Biomedical Engineering - Research Publications
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ItemNo Preview AvailableEffect of sparsity on network stability in random neural networks obeying Dale's law(American Physical Society, 2023-10-01)This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we generalize classical eigenspectral results to sparse (not fully connected) connectivity matrices obeying Dale's law: neurons function as either excitatory (E) or inhibitory (I). By defining α as the probability that a neuron is connected to another neuron, we give explicit formulas that show how sparsity interacts with the E-I population statistics to scale key features of the eigenspectrum in both the balanced and unbalanced cases. Our results show that the eigenspectral outlier is linearly scaled by α, but the eigenspectral radius and density now depend on a nonlinear interaction between α and the E-I population means and variances. Contrary to previous results, we demonstrate that a nonuniform eigenspectral density results if any of the E-I population statistics differ, not just the variances. We also find that local eigenvalue outliers are present for sparse random matrices obeying Dale's law, and demonstrate that these eigenvalues can be controlled by a modified zero row-sum constraint for the balanced case, however, they persist in the unbalanced case. We examine all levels of connection sparsity 0≤α≤1 and distributed E-I population weights to describe a general class of sparse connectivity structures which unifies all the previous results as special cases of our framework. Sparsity and Dale's law are both fundamental anatomical properties of biological neural networks. We generalize their combined effects on the eigenspectrum of random neural networks, thereby gaining insight into network stability, state transitions, and the structure-function relationship.
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ItemQuantifying visual acuity for pre-clinical testing of visual prostheses(IOP Publishing Ltd, 2023-02-01)Objective.Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.Approach.This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized 'C' and 'E' optotypes.Main results.The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.Significance.Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.
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ItemNo Preview AvailablePreferential modulation of individual retinal ganglion cells by electrical stimulation(IOP Publishing Ltd, 2022-08-01)Objective.Retinal prostheses have had limited success in vision restoration through electrical stimulation of surviving retinal ganglion cells (RGCs) in the degenerated retina. This is partly due to non-preferential stimulation of all RGCs near a single stimulating electrode, which include cells that conflict in their response properties and their contribution to visiual processing. Our study proposes a stimulation strategy to preferentially stimulate individual RGCs based on their temporal electrical receptive fields (tERFs).Approach.We recorded the responses of RGCs using whole-cell patch clamping and demonstrated the stimulation strategy, first using intracellular stimulation, then via extracellular stimulation.Main results. We successfully reconstructed the tERFs according to the RGC response to Gaussian white noise current stimulation. The characteristics of the tERFs were extracted and compared based on the morphological and light response types of the cells. By re-delivering stimulation trains that were composed of the tERFs obtained from different cells, we could preferentially stimulate individual RGCs as the cells showed lower activation thresholds to their own tERFs.Significance.This proposed stimulation strategy implemented in the next generation of recording and stimulating retinal prostheses may improve the quality of artificial vision.
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ItemNeural activity shaping utilizing a partitioned target pattern(IOP PUBLISHING LTD, 2021-08)Electrical stimulation of neural tissue is used in both clinical and experimental devices to evoke a desired spatiotemporal pattern of neural activity. These devices induce a local field that drives neural activation, referred to as an activating function or generator signal. In visual prostheses, the spread of generator signal from each electrode within the neural tissue results in a spread of visual perception, referred to as a phosphene.Objective.In cases where neighbouring phosphenes overlap, it is desirable to use current steering or neural activity shaping strategies to manipulate the generator signal between the electrodes to provide greater control over the total pattern of neural activity. Applying opposite generator signal polarities in neighbouring regions of the retina forces the generator signal to pass through zero at an intermediate point, thus inducing low neural activity that may be perceived as a high-contrast line. This approach provides a form of high contrast visual perception, but it requires partitioning of the target pattern into those regions that use positive or negative generator signals. This discrete optimization is an NP-hard problem that is subject to being trapped in detrimental local minima.Approach.This investigation proposes a new partitioning method using image segmentation to determine the most beneficial positive and negative generator signal regions. Utilizing a database of 1000 natural images, the method is compared to alternative approaches based upon the mean squared error of the outcome.Main results.Under nominal conditions and with a set computation limit, partitioning provided improvement for 32% of these images. This percentage increased to 89% when utilizing image pre-processing to emphasize perceptual features of the images. The percentage of images that were dealt with most effectively with image segmentation increased as lower computation limits were imposed on the algorithms.Significance.These results provide a new method to increase the resolution of neural stimulating arrays and thus improve the experience of visual prosthesis users.
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ItemLearning receptive field properties of complex cells in V1(PUBLIC LIBRARY SCIENCE, 2021-03)There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.