scholarly journals Brightness change is optimal stimulus for parasol retinal ganglion cells

2021 ◽  
Author(s):  
Artem Pinchuk
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Smooth Transition ◽  
Linear Filter ◽  
Retinal Ganglion ◽  
Spike Response ◽  
Brightness Change ◽  
Temporal Properties ◽  
Two Sides ◽  
Rebound Excitation

Abstract Magnocellular-projecting retinal ganglion cells show spike response in two cases. Firstly, as a result of presentation of the optimal stimulus. Secondly, rebound excitation when removing the opposite stimulus. Also, there are studies suggesting that rebound excitation meets conditions to participate in visual perception at the same sensitivity and reaction speed as a response to the optimal stimulus. Thus, white noise stimulation creates possibility to catch the form of a smooth transition from one type of response to another. Using freely available data, a spike-triggered behavior map was built that does not show the area of silence between those two types of spike triggers. Moreover, linear filter with biphasic temporal properties which work as the derivative kernel demonstrate that both responses are two sides of the same coin. Thus, it is suggested to determine the optimal stimulus for magnocellular-projecting retinal ganglion cells as brightness change according to concentric center–surround receptive field structure.

How Retinal Ganglion Cells Prevent Synaptic Noise From Reaching the Spike Output

2004 ◽  
Vol 92 (4) ◽  
pp. 2510-2519 ◽  
Author(s):  
Jonathan B. Demb ◽  
Peter Sterling ◽  
Michael A. Freed
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Spike Rate ◽  
Membrane Properties ◽  
Retinal Ganglion ◽  
Intracellular Recordings ◽  
Spike Response ◽  
Membrane Fluctuations ◽  
Fluctuation Amplitude ◽  
Synaptic Convergence

Synaptic vesicles are released stochastically, and therefore stimuli that increase a neuron's synaptic input might increase noise at its spike output. Indeed this appears true for neurons in primary visual cortex, where spike output variability increases with stimulus contrast. But in retinal ganglion cells, although intracellular recordings (with spikes blocked) showed that stronger stimuli increase membrane fluctuations, extracellular recordings showed that noise at the spike output is constant. Here we show that these seemingly paradoxical findings occur in the same cell and explain why. We made intracellular recordings from ganglion cells, in vitro, and presented periodic stimuli of various contrasts. For each stimulus cycle, we measured the response at the stimulus frequency (F1) for both membrane potential and spikes as well as the spike rate. The membrane and spike F1 response increased with contrast, but noise (SD) in the F1 responses and the spike rate was constant. We also measured membrane fluctuations (with spikes blocked) during the response depolarization and found that they did increase with contrast. However, increases in fluctuation amplitude were small relative to the depolarization (<10% at high contrast). A model based on estimated synaptic convergence, release rates, and membrane properties accounted for the relative magnitudes of fluctuations and depolarization. Furthermore, a cell's peak spike response preceded the peak depolarization, and therefore fluctuation amplitude peaked as the spike response declined. We conclude that two extremely general properties of a neuron, synaptic convergence and spike generation, combine to minimize the effects of membrane fluctuations on spiking.

scholarly journals Salamander retinal ganglion cell responses to rich stimuli

2020 ◽  
Author(s):  
Kolia Sadeghi ◽  
Michael J. Berry
Keyword(s):  
Ganglion Cell ◽  
Retinal Ganglion Cells ◽  
Spatial Information ◽  
Ganglion Cells ◽  
Light Stimulus ◽  
Linear Filter ◽  
Retinal Ganglion ◽  
Linear Filters ◽  
Cell Responses ◽  
Random Stimulus

AbstractThe retina’s phenomenological function is often considered to be well-understood: individual retinal ganglion cells are sensitive to a projection of the light stimulus movie onto a classical center-surround linear filter. Recent models elaborating on this basic framework by adding a second linear filter or spike histories, have been quite successful at predicting ganglion cell spikes for spatially uniform random stimuli, and for random stimuli varying spatially with low resolution. Fitting models for stimuli with more finely grained spatial variations becomes difficult because of the very high dimensionality of such stimuli. We present a method of reducing the dimensionality of a fine one dimensional random stimulus by using wavelets, allowing for several clean predictive linear filters to be found for each cell. For salamander retinal ganglion cells, we find in addition to the spike triggered average, 3 identifiable types of linear filters which modulate the firing of most cells. While some cells can be modeled fairly accurately, many cells are poorly captured, even with as many as 4 filters. The new linear filters we find shed some light on the nonlinearities in the retina’s integration of temporal and fine spatial information.

scholarly journals Temporal properties of dual-peak responses of mouse retinal ganglion cells and effects of inhibitory pathways

2016 ◽  
Vol 10 (3) ◽  
pp. 211-223 ◽  
Author(s):  
Ru-Jia Yan ◽  
Hai-Qing Gong ◽  
Pu-Ming Zhang ◽  
Shi-Gang He ◽  
Pei-Ji Liang
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Retinal Ganglion ◽  
Temporal Properties ◽  
Inhibitory Pathways

The dynamics of primate M retinal ganglion cells

1999 ◽  
Vol 16 (2) ◽  
pp. 355-368 ◽  
Author(s):  
ETHAN A. BENARDETE ◽  
EHUD KAPLAN
Keyword(s):  
Frequency Response ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Temporal Frequency ◽  
Gain Control ◽  
Linear Filter ◽  
M Cells ◽  
Retinal Ganglion ◽  
Contrast Gain ◽  
Contrast Gain Control

The retinal ganglion cells (RGCs) of the primate form at least two classes—M and P—that differ fundamentally in their functional properties. M cells have temporal-frequency response characteristics distinct from P cells (Benardete et al., 1992; Lee et al., 1994). In this paper, we elaborate on the temporal-frequency responses of M cells and focus in detail on the contrast gain control (Shapley & Victor, 1979a,b). Earlier data showed that the temporal-frequency response of M cells is altered by the level of stimulus contrast (Benardete et al., 1992). Higher contrast shifts the peak of the frequency-response curve to higher temporal frequency and produces a phase advance. In this paper, by fitting the data to a linear filter model, the effect of contrast on the temporal-frequency response is subsumed into a change in a single parameter in the model. Furthermore, the model fits are used to predict the response of M cells to steps of contrast, and these predictions demonstrate the dynamic effect of contrast on the M cells' response. We also present new data concerning the spatial organization of the contrast gain control in the primate and show that the signal that controls the contrast gain must come from a broadly distributed network of small subunits in the surround of the M-cell receptive field.

Mechanism underlying rebound excitation in retinal ganglion cells

2007 ◽  
Vol 24 (5) ◽  
pp. 709-731 ◽  
Author(s):  
PRATIP MITRA ◽  
ROBERT F. MILLER
Keyword(s):  
Calcium Channels ◽  
Retinal Ganglion Cells ◽  
Low Voltage ◽  
Ganglion Cells ◽  
Resting Membrane Potential ◽  
Retinal Ganglion ◽  
Action Potential Firing ◽  
Potential Firing ◽  
Sufficient Magnitude ◽  
Rebound Excitation

Retinal ganglion cells (RGCs) display the phenomenon of rebound excitation, which is observed as rebound sodium action potential firing initiated at the termination of a sustained hyperpolarization below the resting membrane potential (RMP). Rebound impulse firing, in contrast to corresponding firing elicited from rest, displayed a lower net voltage threshold, shorter latency and was invariably observed as a phasic burst-like doublet of spikes. The preceding hyperpolarization leads to the recruitment of a Tetrodotoxin-insensitive depolarizing voltage overshoot, termed as the net depolarizing overshoot (NDO). Based on pharmacological sensitivities, we provide evidence that the NDO is composed of two independent but interacting components, including (1) a regenerative low threshold calcium spike (LTCS) and (2) a non-regenerative overshoot (NRO). Using voltage and current clamp recordings, we demonstrate that amphibian RGCs possess the hyperpolarization activated mixed cation channels/current,Ih, and low voltage activated (LVA) calcium channels, which underlie the generation of the NRO and LTCS respectively. At the RMP, theIhchannels are closed and the LVA calcium channels are inactivated. A hyperpolarization of sufficient magnitude and duration activatesIhand removes the inactivation of the LVA calcium channels. On termination of the hyperpolarizing influence,Ihadds an immediate depolarizing influence that boosts the generation of the LTCS. The concerted action of both conductances results in a larger amplitude and shorter latency NDO than either mechanism could achieve on its own. The NDO boosts the generation of conventional sodium spikes which are triggered on its upstroke and crest, thus eliciting rebound excitation.

Decoding Visual Information From a Population of Retinal Ganglion Cells

1997 ◽  
Vol 78 (5) ◽  
pp. 2336-2350 ◽  
Author(s):  
David K. Warland ◽  
Pamela Reinagel ◽  
Markus Meister
Keyword(s):  
Action Potential ◽  
Ganglion Cell ◽  
Retinal Ganglion Cells ◽  
Visual Information ◽  
Ganglion Cells ◽  
Spike Trains ◽  
Linear Filter ◽  
Retinal Ganglion ◽  
Response Type ◽  
Local Cluster

Warland, David K., Pamela Reinagel, and Markus Meister.Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78: 2336–2350, 1997. This work investigates how a time-dependent visual stimulus is encoded by the collective activity of many retinal ganglion cells. Multiple ganglion cell spike trains were recorded simultaneously from the isolated retina of the tiger salamander using a multielectrode array. The stimulus consisted of photopic, spatially uniform, temporally broadband flicker. From the recorded spike trains, an estimate was obtained of the stimulus intensity as a function of time. This was compared with the actual stimulus to assess the quality and quantity of visual information conveyed by the ganglion cell population. Two algorithms were used to decode the spike trains: an optimized linear filter in which each action potential made an additive contribution to the stimulus estimate and an artificial neural network trained by back-propagation to match spike trains with stimuli. The two methods performed indistinguishably, suggesting that most of the information about this stimulus can be extracted by linear operations on the spike trains. Individual ganglion cells conveyed information at a rate of 3.2 ± 1.7 bits/s (mean ± SD), with an average information content per spike of 1.6 bits. The maximal possible rate of information transmission compatible with the measured spiking statistics was 13.9 ± 6.3 bits/s. On average, ganglion cells used 22% of this capacity to encode visual information. When a decoder received two spike trains of the same response type, the reconstruction improved only marginally over that obtained from a single cell. However, a decoder using an on and an off cell extracted as much information as the sum of that obtained from each cell alone. Thus cells of opposite response type encode different and nonoverlapping features of the stimulus. As more spike trains were provided to the decoder, the total information rate rapidly saturated, with 79% of the maximal value obtained from a local cluster of just four neurons of different functional types. The decoding filter applied to a given neuron's spikes within such a multiunit decoder differed substantially from the filter applied to that same neuron in a single-unit decoder. This shows that the optimal interpretation of a ganglion cell's action potential depends strongly on the simultaneous activity of other nearby cells. The quality of the stimulus reconstruction varied greatly with frequency: flicker components below 1 Hz and above 10 Hz were reconstructed poorly, and the performance was optimal near 2.5 Hz. Further analysis suggests that temporal encoding by ganglion cell spike trains is limited by slow phototransduction in the cone photoreceptors and a corrupting noise source proximal to the cones.

Receptive-field properties of Q retinal ganglion cells of the cat

1995 ◽  
Vol 12 (2) ◽  
pp. 285-300 ◽  
Author(s):  
J.B. Troy ◽  
D.E. Schweitzer-Tong ◽  
Ch. Enroth-Cugell
Keyword(s):  
Receptive Field ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Quantitative Description ◽  
Spatial Summation ◽  
Nonlinear Behavior ◽  
Retinal Ganglion ◽  
Temporal Properties ◽  
X Cells ◽  
Y Cells

AbstractThe goal of this work was to provide a detailed quantitative description of the recepii ve-field properties of one of the types of rarely encountered retinal ganglion cells of cat; the cell named the Q-cell by Enroth-Cugell et al. (1983). Quantitative comparisons are made between the discharge statistics and between the spatial receptive properties of Q-cells and the most common of cat retinal ganglion cells, the X-cells. The center-surround receptive field of the Q-cell is modeled here quantitatively and the typical Q-cell is described. The temporal properties of the Q-cell receptive field were also investigated and the dynamics of the center mechanism of the Q-cell modeled quantitatively. In addition, the response vs. contrast relationship for a Q-cell at optimal spatial and temporal frequencies is shown, and Q-cells are also demonstrated to have nonlinear spatial summation somewhat like that exhibited by Y-cells, although much higher contrasts are required to reveal this nonlinear behavior. Finally, the relationship between Q-cells and Barlow and Levick's (1969) luminance units was investigated and it was found that most Q-cells could not be luminance units.

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