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Primary open-angle glaucoma (POAG) is an optic neuropathy characterized by irreversible retinal ganglion cell damage and visual field loss. The global POAG prevalence is estimated to be 3.05%, and near term is expected to significantly rise, especially within aging Asian populations. Primary angle-closure glaucoma disproportionately affects Asians, with up to four times greater prevalence of normal-tension glaucoma reported compared with high-tension glaucoma. Estimates for overall POAG prevalence in Asian populations vary, with Chinese and Indian populations representing the majority of future cases. Structural characteristics associated with glaucoma progression including the optic nerve head, retina, and cornea are distinct in Asians, serving as intermediates between African and European descent populations. Patterns in IOP suggest some similarities between races, with a significant inverse relationship between age and IOP only in Asian populations. Genetic differences have been suggested to play a role in these differences, however, a clear genetic pattern is yet to be established. POAG pathogenesis differs between Asians and other ethnicities, and it may differ within the broad classification of the Asian race. Greater awareness and further research are needed to improve treatment plans and outcomes for the increasingly high prevalence of normal tension glaucoma within aging Asian populations.
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Sparse Coding Enables the Reconstruction of High-Fidelity Images and Video from Retinal Spike Trains
The optic nerve transmits visual information to the brain as trains of discrete events, a low-power, low-bandwidth communication channel also exploited by silicon retina cameras. Extracting highfidelity visual input from retinal event trains is thus a key challenge for both computational neuroscience and neuromorphic engineering. Here, we investigate whether sparse coding can enable the reconstruction of high-fidelity images and video from retinal event trains. Our approach is analogous to compressive sensing, in which only a random subset of pixels are transmitted and the missing information is estimated via inference. We employed a variant of the Locally Competitive Algorithm to infer sparse representations from retinal event trains, using a dictionary of convolutional features optimized via stochastic gradient descent and trained in an unsupervised manner using a local Hebbian learning rule with momentum. We used an anatomically realistic retinal model with stochastic graded release from cones and bipolar cells to encode thumbnail images as spike trains arising from ON and OFF retinal ganglion cells. The spikes from each model ganglion cell were summed over a 32 msec time window, yielding a noisy rate-coded image. Analogous to how the primary visual cortex is postulated to infer features from noisy spike trains arising from the optic nerve, we inferred a higher-fidelity sparse reconstruction from the noisy rate-coded image using a convolutional dictionary trained on the original CIFAR10 database. To investigate whether a similar approachworks on non-stochastic data, we demonstrate that the same procedure can be used to reconstruct high-frequency video from the asynchronous events arising from a silicon retina camera moving through a laboratory environment.
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- Award ID(s):
- 1734980
- PAR ID:
- 10075673
- Date Published:
- Journal Name:
- ICONS '18 Proceedings of the International Conference on Neuromorphic Systems Article No. 8
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3229884.3229892
