Complex Spike-Event Pattern of Transient on-off Retinal Ganglion Cells

2006 ◽  
Vol 96 (6) ◽  
pp. 2845-2856 ◽  
Author(s):  
Martin Greschner ◽  
Andreas Thiel ◽  
Jutta Kretzberg ◽  
Josef Ammermüller
Keyword(s):  
Retinal Ganglion Cells ◽  
Computational Models ◽  
Ganglion Cells ◽  
Temporal Structure ◽  
Gain Control ◽  
Saccadic Eye Movements ◽  
Cell Subpopulation ◽  
Retinal Ganglion ◽  
Filter Model ◽  
Spike Event

on-off transient ganglion cells of the turtle retina show distinct spike-event patterns in response to abrupt intensity changes, such as during saccadic eye movements. These patterns consist of two main spike events, with the latency of each event showing a systematic dependency on stimulus contrast. Whereas the latency of the first event decreases monotonically with increasing contrast, as expected, the second event shows the shortest latency for intermediate contrasts and a longer latency for high and low contrasts. These spike-event patterns improve the discrimination of different light-intensity transitions based on ensemble responses of the on-off transient ganglion cell subpopulation. Although the discrimination results are far better than chance using either spike counts or latencies of the first spikes, they are further improved by using properties of the second spike event. The best classification results are obtained when spike rates and latencies of both events are considered in combination. Thus spike counts and temporal structure of retinal ganglion cells carry complementary information about the stimulus condition, and thus spike-event patterns could be an important aspect of retinal coding. To investigate the origin of the spike-event patterns in retinal ganglion cells, two computational models of retinal processing are compared. A linear–nonlinear model consisting of separate filters for on and off response components fails to reproduce the spike-event patterns. A more complex cascade filter model, however, accurately predicts the timing of the spike events by using a combination of gain control loop and spike rate adaptation.

Contrast gain control in the primate retina: P cells are not X-like, some M cells are

1992 ◽  
Vol 8 (5) ◽  
pp. 483-486 ◽  
Author(s):  
Ethan A. Benardete ◽  
Ehud Kaplan ◽  
Bruce W. Knight
Keyword(s):  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Temporal Frequency ◽  
Gain Control ◽  
M Cells ◽  
Retinal Ganglion ◽  
M Cell ◽  
Contrast Gain ◽  
Contrast Gain Control ◽  
P Cells

AbstractPrimate retinal ganglion cells that project to the magnocellular layers of the lateral geniculate nucleus (M) are much more sensitive to luminance contrast than those ganglion cells projecting to the parvocellular layers (P). We now report that increasing contrast modifies the temporal-frequency response of M cells, but not of P cells. With rising contrast, the M cells' responses to sinusoidal stimuli show an increasing attenuation at low temporal frequencies while the P cells' responses scale uniformly. The characteristic features of M-cell dynamics are well described by a model originally developed for the X and Y cells of the cat, where the hypothesized nonlinear feedback mechanism responsible for this behavior has been termed the contrast gain control (Shapley & Victor, 1978, 1981; Victor, 1987, 1988). These data provide further physiological evidence that the M-cell pathway differs from the P-cell pathway with regard to the functional elements in the retina. Furthermore, the similarity in dynamics between primate M cells and cat X and Y retinal ganglion cells suggests the possibility that P cells, being different from either group, are a primate specialization not found in the retinae of lower mammals.

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.

Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses

2020 ◽  
Vol 30 (09) ◽  
pp. 2050045 ◽  
Author(s):  
Antonio Lozano ◽  
Juan Sebastián Suárez ◽  
Cristina Soto-Sánchez ◽  
Javier Garrigós ◽  
J. Javier Martínez-Alvarez ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
Deep Learning ◽  
Retinal Ganglion Cells ◽  
Computational Models ◽  
Microelectrode Array ◽  
Ganglion Cells ◽  
Semantic Segmentation ◽  
Video Stream ◽  
Retinal Ganglion ◽  
Task Oriented

Visual neuroprosthesis, that provide electrical stimulation along several sites of the human visual system, constitute a potential tool for vision restoration for the blind. Scientific and technological progress in the fields of neural engineering and artificial vision comes with new theories and tools that, along with the dawn of modern artificial intelligence, constitute a promising framework for the further development of neurotechnology. In the framework of the development of a Cortical Visual Neuroprosthesis for the blind (CORTIVIS), we are now facing the challenge of developing not only computationally powerful tools and flexible approaches that will allow us to provide some degree of functional vision to individuals who are profoundly blind. In this work, we propose a general neuroprosthesis framework composed of several task-oriented and visual encoding modules. We address the development and implementation of computational models of the firing rates of retinal ganglion cells and design a tool — Neurolight — that allows these models to be interfaced with intracortical microelectrodes in order to create electrical stimulation patterns that can evoke useful perceptions. In addition, the developed framework allows the deployment of a diverse array of state-of-the-art deep-learning techniques for task-oriented and general image pre-processing, such as semantic segmentation and object detection in our system’s pipeline. To the best of our knowledge, this constitutes the first deep-learning-based system designed to directly interface with the visual brain through an intracortical microelectrode array. We implement the complete pipeline, from obtaining a video stream to developing and deploying task-oriented deep-learning models and predictive models of retinal ganglion cells’ encoding of visual inputs under the control of a neurostimulation device able to send electrical train pulses to a microelectrode array implanted at the visual cortex.

scholarly journals Insights from Computational Modelling: Selective Stimulation of Retinal Ganglion Cells

2020 ◽  
pp. 233-247
Author(s):  
Tianruo Guo ◽  
David Tsai ◽  
Siwei Bai ◽  
Mohit Shivdasani ◽  
Madhuvanthi Muralidharan ◽  
...  
Keyword(s):  
Retinal Ganglion Cells ◽  
Computational Models ◽  
Ganglion Cells ◽  
Computational Modelling ◽  
Retinal Ganglion ◽  
Retinal Prostheses ◽  
Differential Activation ◽  
Photoreceptor Layer ◽  
Suprachoroidal Space ◽  
Wide Range

AbstractImprovements to the efficacy of retinal neuroprostheses can be achieved by developing more sophisticated neural stimulation strategies to enable selective or differential activation of specific retinal ganglion cells (RGCs). Recent retinal studies have demonstrated the ability to differentially recruit ON and OFF RGCs – the two major information pathways of the retina – using high-frequency electrical stimulation (HFS). However, there remain many unknowns, since this is a relatively unexplored field. For example, can we achieve ON/OFF selectivity over a wide range of stimulus frequencies and amplitudes? Furthermore, existing demonstrations of HFS efficacy in retinal prostheses have been based on epiretinal placement of electrodes. Other clinically popular techniques include subretinal or suprachoroidal placement, where electrodes are located at the photoreceptor layer or in the suprachoroidal space, respectively, and these locations are quite distant from the RGC layer. Would HFS-based differential activation work from these locations? In this chapter, we conducted in silico investigations to explore the generalizability of HFS to differentially active ON and OFF RGCs. Computational models are particularly well suited for these investigations. The electric field can be accurately described by mathematical formulations, and simulated neurons can be “probed” at resolutions well beyond those achievable by today’s state-of-the-art experimental techniques.

Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex

2001 ◽  
Vol 13 (6) ◽  
pp. 1255-1283 ◽  
Author(s):  
Rufin Van Rullen ◽  
Simon J. Thorpe
Keyword(s):  
Visual Cortex ◽  
Information Transmission ◽  
Retinal Ganglion Cells ◽  
Information Transfer ◽  
Ganglion Cells ◽  
Rank Order ◽  
Temporal Structure ◽  
Retinal Ganglion ◽  
Information Transfer Rate ◽  
The Mean

It is often supposed that the messages sent to the visual cortex by the retinal ganglion cells are encoded by the mean firing rates observed on spike trains generated with a Poisson process. Using an information transmission approach, we evaluate the performances of two such codes, one based on the spike count and the other on the mean interspike interval, and compare the results with a rank order code, where the first ganglion cells to emit a spike are given a maximal weight. Our results show that the rate codes are far from optimal for fast information transmission and that the temporal structure of the spike train can be efficiently used to maximize the information transfer rate under conditions where each cell needs to fire only one spike.

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