Frequency responses of rat retinal ganglion cells

<div>This repository contains the data associated with the manuscript, "Frequency responses of rat retinal ganglion cells", by Alex E. Hadjinicolaou et al., published in PLOS ONE. There are three components:</div><div><br></div><div>* recordings: whole-cell recordings from retinal ganglion cells (RGCs) [recordings.7z];</div><div>* reconstructions: morphological reconstructions from a subset of the recorded RGCs [ANU.7z | NVRI, part 1-3.7z]; and</div><div>* figures: MATLAB code used to generate the manuscript figures.</div><div><br></div><div>Note that there is not always a 1-1 relationship between the recording name (e.g. 050211r1c3) and the Cell ID associated with the reconstruction (in this case, 20110502_c2). The Excel sheet "recordings" within the file "data summary.xlsx" lists these associations. The "morphology" sheet within the same file contains the soma/dendritic field data obtained from measurements made using FIJI, an ImageJ-based analysis program. Morphological classification was guided by Sun W, Li N, He S. Large-scale morophological survey of rat retinal ganglion cells. Vis Neurosci. 2002;19: 483–493.</div

The responses of model end-stopped neurons in V1 (plotted using the same format as in Fig 6).

<p>The neurons at the end-points of the bar have much stronger activity compared to neurons along the edge. In this case, the input stimulus is a bar moving to the right, so neurons at the terminators selective to this direction have higher activity. As a result of lateral inhibition between neurons in V1, the activities of the neurons along the bar are suppressed.</p

The activity of model V1 complex neurons.

<p>Each graph shows the activity of the V1 complex neurons selective to the direction shown by the colored arrow. The angle of each arrow also indicates its direction. The axes represent the location and the gray scale intensity indicates the level of activity. Neurons at the edges have higher activity compared to neurons at the terminators, which have unambiguous motion signals. The cartoon in the middle summarizes the results shown in eight graphs. The colored section of the bar shows neurons selective to the directions that have the highest levels of activity at those locations. For a bar moving towards the right, the terminators, indicated by the purple color, show the correct direction of motion; the colors of the edges represent the directions that are incorrect because of the aperture problem.</p

The average error of the integration neurons in the network to correctly classify the direction of motion with different levels of neural noise.

<p>The represented error is the average of the measured values of the error after 10 experiments and the error bars indicate standard error of the mean. An error of 0 represents an accurate estimation of motion by a majority of the MT neurons while an error of 1 indicates that the majority winning MT neurons have wrong estimates of the direction of motion, measured in a region within three pixels of the edges of the moving bar in one frame of the motion.</p

The interconnections between neurons in V1 and MT.

<p>The interconnections represented by red (excitatory) and blue (inhibitory) arrows, respectively. Integration neurons receive inputs from both sets of complex and end-stopped cells in V1. They also receive inhibitory connections from segmentation cells. Segmentation cells receive excitatory input from complex cells and are inhibited by end-stopped cells. They also receive a conditional inhibitory connection from integration cells when the neurons in the receptive field center and surround are active. The connection parameters and variables are explained in the text and in Tables <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164813#pone.0164813.t001" target="_blank">1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164813#pone.0164813.t002" target="_blank">2</a>.</p

The constant parameters used in the model, their values, and their units.

<p>The constant parameters used in the model, their values, and their units.</p

The effect of dendritic field size on the frequency response of retinal ganglion cells.

<p>Panels (A–D) show the mean frequency response of A2, C2, D1 and D2 RGC types. Within each RGC type, cells are grouped and their frequency responses averaged according to their dendritic field diameter. Within each RGC type, cells with the largest dendritic fields are shown in black and those with the smallest dendritic fields are shown in grey. For comparison, the frequency response, averaged over all cells of a given type, irrespective of dendritic field size, is shown by the dashed line. Black dots indicate statistically significant differences between large-field and small-field RGC responses (t-tests, p < 0.05). Panels (E–H) show distributions of dendritic field diameter for each of the A2, C2, D1 and D2 RGC types, respectively.</p

Activity of Retinal Neurons Can Be Modulated by Tunable Near-Infrared Nanoparticle Sensors

The vision of patients rendered blind by photoreceptor degeneration can be partially restored by exogenous stimulation of surviving retinal ganglion cells (RGCs). Whereas conventional electrical stimulation techniques have failed to produce naturalistic visual percepts, nanoparticle-based optical sensors have recently received increasing attention as a means to artificially stimulate the RGCs. In particular, nanoparticle-enhanced infrared neural modulation (NINM) is a plasmonically mediated photothermal neuromodulation technique that has a demonstrated capacity for both stimulation and inhibition, which is essential for the differential modulation of ON-type and OFF-type RGCs. Gold nanorods provide tunable absorption through the near-infrared wavelength window, which reduces interference with any residual vision. Therefore, NINM may be uniquely well-suited to retinal prosthesis applications but, to our knowledge, has not previously been demonstrated in RGCs. In the present study, NINM laser pulses of 100 μs, 500 μs and 200 ms were applied to RGCs in explanted rat retinae, with single-cell responses recorded via patch-clamping. The shorter laser pulses evoked robust RGC stimulation by capacitive current generation, while the long laser pulses are capable of inhibiting spontaneous action potentials by thermal block. Importantly, an implicit bias toward OFF-type inhibition is observed, which may have important implications for the feasibility of future high-acuity retinal prosthesis design based on nanoparticle sensors

Results of estimation of the constant parameters in the model using the Genetic Algorithm (GA).

<p>Each line represents a different set of parameters. The optimization algorithm has successfully converged to the solution for different sets of parameters. The model is not very sensitive to small changes in the values of some parameters.</p

The activities of MT integration neurons when they do not receive input from end-stopped neurons, when the input stimulus is a bar moving to the right (plotted similarly to Fig 6).

<p>The activities of MT integration neurons when they do not receive input from end-stopped neurons, when the input stimulus is a bar moving to the right (plotted similarly to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0164813#pone.0164813.g006" target="_blank">Fig 6</a>).</p