrine cell were tracer-coupled but coupling was more extensive in the N amacrine cells. Responses from C amacrine cells lacked a DC component and had a small linear component that was <10% in terms of mean square error (MSE); the second-order component often accounted for >50% of the modulation response. The C amacrine cells did not show any characteristic color coding under any stimulus condition. Most responses of N cells to a pulsatile stimulus consisted of a series of depolarizing transient potentials and steady illumination did not generate any DC potential in these cells. The response to a white-noise modulated input was composed of well-defined first- and second-order components and, possibly, higher-order components. The response evoked by a red or green white-noise–modulated stimulus given alone was not color coded. Modulated red illumination in the presence of a green illumination elicited a color-coded response from >70% of N amacrine cells. Color information was carried not only by the polarity but also by the dynamics of the first-order component. No convincing evidence was obtained to indicate that the second-order component might be involved in color processing. Some N amacrine cells produced a well-defined (second-order) interaction kernel to show that the temporal sequence of red and green stimuli was a parameter to be considered. In a complex cell such as an amacrine cell, responses evoked by a pulsatile stimulus given in darkness and by modulation of a mean luminance could be very different in terms of their characteristics. It was not always possible to predict the response evoked by one stimulus from observing the cell's response to another stimulus. This is because, in N cells, a flash-evoked (nonsteady state) response is composed largely of nonlinear components whereas a modulation (steady state) response is composed of linear as well as nonlinear components.

Response dynamics and receptive-field organization of catfish amacrine cells

1992 ◽  
Vol 67 (2) ◽  
pp. 430-442 ◽  
Author(s):  
H. M. Sakai ◽  
K. Naka
Keyword(s):  
Receptive Field ◽  
White Noise ◽  
Amacrine Cells ◽  
Second Order ◽  
Response Dynamics ◽  
Field Center ◽  
Mean Luminance ◽  
Nonlinear Components ◽  
Receptive Field Center ◽  
Dc Potentials

1. We have applied Wiener analysis to a study of response dynamics of N (sustained) and C (transient) amacrine cells. Stimuli were a spot and an annulus of light, the luminance of which was modulated by two independent white-noise signals. First- and second-order Wiener kernels were computed for each spot and annulus input. The analysis allowed us to separate a modulation response into its linear and nonlinear components, and into responses generated by a receptive-field center and its surround. 2. Organization of the receptive field of N amacrine cells consists of both linear and nonlinear components. The receptive field of linear components was center-surround concentric and opposite in polarity, whereas that of second-order nonlinear components was monotonic. 3. In NA (center-depolarizing) amacrine cells, the membrane DC potentials brought about by the mean luminance of a white-noise spot or a steady spot were depolarizations, whereas those brought about by the mean luminance of a white-noise annulus or a steady annulus were hyperpolarizations. In NB (center-hyperpolarizing) amacrine cells, this relationship was reversed. If both receptive-field center and surround were stimulated by a spot and annulus, membrane DC potentials became close to the dark level and the amplitude of modulation responses became larger. 4. The linear responses of a receptive-field center of an N amacrine cell, measured in terms of the first-order Wiener kernel, were facilitated if the surround was stimulated simultaneously. The amplitude of the kernel became larger, and its peak response time became shorter. The same facilitation occurred in the linear responses of a receptive-field surround if the center was stimulated simultaneously. 5. The second-order nonlinear responses were not usually generated in N amacrine cells if the stimulus was either a white-noise spot or a white-noise annulus alone. Significant second-order nonlinearity appeared if the other region of the receptive field was also stimulated. 6. Membrane DC potentials of C amacrine cells remained at the dark level with either a white-noise spot or a white-noise annulus. The cell responded only to modulations. 7. The major characteristics of center and surround responses of C amacrine cells could be approximated by second-order Wiener kernels of the same structure. The receptive field of second-order nonlinear components of C amacrine cells was monotonic.(ABSTRACT TRUNCATED AT 400 WORDS)

Processing of Color- and Noncolor-Coded Signals in the Gourami Retina. III. Ganglion Cells

1997 ◽  
Vol 78 (4) ◽  
pp. 2034-2047 ◽  
Author(s):  
Hiroko M. Sakai ◽  
Hildred Machuca ◽  
Michael J. Korenberg ◽  
Ken-Ichi Naka
Keyword(s):  
White Noise ◽  
Spike Train ◽  
Ganglion Cells ◽  
Green Light ◽  
Amacrine Cells ◽  
Second Order ◽  
First Order ◽  
Wide Range ◽  
Mean Luminance ◽  
Order Components

Sakai, Hiroko M., Hildred Machuca, Michael J. Korenberg, and Ken-Ichi Naka. Processing of color- and noncolor-coded signals in the gourami retina. III. Ganglion cells. J. Neurophysiol. 78: 2034–2047, 1997. The dynamics of intracellular responses from ganglion cells, as well as that of spike discharges, were studied with the stimulus regimens and analytic procedures identical to those used to study the dynamics of the responses from horizontal and amacrine cells ( Sakai et al. 1997a , b ). The stimuli used were large fields of red and green light given as a pulsatile input or modulation about a mean luminance by a white-noise signal. Spike discharges evoked by a white-noise stimulus were analyzed in exactly the same manner as that used for analysis of analog responses. The canonical nature of kernels allowed us to correlate the first- and second-order components in a spike train with those of the intracellular responses from horizontal, amacrine, and ganglion cells. Both red and green stimuli given alone in darkness produced noncolor-coded responses from all ganglion cells. In the case of some cells, steady red illumination changed the polarity or waveform of the response to green light. Color-coded ganglions responded only to simultaneous color contrast. Nonlinearities recovered from intracellular responses, and spike discharges were similar to those found in responses from amacrine cells and were of two types, one characteristic of the C amacrine cells and the other characteristic of the N amacrine cells. The first-order kernels of most ganglion cells could be divided into two basic types, biphasic and triphasic. The combination of kernels of these two basic types with different polarities can produce a wide range of responses. Addition of two types of second-order nonlinearity could render color coding in this relatively simple retina as an extremely complex process. Color information appeared to be represented by the polarity, as well as the waveform, of the first-order kernel. The response dynamics is a means of transmission of color-coded information. Second-order components carry information about changes around a mean luminance regardless of the color of an input. Some spike discharges produced a well-defined cross-kernel between red and green inputs to show that a particular time sequence of red and green stimuli was detected by the retinal neuron network. The similarity between signatures of second-order kernels for both amacrine and ganglion cells indicates that signals undergo a minimal transformation in the temporal domain when they are transmitted from amacrine to ganglion cells and then transformed into a spike train. Under our experimental conditions, a single spike train carried simultaneously information about red and green inputs, as well as about linear and nonlinear components. In addition, the spike train also carries a cross-talk component. A spike train is a carrier of multiple signals. Conversely, many types of information in a stimulus are independently encoded into a spike train.

Dissection of the neuron network in the catfish inner retina. V. Interactions between NA and NB amacrine cells

1990 ◽  
Vol 63 (1) ◽  
pp. 120-130 ◽  
Author(s):  
H. M. Sakai ◽  
K. I. Naka
Keyword(s):  
White Noise ◽  
Small Amplitude ◽  
Amacrine Cell ◽  
Amacrine Cells ◽  
The Other ◽  
First Order ◽  
Membrane Noise ◽  
Current Pulses ◽  
Damped Oscillation ◽  
Order Kernel

1. Simultaneous intracellular recordings were made from two neighboring N amacrine cells, one an ON amacrine (NA) cell and the other an OFF amacrine (NB) cell. Extrinsic current was injected into one amacrine cell, and the resulting intracellular responses were recorded from the other amacrine cell. Test signals included 1) a single-frequency sinusoid, 2) a depolarizing or hyperpolarizing pulse, or 3) a white-noise modulated current. In some cell pairs, membrane noise was measured in the dark as well as under a steady background illumination. 2. Current pulses injected into a NA cell evoked a damped oscillation from a NB cell. The first-order kernel derived by cross-correlating the white-noise current injected into a NA cell against the evoked response from a NB cell was a large depolarization followed by a damped oscillation. The frequency of oscillations varied slightly from pair to pair but averaged 35 Hz. 3. Current pulses injected into a NB cell evoked a sign-inverting response (hyperpolarization) of very small amplitude from a NA cell. Similarly, the first-order kernel was a hyperpolarization of very small amplitude. 4. The power spectrum of the membrane noise recorded from NA and NB cells in the dark or during steady illumination often showed a peak at 35 Hz. Such membrane noise synchronizes synergistically among NA cells and among NB cells in the dark. In addition, the membrane fluctuations seen in NA and NB cells in the dark were out of phase. 5. Transmission between NA and NB cells was largely accounted for by a linear component; however, a very small but significant second- and third-order nonlinearity was also generated. 6. These results show that the interactions occurring between amacrine cells of opposite response polarity are much more complex than those between cells of the same response polarity and that the neural circuitry in the inner retina actively controls interactions between ON and OFF channels in the dark as well as in the presence of light stimuli.

scholarly journals Dynamics of cockroach ocellar neurons.

1986 ◽  
Vol 88 (2) ◽  
pp. 275-292 ◽  
Author(s):  
M Mizunami ◽  
H Tateda ◽  
K Naka
Keyword(s):  
White Noise ◽  
Periplaneta Americana ◽  
Second Order ◽  
Response Dynamics ◽  
Slow Potential ◽  
Order Neuron ◽  
Amplitude Compression ◽  
Noise Input ◽  
Modulated Light ◽  
Order Kernel

The incremental responses from the second-order neurons of the ocellus of the cockroach, Periplaneta americana, have been measured. The stimulus was a white-noise-modulated light with various mean illuminances. The kernels, obtained by cross-correlating the white-noise input against the resulting response, provided a measure of incremental sensitivity as well as of response dynamics. We found that the incremental sensitivity of the second-order neurons was an exact Weber-Fechner function; white-noise-evoked responses from second-order neurons were linear; the dynamics of second-order neurons remain unchanged over a mean illuminance range of 4 log units; the small nonlinearity in the response of the second-order neuron was a simple amplitude compression; and the correlation between the white-noise input and spike discharges of the second-order neurons produced a first-order kernel similar to that of the cell's slow potential. We conclude that signal processing in the cockroach ocellus is simple but different from that in other visual systems, including vertebrate retinas and insect compound eyes, in which the system's dynamics depend on the mean illuminance.

Amacrine cells, displaced amacrine cells and interplexiform cells in the retina of the rat

1980 ◽  
Vol 208 (1173) ◽  
pp. 415-431 ◽  
Keyword(s):  
Ganglion Cell ◽  
Amacrine Cell ◽  
Ganglion Cell Layer ◽  
Cell Layer ◽  
Amacrine Cells ◽  
Cell System ◽  
Wide Field ◽  
Dendritic Field ◽  
Displaced Amacrine Cells ◽  
Bistratified Cell

The amacrine cells in the retina of the rat are described in Golgi-stained whole-mounted retinae. Nine morphologically distinct types of cell were found: one type of diffuse cell, five types of unistratified cell, two types of bistratified cell, and one type of stratified diffuse cell. Measurements show that the largest unistratified cells have a dendritic field 2 mm across. One type of interplexiform cell is also described. Wide-field diffuse amacrine cells and unistratified amacrine cells were found with their somata located in either the inner nuclear layer or the ganglion cell layer. It is clear that there may be an amacrine cell system in the ganglion cell layer of the rat retina.

scholarly journals Study on Nonlinear Stochastic Process of Deck Slamming on Floating Offshore Platform

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 231
Author(s):  
Hyun-Seung Nam ◽  
Yonghwan Kim
Keyword(s):  
Statistical Approach ◽  
Volterra Series ◽  
Probability Distributions ◽  
Joint Probability ◽  
Second Order ◽  
Wave Crest ◽  
Order Component ◽  
Wave Elevation ◽  
Order Components ◽  
Joint Probability Density

In this paper, a semi-analytic method is introduced to predict the deck-slamming probability and corresponding loads. This method is based on a nonlinear statistical approach that takes into account the linear and second-order components of the relative wave elevation up to the second order. The linear and second-order wave elevation is assumed to be a two-term Volterra series. The joint probability density function of the relative wave elevation and velocity are formulated using the Hermite-moment method, and the probability distributions of the wave crest and relative wave velocity are calculated. These probability distributions are verified using the data sampled from the linear and second-order relative wave elevation. Based on this formulation, the probabilities of deck slamming and slamming-induced loads are estimated. This method is applied to a tension leg platform (TLP) model, and the effects of the second-order component of the relative wave elevation on the deck slamming are investigated.

Signal transmission in the catfish retina. III. Transmission to type-C cell

1985 ◽  
Vol 53 (2) ◽  
pp. 411-428 ◽  
Author(s):  
M. Sakuranaga ◽  
K. Naka
Keyword(s):  
Signal Transmission ◽  
Horizontal Cell ◽  
Frequency Doubling ◽  
Amacrine Cells ◽  
Second Order ◽  
C Cells ◽  
Type C ◽  
The Difference ◽  
Nonlinear Components ◽  
Relationship Of

Current injected into horizontal-cell somas and axons produced transient (on-off) depolarizations from type-C cells (commonly known as transient amacrine cells) similar to those produced by light. Both the light- and current-induced responses had very small linear components and nonlinear components as represented by the second-order kernels, which reproduced the cell's response with a reasonable degree of accuracy. The second-order kernels were well defined and stereotyped. The quadratic nature of the nonlinear component is reflected in the frequency doubling response as well as the very steep input-output relationship of the cell. Type-C cell's responses evoked by light and current differed in a subtle but distinct fashion, and this difference appeared in the signature of the second-order kernels. The light-produced kernels had two diagonal positive peaks and off-diagonal valleys ("four-eye" structure), whereas the current-produced kernels had a single on-diagonal positive peak with off-diagonal negative valleys ("three-eye" structure). The difference in the kernel configuration was reflected in the cell's step-evoked response. Some type-C cells produced faster responses whereas others produced slower responses, whether evoked by light or by current. Our past and present results show that type-C cells produce a very nonlinear response that is not necessarily complex.

Signal transmission in the catfish retina. II. Transmission to type-N cell

1985 ◽  
Vol 53 (2) ◽  
pp. 390-410 ◽  
Author(s):  
M. Sakuranaga ◽  
K. Naka
Keyword(s):  
White Noise ◽  
Light Stimulus ◽  
Horizontal Cell ◽  
Amacrine Cells ◽  
Complex Nature ◽  
Evoked Responses ◽  
Synaptic Organization ◽  
Third Order ◽  
Extrinsic Noise ◽  
Nonlinear Components

Responses from channel catfish type-N (sustained amacrine) cells were evoked either by step changes in illuminance, i.e. brightening or dimming from a mean illuminance, or by a white-noise modulated light stimulus. Current injected into the horizontal-cell soma or axon produced responses in type-N cells that were very similar to those produced by light stimuli. Light- and current-evoked responses had linear and second- and third-order nonlinear components; the former contributed 40-50%, whereas the latter contributed 20-30% to the total response. The remainder of the response could have been due to higher-order nonlinearities or to intrinsic as well as extrinsic noise. Nonlinear components in the light- and current-evoked responses were sharp transient peaks, which were prominent in white-noise-evoked responses, and oscillatory wavelets. The high-frequency components in the cell's response, which result from nonlinearity, were absent in the responses from bipolar and horizontal cells. The nonlinear responses were predicted by the second- and third-order kernels. The type-N cell response was complex because the response had both linear and nonlinear components, and because of the complexities of second- and, probably, third-order kernels. The cell's complex response reflects the complex nature of the cell's function as well as its synaptic organization.

Random exponential attractor for second-order nonautonomous stochastic lattice systems with multiplicative white noise

2019 ◽  
Vol 19 (06) ◽  
pp. 1950044
Author(s):  
Haijuan Su ◽  
Shengfan Zhou ◽  
Luyao Wu
Keyword(s):  
White Noise ◽  
Weighted Space ◽  
Sufficient Conditions ◽  
Second Order ◽  
Lattice System ◽  
Exponential Attractor ◽  
Lattice Systems ◽  
Random System ◽  
Multiplicative White Noise ◽  
Infinite Sequences

We studied the existence of a random exponential attractor in the weighted space of infinite sequences for second-order nonautonomous stochastic lattice system with linear multiplicative white noise. Firstly, we present some sufficient conditions for the existence of a random exponential attractor for a continuous cocycle defined on a weighted space of infinite sequences. Secondly, we transferred the second-order stochastic lattice system with multiplicative white noise into a random lattice system without noise through the Ornstein–Uhlenbeck process, whose solutions generate a continuous cocycle on a weighted space of infinite sequences. Thirdly, we estimated the bound and tail of solutions for the random system. Fourthly, we verified the Lipschitz continuity of the continuous cocycle and decomposed the difference between two solutions into a sum of two parts, and carefully estimated the bound of the norm of each part and the expectations of some random variables. Finally, we obtained the existence of a random exponential attractor for the considered system.

scholarly journals A Hida–Malliavin white noise calculus approach to optimal control

2018 ◽  
Vol 21 (03) ◽  
pp. 1850014
Author(s):  
Nacira Agram ◽  
Bernt Øksendal
Keyword(s):  
Optimal Control ◽  
Diffusion Coefficient ◽  
White Noise ◽  
Backward Stochastic Differential Equation ◽  
Second Order ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Alternative Approach ◽  
Quadratic Optimal Control ◽  
White Noise Calculus

The classical maximum principle for optimal stochastic control states that if a control [Formula: see text] is optimal, then the corresponding Hamiltonian has a maximum at [Formula: see text]. The first proofs for this result assumed that the control did not enter the diffusion coefficient. Moreover, it was assumed that there were no jumps in the system. Subsequently, it was discovered by Shige Peng (still assuming no jumps) that one could also allow the diffusion coefficient to depend on the control, provided that the corresponding adjoint backward stochastic differential equation (BSDE) for the first-order derivative was extended to include an extra BSDE for the second-order derivatives. In this paper, we present an alternative approach based on Hida–Malliavin calculus and white noise theory. This enables us to handle the general case with jumps, allowing both the diffusion coefficient and the jump coefficient to depend on the control, and we do not need the extra BSDE with second-order derivatives. The result is illustrated by an example of a constrained linear-quadratic optimal control.

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