This research is funded by The National Health and Medical Research Council Project Grant (Grant Number 1109056).

1. Setting up the finite element model for electric potential calculations
<ol>
	<li>Determine the simulation steps and the complexity of the model 
	NOTE: The aim of the first step is to clarify the purpose of the modeling, which will determine the necessary elements of the model and the simulation procedure. An important point to consider is the behavior of neural cells that needs to be shown by the model, and what testing protocol would be needed to demonstrate that behavior. This study shows a...

<ol>
	<li>Greenberg, R. J., Velte, T. J., Humayun, M. S., Scarlatis, G. N., de Juan, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+computational+model+of+electrical+stimulation+of+the+retinal+ganglion+cell.">A computational model of electrical stimulation of the retinal ganglion cell.</a> IEEE Transactions on Bio-medical Engineering. 46 (5), 505-514 (1999).</li><li>Guo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mediating+retinal+ganglion+cell+spike+rates+using+high-frequency+electrical+stimulation.">Mediating retinal ganglion cell spike rates using high-frequency electrical stimulation.</a> Frontiers in Neuroscience. 13, 413 (2019).</li><li>Loizos, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+electrical+stimulation+efficacy+in+degenerated+retina:+Stimulus+waveform+design+in+a+multiscale+computational+model.">Increasing electrical stimulation efficacy in degenerated retina: Stimulus waveform design in a multiscale computational model.</a> IEEE Transactions on Neural Systems and Rehabilitation Engineering. 26 (6), 1111-1120 (2018).</li><li>Cao, X., Sui, X., Lyu, Q., Li, L., Chai, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+different+three-dimensional+electrodes+on+epiretinal+electrical+stimulation+by+modeling+analysis.">Effects of different three-dimensional electrodes on epiretinal electrical stimulation by modeling analysis.</a> Journal of Neuroengineering and Rehabilitation. 12 (1), 73 (2015).</li><li>Wilke, R. 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COMSOL AB Available from: <a target="_blank" href="https://doc.comsol.com/5.4/doc/com.comsol.help.acdc/ACDCModuleUsersGuide.pdf">https://doc.comsol.com/5.4/doc/com.comsol.help.acdc/ACDCModuleUsersGuide.pdf</a> (2018)</li><li>Malmivuo, P., Malmivuo, J., Plonsey, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. , (1995).</li><li>Rall, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiology+of+a+dendritic+neuron+model.">Electrophysiology of a dendritic neuron model.</a> Biophysical Journal. 2, 145-167 (1962).</li><li>Carnevale, N. T., Hines, M. 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F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+quantitative+description+of+membrane+current+and+its+application+to+conduction+and+excitation+in+nerve.">A quantitative description of membrane current and its application to conduction and excitation in nerve.</a> The Journal of Physiology. 117 (4), 500-544 (1952).</li><li>Liang, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Threshold+suprachoroidal-transretinal+stimulation+current+required+by+different-size+electrodes+in+rabbit+eyes.">Threshold suprachoroidal-transretinal stimulation current required by different-size electrodes in rabbit eyes.</a> Ophthalmic Research. 45 (3), 113-121 (2011).</li><li>Jensen, R. J., Rizzo, J. F., Ziv, O. R., Grumet, A., Wyatt, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thresholds+for+activation+of+rabbit+retinal+ganglion+cells+with+an+ultrafine,+extracellular+microelectrode.">Thresholds for activation of rabbit retinal ganglion cells with an ultrafine, extracellular microelectrode.</a> Investigative Ophthalmology and Visual Science. 44 (8), 3533-3543 (2003).</li><li>Kim, W., Choi, M., Kim, S. -. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+normative+retinal+and+choroidal+thicknesses+of+the+rabbit+as+revealed+by+spectral+domain+optical+coherence+tomography.">The normative retinal and choroidal thicknesses of the rabbit as revealed by spectral domain optical coherence tomography.</a> Journal of the Korean Ophthalmological Society. 62 (3), 354-361 (2021).</li><li>Guo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+cell+morphology+in+a+computational+model+of+ON+and+OFF+retinal+ganglion+cells.">Influence of cell morphology in a computational model of ON and OFF retinal ganglion cells.</a> 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2013, 4553-4556 (2013).</li><li>Haberbosch, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Safety+aspects,+tolerability+and+modeling+of+retinofugal+alternating+current+stimulation.">Safety aspects, tolerability and modeling of retinofugal alternating current stimulation.</a> Frontiers in Neuroscience. 13, 783 (2019).</li><li>Sheasby, B. W., Fohlmeister, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impulse+encoding+across+the+dendritic+morphologies+of+retinal+ganglion+cells.">Impulse encoding across the dendritic morphologies of retinal ganglion cells.</a> Journal of Neurophysiology. 81 (4), 1685-1698 (1999).</li><li>Rockhill, R. L., Daly, F. J., MacNeil, M. A., Brown, S. P., Masland, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+diversity+of+ganglion+cells+in+a+mammalian+retina.">The diversity of ganglion cells in a mammalian retina.</a> Journal of Neuroscience. 22 (9), 3831-3843 (2002).</li><li>Lukasiewicz, P., Werblin, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+slowly+inactivating+potassium+current+truncates+spike+activity+in+ganglion+cells+of+the+tiger+salamander+retina.">A slowly inactivating potassium current truncates spike activity in ganglion cells of the tiger salamander retina.</a> The Journal of Neuroscience: The Official Journal of the Society for Neuroscience. 8 (12), 4470-4481 (1988).</li><li>Van Rossum, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Python Reference Manual. , (1995).</li><li>. Welcome to Spyder's Documentation - Spyder 5 documentation Available from: <a target="_blank" href="https://docs.spyder-idle.org/current/index.html">https://docs.spyder-idle.org/current/index.html</a> (2022)</li><li>Rattay, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ways+to+approximate+current-distance+relations+for+electrically+stimulated+fibers.">Ways to approximate current-distance relations for electrically stimulated fibers.</a> Journal of Theoretical Biology. 125 (3), 339-349 (1987).</li><li>Tsai, D., Morley, J. W., Suaning, G. J., Lovell, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Direct+activation+and+temporal+response+properties+of+rabbit+retinal+ganglion+cells+following+subretinal+stimulation.">Direct activation and temporal response properties of rabbit retinal ganglion cells following subretinal stimulation.</a> Journal of Neurophysiology. 102 (5), 2982-2993 (2009).</li><li>Tsai, D., Morley, J. W., Suaning, G. J., Lovell, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Frequency-dependent+reduction+of+voltage-gated+sodium+current+modulates+retinal+ganglion+cell+response+rate+to+electrical+stimulation.">Frequency-dependent reduction of voltage-gated sodium current modulates retinal ganglion cell response rate to electrical stimulation.</a> Journal of Neural Engineering. 8 (6), 066007 (2011).</li><li>Joucla, S., Gli&#232;re, A., Yvert, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+approaches+to+model+extracellular+electrical+neural+microstimulation.">Current approaches to model extracellular electrical neural microstimulation.</a> Frontiers in Computational Neuroscience. 8, 13 (2014).</li><li>. OpenFOAM Available from: <a target="_blank" href="https://www.openfoam.com/">https://www.openfoam.com/</a> (2022)</li><li>Barba, L., Forsyth, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CFD+Python:+The+12+steps+to+Navier-Stokes+equations.">CFD Python: The 12 steps to Navier-Stokes equations.</a> Journal of Open Source Education. 1 (9), 21 (2018).</li></ol>

In this paper, we have demonstrated a modeling workflow that combined finite element and biophysical neuron modeling. The model is highly flexible, as it can be modified in its complexity to fit different purposes, and it provides a way to validate the results against empirical findings. We also demonstrated how we parameterized the model to enable automation.
The two-step modeling method combines the advantages of using FEM and neuron computational suite to solve the neuron's cable equation i...

Visual neuroprostheses are a group of devices that deliver stimulations (electrical, light, etc.) to the neural cells in the visual pathway to create phosphenes or sensation of seeing the light. It is a treatment strategy that has been in clinical use for almost a decade for people with permanent blindness caused by degenerative retinal diseases. Typically, a complete system would include an external camera that captures the visual information around the user, a power supply and computing unit to process and translate the image to a series of electrical pulses, and an implanted electrode array that interfaces the neural tissue and deliver the electrical pulses to the neural cells. The working principle allows a visual neuroprosthesis to be placed in different sites along the visual pathway from the retina to the visual cortex, as long as it is downstream from the damaged tissue. A majority of current research in visual neuroprostheses focuses on increasing the efficacy of the stimulation and improving the spatial acuity to provide a more natural vision.
In the efforts to improve the efficacy of the stimulation, computational modeling has been a cost- and time-effective method to validate a prosthesis design and simulate its visual outcome. Computational modeling in this field gained popularity since 1999 as Greenberg1 modeled the response of a retinal ganglion cell to extracellular electrical stimuli. Since then, computational modeling has been used to optimize the parameters of the electrical pulse2,3 or the geometrical design of the electrode4,5. Despite the variation in complexity and research questions, these models work by determining the electrical voltage distribution in the medium (e.g., neural tissue) and estimating the electrical response that the neurons in the vicinity will produce due to the electrical voltage.
The electrical voltage distribution in a conductor can be found by solving the Poisson equations6 at all locations:
<img alt="Equation 1" src="/files/ftp_upload/63792/63792eq01.jpg" style="margin: auto;" />
<img alt="Equation 2" src="/files/ftp_upload/63792/63792eq02.jpg" style="margin: auto;" />
where E is the electric field, V the electric potential, J the current density, and&#160;&#963; is the electrical conductivity. The <img alt="Equation 12" src="/files/ftp_upload/63792/63792eq12.jpg" style="margin: auto;" /> in the equation indicates a gradient operator. In the case of stationary current, the following boundary conditions are imposed on the model:
<img alt="Equation 3" src="/files/ftp_upload/63792/63792eq3.jpg" style="margin: auto;" />
<img alt="Equation 4" src="/files/ftp_upload/63792/63792eq04.jpg" style="margin: auto;" />
where n is the normal to the surface, &#937; represents the boundary, and I0 represents the specific current. Together, they create electrical insulation at the external boundaries and create a current source for a selected boundary. If we assume a monopolar point source in a homogeneous medium with an isotropic conductivity, the extracellular electric potential at an arbitrary location can be calculated by7:
<img alt="Equation 5" src="/files/ftp_upload/63792/63792eq05.jpg" style="margin: auto;" />
where Ie&#160;is the current and is the distance between the electrode and the point of measurement. When the medium is inhomogeneous or anisotropic, or the electrode array has multiple electrodes, a computational suite to numerically solve the equations can be convenient. A finite-element modeling software6 breaks up the volume conductor into small sections known as &#39;elements&#39;. The elements are interconnected with each other such that the effects of change in one element influence change in others, and it solves the physical equations that serve to describe these elements. With the increasing computational speed of modern computers, this process can be completed within seconds. Once the electric potential is calculated, one can then estimate the electrical response of the neuron.
A neuron sends and receives information in the form of electrical signals. Such signals come in two forms - graded potentials and action potentials. Graded potentials are temporary changes to the membrane potential wherein the voltage across the membrane becomes more positive (depolarization) or negative (hyperpolarization). Graded potentials typically have localized effects. In cells that produce them, action potentials are all-or-nothing responses that can travel long distances along the length of an axon. Both graded and action potentials are sensitive to the electrical as well as the chemical environment. An action potential spike can be produced by various neuronal cell types, including the retinal ganglion cells, when a threshold transmembrane potential is crossed. The action potential spiking and propagation then trigger synaptic transmission of signals to downstream neurons. A neuron can be modeled as a cable that is divided into cylindrical segments, where each segment has capacitance and resistance due to the lipid bilayer membrane8. A neuron computational programme9 can estimate the electrical activity of an electrically-excitable cell by discretizing the cell into multiple compartments and solving the mathematical model10:
<img alt="Equation 6" src="/files/ftp_upload/63792/63792eq06.jpg" style="margin: auto;" />
In this equation, Cm is the membrane capacitance, Ve,n is the extracellular potential at node n, Vi,n the intracellular potential at node n, Rn the intracellular (longitudinal) resistance at node n, and Iion is the ionic current going through the ion channels at node n. The values of V from the FEM model are implemented as Ve,n for all nodes in the neuron when the stimulation is active.
The transmembrane currents from ion channels can be modeled by using Hodgkin-Huxley formulations11:
<img alt="Equation 7" src="/files/ftp_upload/63792/63792eq07.jpg" style="margin: auto;" />
where gi is the specific conductance of the channel, Vm the transmembrane potential (Vi,n - Ve,n) and Eion the reversal potential of the ion channel. For voltage-gated channels, such as Na channel, dimensionless parameters, m, and h, that describe the probability of opening or closing of the channels are introduced:
<img alt="Equation 8" src="/files/ftp_upload/63792/63792eq08.jpg" style="margin: auto;" />
where <img alt="Equation 9" src="/files/ftp_upload/63792/63792eq09.jpg" style="margin: auto;" /> is the maximum membrane conductance for the particular ion channel, and the values of parameters m and h are defined by differential equations:
<img alt="Equation 10" src="/files/ftp_upload/63792/63792eq10.jpg" style="margin: auto;" />
where &#945;x and&#160;&#946;x are voltage-dependent functions that define the rate constants of the ion channel. They generally take the form:
<img alt="Equation 11" src="/files/ftp_upload/63792/63792eq11v2.jpg" style="margin: auto;" />
The values of the parameters in these equations, including maximal conductance, as well as the constants A, B, C, and D, were typically found from empirical measurements.
With these building blocks, models of different complexities can be built by following the steps described. An FEM software is useful when the Poisson equation cannot be solved analytically, such as in the case of inhomogeneous or anisotropic conductance in the volume conductor or when the geometry of the electrode array is complex. After the extracellular potential values have been solved, the neuron cable model can then be numerically solved in the neuron computational software. Combining the two software enables the computation of a complex neuron cell or network to a non-uniform electric field.
A simple two-step model of a retinal ganglion cell under a suprachoroidal stimulation will be built using the aforementioned programs. In this study, the retinal ganglion cell will be subjected to a range of magnitudes of electrical current pulses. The location of the cell relative to the stimulus is also varied to show the distance-threshold relationship. Moreover, the study includes a validation of the computational result against an in vivo study of the cortical activation threshold using different sizes of stimulation electrode12, as well as an in vitro study showing the relationship between the electrode-neuron distance and the activation threshold13.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Computer workstation</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>Windows 64-bit operating system, at least 4GB of RAM, at least 3 GB of disk space</td>
		</tr>
		<tr>
			<td>Anaconda Python</td>
			<td>Anaconda Inc.</td>
			<td>Version 3.9</td>
			<td>The open source Individual Edition containing Python 3.9 and preinstalled packages to perform data manipulation, as well as Spyder Integrated Development Environment. It could be used to control the simulation, as well as to display and analyse the simulation data.</td>
		</tr>
		<tr>
			<td>COMSOL Multiphysics</td>
			<td>COMSOL</td>
			<td>Version 5.6</td>
			<td>The simulation suite to perform finite element modelling. The licence for the AC/DC module should be purchased. The Application Builder capability should be included in the licence to follow the automation tutorial.</td>
		</tr>
		<tr>
			<td>NEURON</td>
			<td>NEURON</td>
			<td>Version 8.0</td>
			<td>A freely-distributed software to perform the computation of neuronal cells and/or neural networks.</td>
		</tr>
	</tbody>
</table>

computational modeling of retinal neurons for visual prosthesis research - fundamental approaches

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro systems. This has the key advantage of minimizing the number of animals required in a given study by providing an often very precise prediction of physiological outcomes. In the field of visual prosthesis, computational modeling has an array of practical applications, including informing the design of an implantable electrode array and prediction of visual percepts that may be elicited through the delivery of electrical impulses from the said array. Some models described in the literature combine a three-dimensional (3D) morphology to compute the electric field and a cable model of the neuron or neural network of interest. To increase the accessibility of this two-step method to researchers who may have limited prior experience in computational modeling, we provide a video of the fundamental approaches to be taken in order to construct a computational model and utilize it in predicting the physiological and psychophysical outcomes of stimulation protocols deployed via a visual prosthesis. The guide comprises the steps to build a 3D model in a finite element modeling (FEM) software, the construction of a retinal ganglion cell model in a multi-compartmental neuron computational software, followed by the amalgamation of the two. A finite element modeling software to numerically solve physical equations would be used to solve electric field distribution in the electrical stimulations of tissue. Then, specialized software to simulate the electrical activities of a neural cell or network was used. To follow this tutorial, familiarity with the working principle of a neuroprosthesis, as well as neurophysiological concepts (e.g., action potential mechanism and an understanding of the Hodgkin-Huxley model), would be required.

We conducted two simulation protocols to demonstrate the use of the model. The first protocol involved varying the electrode size while keeping the location of the neuron and the electrical pulse parameters the same. The second protocol involved shifting the neuron in the x-direction in 100 &#181;m steps, while the size of the electrode remained constant. For both protocols, the pulse used was a single cathodic-first biphasic pulse of 0.25 ms width with a 0.05 ms interphase gap. For the first protocol, the radius of the ...

Watch this Scientific Journal Video about Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches at JoVE.com

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

We summarize a workflow to computationally model a retinal neuron's behaviors in response to electrical stimulation. The computational model is versatile and includes automation steps that are useful in simulating a range of physiological scenarios and anticipating the outcomes of future in vivo/in vitro studies.

Computational modeling has become an increasingly important method in neural engineering due to its capacity to predict behaviors of in vivo and in vitro ...

This protocol helps predict the response of neural cells to different stimuli, allowing us to explore novel ideas in neural stimulation without needing physical prototype or life tissue. It can be used as a preliminary study to test concepts that are challenging to emulate physically, and it is cost and time-efficient, enabling testing of large numbers of parameters. This method can predict neural response in other neural stimulation systems besides the retina.It is also not limited to electrical stimulations, but can be used for light stimuli as well. To begin, run the FEM software and click on model wizard, then 3D. In the select physics list box, expand AC/DC and select electric fields and current.Click on study and add a stationary study under the general studies option, and then click on done. In the geometry settings, change the unit of length from meter to micrometer. Right-click on geometry one, then click block to create a block domain.Repeat this step two more times to create three blocks in total. For all blocks, set both the depth and width to 5, 000 micrometers and assign the height value for the block. Change the base option to center and assign Z-values for each block.To create a work plane for adding an electrode to the model, right-click on the geometry one in the model tree and choose work plane. Click on work plane one and change the plane type to face parallel. Click on the activate selection button below the plane type and choose the bottom surface of block one.Click on parameters one and define the value for the radius of the electrode. To draw a disk electrode on the work plane, click on plane geometry under work plane one and click on sketch in the main toolbar. Select circle, click anywhere inside the rectangle in the graphics tab, and drag to create a disk electrode.Change the radius to a predefined value in micrometer, xw and yw to zero micrometer, and then click on build all. In the model tree, right-click on material, then click blank material, and then click on material one and change the selection to manual. Click on the domains in the graphics window so that only domain one is chosen.Choose material properties, basic properties, then click electrical conductivity and click on the add to material button. Change the electrical conductivity value to 0.043 siemens per meter. Repeat the steps for domains two and three with the electrical conductivity values of 0.7 and 1.55 siemens per meter, respectively.To mesh a 3D model, go to the model tree and right-click on mesh one, then click free tetrahedral. Click on free tetrahedral one and choose build all. To apply physics to FEM, expand electric currents one in model tree and check whether current conservation one, electric insulation one, and initial values one are listed.Then right-click on the electric currents. Then click ground and apply this to the surface furthest from the electrode. Next, right-click on the electric currents.Then click floating potential assigned to the disk electrode and change the I0 value to one microampere to apply a unitary current. To run the simulation with a parametric sweep, in the model tree, right-click on study one, then click parametric sweep. Click on parametric sweep, and in the study setting table, click on add, and then choose elec_rad for the parameter name.Type 50, 150, 350, 500 for the parameter value list and micrometer for the parameter unit. Then click on compute to run the study. To import the morphology using the CellBuilder feature, run nrngui from the NEURON Computational Suite's installation folder.Next, click on tools, then click miscellaneous, import 3D, and then tick the choose a file box. Locate the downloaded SWC file and click on read. Once the geometry has been imported, click on export, then CellBuilder.To create a HOC file of the imported cell morphology, go to the subsets tab and observe the subsets that have been predefined in the model. Tick the continuous create box, go to management, then click export and export the morphology as rgc.hoc. To view the morphology of the cell, click on tool, model view, one real cells.Then click root soma zero on the toolbar. Right-click on the appearing window and click on access type and view access. By visual inspection, this model's dendritic field diameter should be around 250 micrometers.Close the NEURON windows for now. Open the FEM software. Go to the Application Builder.Right-click on methods in the Application Builder tree. Choose new method and click on okay. Go to file, then click preferences and methods.Tick the view all codes box and click on okay. Write the HOC file that loads the coordinates of the neurons'segments to a text file. Use the FEM method script to shift the values to match the desired location and save a text file containing the coordinate values for the new location of the cell.Open the COMSOL method and save the shifted coordinate and voltage values. To run the automated steps in the FEM software, switch to the model builder, developer, run method, and click method one. This will produce DAT files with the appropriate voltage values.Loop the simulations in a general-purpose programming language by opening the chosen IDE and click on new file to make a new script, as determined in the text manuscript. Finally, click on run or press F5 to run the script, which will also open the NEURON Computational Suite's GUI. Graph the response of the NEURON model to the extracellular stimulation in the NEURON Computational Suite's GUI.To do this, run stimulation. hoc, click on graph, then click voltage access from the toolbar, and on the graph window, right-click anywhere and choose, plot what? Type in axon.v1 in the variable to graph field, which means that it will plot the transmembrane potential of the last segment of the axon per time step. The model described here confirmed that increasing the suprachoroidal electrode size at a 0.25 millisecond pulse width increased the activation threshold of the model neuron. The action potential characteristics were observed to validate the model.The latency or the time between the stimulus onset and the peak of the action potential spike ranged from 1 to 2.2 milliseconds. This corresponded to the short latency spiking due to non-network-mediated retinal activation. The spike width of this model was one millisecond and this is in the same range as the spike widths of rabbit RGCs measured in vitro.The model showed the lowest threshold when the narrow segment of the axon was immediately above the disk electrode and it increased as the X distance became larger. Moving the electrode further toward the distal axon produced a lower threshold as compared to moving the electrode toward the dendrites due to the presence of the axon initial segment and the narrow segment where the sodium channels are more prevalent. The described method is easy to apply and it accelerates researchers in producing proofs of concept for novel stimulation methods or neuro-electro designs.

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JoVE Journal

Neuroscience

1.5K visualizações.  The University of Sydney. Este protocolo ajuda a prever a resposta das células neurais a diferentes estímulos, permitindo-nos explorar novas ideias em estimulação neural sem a necessidade de protótipo físico ou tecido vital. Ele pode ser usado como um estudo preliminar para testar conceitos que são difíceis de emular fisicamente, e é eficiente em termos de custo e tempo, permitindo o teste de um grande número de parâmetros. Este método pode prever a resposta neural em outros sistemas de estimulação neura...