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Objective. Retinal prostheses seek to create artificial vision by stimulating surviving retinal neurons of patients with profound vision impairment. Notwithstanding tremendous research efforts, the performance of all implants tested to date has remained rudimentary, incapable of overcoming the threshold for legal blindness. To maximize the perceptual efficacy of retinal prostheses, a device must be capable of controlling retinal neurons with greater spatiotemporal precision. Most studies of retinal stimulation were derived from either non-primate species or the peripheral primate retina. We investigated if artificial stimulation could leverage the high spatial resolution afforded by the neural substrates at the primate fovea and surrounding regions to achieve improved percept qualities. Approach. We began by developing a new computational model capable of generating anatomically accurate retinal ganglion cell (RGC) populations within the human central retina. Next, multiple RGC populations across the central retina were stimulated in-silico to compare clinical and recently proposed neurostimulation configurations based on their ability to improve perceptual efficacy and reduce activation thresholds. Main results. Our model uniquely upholds eccentricity-dependent characteristics such as RGC density and dendritic field diameter, whilst incorporating anatomically accurate features such as axon projection and three-dimensional (3D) RGC layering, features often forgone in favor of reduced computational complexity. Following epiretinal stimulation, the RGCs in our model produced response patterns in shapes akin to the complex and non-trivial percepts reported in clinical trials. Our results also demonstrated that even within the neuron-dense central retina, epiretinal stimulation using a multi-return hexapolar electrode arrangement could reliably achieve spatially focused RGC activation and could achieve single-cell excitation in 56% of all tested locations. Significance. This study establishes an anatomically accurate 3D model of RGC populations within the human central retina and demonstrates the potential for an epiretinal hexapolar configuration to achieve consistent, spatially confined retinal responses, even within the unique and neuron-dense foveal region. Our results and model promote the prospect and optimization of higher spatial resolution in future epiretinal implants.

Objective. A major reason for poor visual outcomes provided by existing retinal prostheses is the limited knowledge of the impact of photoreceptor loss on retinal remodelling and its subsequent impact on neural responses to electrical stimulation. Computational network models of the neural retina assist in the understanding of normal retinal function but can be also useful for investigating diseased retinal responses to electrical stimulation. Approach. We developed and validated a biophysically detailed discrete neuronal network model of the retina in the software package NEURON. The model includes rod and cone photoreceptors, ON and OFF bipolar cell pathways, amacrine and horizontal cells and finally, ON and OFF retinal ganglion cells with detailed network connectivity and neural intrinsic properties. By accurately controlling the network parameters, we simulated the impact of varying levels of degeneration on retinal electrical function. Main results. Our model was able to reproduce characteristic monophasic and biphasic oscillatory patterns seen in ON and OFF neurons during retinal degeneration (RD). Oscillatory activity occurred at 3 Hz with partial photoreceptor loss and at 6 Hz when all photoreceptor input to the retina was removed. Oscillations were found to gradually weaken, then disappear when synapses and gap junctions were destroyed in the inner retina. Without requiring any changes to intrinsic cellular properties of individual inner retinal neurons, our results suggest that changes in connectivity alone were sufficient to give rise to neural oscillations during photoreceptor degeneration, and significant network connectivity destruction in the inner retina terminated the oscillations. Significance. Our results provide a platform for further understanding physiological retinal changes with progressive photoreceptor and inner RD. Furthermore, our model can be used to guide future stimulation strategies for retinal prostheses to benefit patients at different stages of disease progression, particularly in the early and mid-stages of RD.

Background:Electroconvulsive therapy (ECT) is a highly effective treatment for severe psychiatric disorders. Despite its high efficacy, the use of ECT would be greater if the risk of cognitive side effects were reduced. Over the last 20 years, developments in ECT technique, including improvements in the dosing methodology and modification of the stimulus waveform, have allowed for improved treatment methods with reduced adverse cognitive effects. There is increasing evidence that the electrode placement is important for orienting the electrical stimulus and therefore modifying treatment outcomes, with potential for further improvement of the placements currently used in ECT.Objective:We used computational modelling to perform an in-depth examination into regional differences in brain excitation by the ECT stimulus for several lesser known and novel electrode placements, in order to investigate the potential for an electrode placement that may optimise clinical outcomes.Methods:High resolution finite element human head models were generated from MRI scans of three subjects. The models were used to compare regional differences in average electric field (EF) magnitude among a total of thirteen bipolar ECT electrode placements, i.e. three conventional placements as well as ten lesser known and novel placements.Results and conclusion:In this exploratory study on a systemic comparison of thirteen ECT electrode placements, the EF magnitude at regions of interest (ROIs) was highly dependent upon the position of both electrodes, especially the ROIs close to the cortical surface. Compared to conventional right-unilateral (RUL) ECT using a temporo-parietal placement, fronto-parietal and supraorbito-parietal RUL also robustly stimulated brain regions considered important for efficacy, while sparing regions related to cognitive functions, and may be a preferrable approach to the currently used placement for RUL ECT. The simulations also found that regional average EF magnitude varied between individual subjects, due to factors such as head size, and results also depended on the size of the defined ROI.

Present retinal neuroprostheses have limited performance capabilities due to indiscriminate activation of different neural pathways. Based on our success in differentially activating ON and OFF cells using high frequency stimuli in a healthy retina, in this study we explored whether we could achieve similar differential activation between these two cell types but in degenerate retina. We found that after blocking the synaptic network, ON retinal ganglion cells (RGCs) could be differentially activated at higher frequencies (4 - 6 kHz) and amplitudes (200 - 240 µA), and OFF RGCs at relatively lower amplitudes (80 - 160 µA) across all tested frequencies. We further found that both cell types could be controlled by quickly modulating the frequency using short stimulation bursts. This work takes us one step closer to reducing the likelihood of indiscriminate activation of RGCs by accurately controlling the activation of functionally-distinct neural pathways.

Retinal prostheses face with the problem of low resolution in clinical trial. Current steering is an effective way to produce extra perceptual channels called virtual electrodes (VEs) and therefore improve the perceptual resolution, without increasing the number of stimulating electrodes. However, little research has been reported about applying this method to subretinal prostheses. In this study, multilayer models of normal human retina and degenerated human retina with retinitis pigmentosa (RP) were established. The configuration and parameter of stimulating electrodes were studied for generating focal VEs. Our stimulated results showed that distant sclera is a suitable site set as ground compared to distant vitreous body. Simultaneous stimulating of adjacent electrodes can generate focal VEs in the target retinal layer that subretinal prostheses prefer to stimulating, when parameters of the stimulating electrode (diameter and edge-to-edge distance) were selected appropriately. This study may provide theoretical support for future application of VEs in subretinal prostheses.

Retinal electrostimulation is promising a successful therapy to restore functional vision. However, a narrow stimulating current range exists between retinal neuron excitation and inhibition which may lead to misperformance of visual prostheses. As the conveyance of representation of complex visual scenes may require neighbouring electrodes to be activated simultaneously, electric field summation may contribute to reach this inhibitory threshold. This study used three approaches to assess the implications of relatively high stimulating conditions in visual prostheses: (1) in vivo, using a suprachoroidal prosthesis implanted in a feline model, (2) in vitro through electrostimulation of murine retinal preparations, and (3) in silico by computing the response of a population of retinal ganglion cells. Inhibitory stimulating conditions led to diminished cortical activity in the cat. Stimulus-response relationships showed non-monotonic profiles to increasing stimulating current. This was observed in vitro and in silico as the combined response of groups of neurons (close to the stimulating electrode) being inhibited at certain stimulating amplitudes, whilst other groups (far from the stimulating electrode) being recruited. These findings may explain the halo-like phosphene shapes reported in clinical trials and suggest that simultaneous stimulation in retinal prostheses is limited by the inhibitory threshold of the retinal ganglion cells.

The vertebrate retina is a clearly organized signal-processing system. It contains more than 60 different types of neurons, arranged in three distinct neural layers. Each cell type is believed to serve unique role(s) in encoding visual information. While we now have a relatively good understanding of the constituent cell types in the retina and some general ideas of their connectivity, with few exceptions, how the retinal circuitry performs computation remains poorly understood. Computational modeling has been commonly used to study the retina from the single cell to the network level. In this article, we begin by reviewing retinal modeling strategies and existing models. We then discuss in detail the significance and limitations of these models, and finally, we provide suggestions for the future development of retinal neural modeling.

Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. These cell-specific properties are responsible for the unique way they process synaptic inputs. A cell-specific modeling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. ON and OFF RGC models based on accurate biophysics and realistic representation of morphologies were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC electrical activity. Using this approach, morphologically-complex retinal neurons such as amacrine cells or RGCs can be modelled and their interactions and processing can be better understood.

In this study, ON and OFF retinal ganglion cell (RGC) models based on accurate biophysics and realistic representations of cell morphologies were used to understand how these cells selectively respond to high-frequency electrical stimulation (HFS). With optimized model parameters and the incorporation of detailed cell morphologies, these two models were able to closely replicate experimental ON and OFF RGC responses to epiretinal electrical stimulation. This modeling approach can be used to design electrical stimulus profiles capable of cell-specific activation, and is broadly applicable for the development of sophisticated stimulation strategies for visual prostheses.

Variations in ionic channel expression and anatomical properties can influence how different retinal ganglion cell (RGC) types process synaptic information. Computational modeling approaches allow us to precisely control these biophysical and physical properties and isolate their effects in shaping RGC firing patterns. In this study, three models based on realistic representations of RGC morphologies were used to simulate the contribution of spatial structure of neurons and membrane ion channel properties to RGC electrical activity. In all simulations, the RGC models shared common ionic channel kinetics, differing only in their regional ionic channel distributions and cell morphology.

A 3D model of atrial electrical activity has been developed with spatially heterogeneous electrophysiological properties. The atrial geometry, reconstructed from the male Visible Human dataset, included gross anatomical features such as the central and peripheral sinoatrial node (SAN), intra-atrial connections, pulmonary veins, inferior and superior vena cava, and the coronary sinus. Membrane potentials of myocytes from spontaneously active or electrically pacedin vitrorabbit cardiac tissue preparations were recorded using intracellular glass microelectrodes. Action potentials of central and peripheral SAN, right and left atrial, and pulmonary vein myocytes were each fitted using a generic ionic model having three phenomenological ionic current components: one time-dependent inward, one time-dependent outward, and one leakage current. To bridge the gap between the single-cell ionic models and the gross electrical behaviour of the 3D whole-atrial model, a simplified 2D tissue disc with heterogeneous regions was optimised to arrive at parameters for each cell type under electrotonic load. Parameters were then incorporated into the 3D atrial model, which as a result exhibited a spontaneously active SAN able to rhythmically excite the atria. The tissue-based optimisation of ionic models and the modelling process outlined are generic and applicable to image-based computer reconstruction and simulation of excitable tissue.

Objective Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. Cell-specific properties are responsible for the unique way RGCs process synaptic inputs, as well as artificial electrical signals such as that from a visual prosthesis. A cell-specific computational modelling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology. Approach In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. Main results With well-defined cell-specific model parameters and the incorporation of detailed cell morphologies, these models were able to closely reconstruct and predict ON and OFF RGC response properties recorded experimentally. Significance The resulting models were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC activation. The techniques of this study are generally applicable to other excitable cell models, increasing the utility of theoretical models in accurately predicting the response of real biological neurons.

Generic ionic models optimized to replicate experimentally recorded cardiac action potentials (APs) from the central and peripheral sinoatrial node (SAN), the natural pacemaker of the heart, as well as atrial intact-myocytes are implemented in a realistic 2D model of rabbit SAN geometry. The model was used to investigate two frequently-proposed modes of SAN architecture: the gradient and mosaic hypotheses. In a simplified gradient arrangement, the peripheral SAN region acts as a transition zone between the central SAN and atrium and is required for spontaneous rhythmic initiation of APs from central SAN into the atria. Furthermore, the application of optimized single cell parameters to the realistic 2D rabbit geometry did not accurately replicate experimentally recorded APs. On the other hand, in an adapted mosaic geometry, peripheral SAN cells were not required to produce spontaneous regular excitation.

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

A three-dimensional anatomically and electro-physiologically realistic model of atrial propagation is developed utilizing generic cardiac ionic models fitted to experimentally recorded action potentials (APs). The atrial geometry incorporated realistic wall thickness and twelve anatomical structures, including the sino-atrial node (SAN), pulmonary veins, interatrial septum, Bachmann's bundle and coronary sinus as interatrial conduction pathways. The SAN was further subdivided into central and peripheral regions, characterized by different ionic parameters. These parameters were obtained by optimizing ionic models to fit spontaneous APs recorded intracellularly from intact rabbit in vitro sino-atrial tissue preparations. The SAN region in the 3D model was able to initiate spontaneous rhythmic APs and excite the surrounding atrium. The pattern of atrial activation was similar to that observed in humans. The use of model optimization allows direct incorporation of experimental data into anatomically realistic geometries and is a step towards developing patient-specific models for the treatment of atrial arrhythmias.

A multiple dataset model fitting approach for improving parameter reliability in action potential modeling is presented. A robust generic cardiac ionic model employing membrane currents based on two-gate Hodgkin-Huxley kinetics is described. Its generic nature allows it to accurately reproduce action potential waveforms in heterogeneous cardiac tissue by optimizing parameters governing ion channel kinetics and magnitudes. The model allows a user-defined number of voltage and time-dependent ion currents to be incorporated, in order to reproduce and predict multiple action potential waveforms recorded in intact cardiac myocyte. In total 12 N(c)+2 parameters were optimized using a curvilinear gradient method, where N(c) is the user-specified number of time-dependent currents. Given appropriate experimental datasets, many of the known physiological membrane currents could be effectively reconstructed. Also, the optimized models were able to predict additional experimental action potential recordings that were not used in the optimization process.