RetinaStudio: A bioinspired framework to encode visual information

Martínez-Álvarez, Antonio; Olmedo-Payá, Andrés; Cuenca-Asensi, Sergio; Vicente, José Manuel Ferrández; Fernández, Eduardo

doi:10.1016/j.neucom.2012.07.035

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…In this framework some authors are starting to incorporate these involuntary eye movements into their retinal models using different strategies: micro-saccades, drifts and tremor. The results show that transforming an originally stationary image into one that varies spatiotemporally (thus mimicking real fixational eye movements) improve feature estimation (Martínez-Álvarez et al, 2013;Olmedo-Paya et al, 2015).…”

Section: Delivering Of Information To Implantsmentioning

confidence: 94%

Development of visual Neuroprostheses: trends and challenges

Fernández

2018

Bioelectron Med

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Visual prostheses are implantable medical devices that are able to provide some degree of vision to individuals who are blind. This research field is a challenging subject in both ophthalmology and basic science that has progressed to a point where there are already several commercially available devices. However, at present, these devices are only able to restore a very limited vision, with relatively low spatial resolution. Furthermore, there are still many other open scientific and technical challenges that need to be solved to achieve the therapeutic benefits envisioned by these new technologies. This paper provides a brief overview of significant developments in this field and introduces some of the technical and biological challenges that still need to be overcome to optimize their therapeutic success, including long-term viability and biocompatibility of stimulating electrodes, the selection of appropriate patients for each artificial vision approach, a better understanding of brain plasticity and the development of rehabilitative strategies specifically tailored for each patient.

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Section: Delivering Of Information To Implantsmentioning

confidence: 94%

Development of visual Neuroprostheses: trends and challenges

Fernández

2018

Bioelectron Med

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“…To aid experimenters in choosing a good stimulus, we demonstrated that also the parameters of a stimulus can be optimized with gradient descent to maximize the difference between two very similar models and thus the information that new experiments will provide for the model fitting process (Section 4.5). [30], Topographica [4], Virtual Retina++ (continued development of VR in the ENAS / PRANAS package [8]), Lorach et al [17], and RetinaStudio [18] . ≈VR signifies that the gain control was implemented similarly to VirtualRetina: Contrast gain control through shunting inhibition, using a local estimate of spatio-temporal contrast [31].…”

Section: 3)mentioning

confidence: 99%

Convis: A Toolbox To Fit and Simulate Filter-based Models of Early Visual Processing

Huth

Masquelier

Arleo

2017

Preprint

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We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e.g., Monte Carlo or particle optimizations without gradients. We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina (Wohrer and Kornprobst, 2009), with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator. For instance it can also predict the response of V1 orientation selective cells. Convis is open source under the GPL-3.0 license and available from https://github.com/jahuth/convis/ with documentation at https://jahuth.github.io/convis/.

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“…In this paper several retina models which are sensitive to variations in luminance are described by mean of RetinaStudio [18], a framework to encode visual information that allows the specification, testing, simulation, validation and implementation of bioinspired retina models. Each retina model is defined as a matrix of different kind of ganglion cells.…”

Section: Retina Modelmentioning

confidence: 99%

Modeling the role of fixational eye movements in real-world scenes

et al. 2015

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Cite this article as: Andrés Olmedo-Payá, Antonio Martínez-Álvarez, Sergio Cuenca-Asensi, J.M. Ferrández, E. Fernández, Modeling the role of fixational eye movements in real-world scenes, Neurocomputing, http://dx.doi.org/10.1016/j. neucom. 2014.09.068 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractOur eyes never remain still. Even when we stare at a fixed point, small involuntary movements take place in our eyes in an imperceptible manner.Researchers agree on the presence of three main contributions to eye movements when we fix the gaze: microsaccades, drifts and tremor. These small movements carry the image across the retina stimulating the photoreceptors and thus avoiding fading. Nowadays it is commonly accepted that these movements can improve the discrimination performance of the retina. In this paper, several retina models with and without fixational eye movements were implemented by mean of RetinaStudio tool to test the feasability of these models to be incorporated in future neuroprostheses. For this purpose each retina model has been stimulated with natural scenes images in two experiments. Results are discussed from the point of view of a neuroprosthesis development.

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RetinaStudio: A bioinspired framework to encode visual information

Cited by 13 publications

References 8 publications

Development of visual Neuroprostheses: trends and challenges

Development of visual Neuroprostheses: trends and challenges

Convis: A Toolbox To Fit and Simulate Filter-based Models of Early Visual Processing

Modeling the role of fixational eye movements in real-world scenes

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