Vision Science and Prosthetics

Visual perception is a highly-developed and complex sensory phenomenon in humans. It begins in the retina, an area at the back of the eyeball which is densely covered in photoreceptors.[1] Coded information about the visual world is transmitted along the geniculostriate pathway, passing the lateral geniculate nucleus (LGN) en route to the primary visual cortex (V1).[1] Visual information is sorted into its constituent components along the thalamus, striate and extrastriate cortex before being projected to the parietal and inferotemporal lobes, where objects and their spatial relationships are represented.[2] Because humans have relatively underdeveloped sensory systems for olfaction, audition, touch, and taste, vision is particularly important for interacting with the external environment. As such, the restoration of vision for the blind has become a favoured and fruitful area of research. Indeed, several methods have been designed for implantation in various structures that modulate incoming visual signals from its origin in the eyeball to pathways within the brain. Candidates include the retina itself, the thalamus, and the primary visual cortex. Understanding electrical circuitry and neuronal impulses will be important in creating functional prostheses. Vision is also of interest to researchers who study cognition, as visual imagery – which is based on the same neural machinery as visual perception – is a fundamental component of thought and memory.[2]

Bibliography
1. Troncoso X.G., Macknik S.L., Martinez-Conde S. (2011). Vision’s First Steps: Anatomy, Physiology, and Perception in the Retina, Lateral Geniculate Nucleus, and Early Visual Cortical Areas. Visual Prosthetics. Pp 23-57.
2. Cavangh P. (2011). Visual Cognition. Vision Res. 51(13):1538-51.
3. Meister, M., & Berry II, M. J. (1999). The Neural Code of the Retina. Neuron, 22(3), 435-450. 
4. Nirenberg, S., & Pandarinath, C. (2012). Retinal prosthetic strategy with the capacity to restore normal vision. Proceedings of the National Academy of Sciences of the United States of America, 109(37), 15012–15017.


Cortical Implants

main article: Cortical Implants
author: Joel Tan

Cortical implants in V1 cortex
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Visual perception in humans is a complex neuronal sensory pathway developed through the evolution of optic lenses and photoreceptors. Those specialized neurons convert photons into electrical signals that are transported to the visual cortex, where information is interpreted and processed.[1] Damage to any parts of the optical pathway results in visual impairments. Blindness is a condition suffered by more than 40 million people in the world, and it creates an obstacle to leading a normal life. Hence, a great deal of research has been done in the field of vision restoration. Retinal implants and bionic eyes are pioneering the field in terms of neural prosthetics. However, newer animal models have been recently developed and proposed to replace sensory input by bypassing the pre-cortical visual pathway to provide a direct stimulus to the cortex.[2]

Bibliography
1. first full source reference
2. second full source reference


hESC Derived Subretinal Implants

main article: hESC Derived Subretinal Implants
author: Waldo Lefever Olmedo

Figure 1: Eye Anatomy: Focus – Fovea, Retina
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Image Source: [Bibliography item 9 not found.]

Age Related Macular Degeneration, (AMD), is a visual disorder which disrupts function in the macula of the retina. The center of visual field is located in the macula; it is critical for vision sensitive tasks such as reading, and driving. There are two known forms of AMD; both of them affect the Retinal Pigmented Epithelium cells, (RPE) [2]. Dry AMD leads to severe atrophy of RPE cells, eventually causing malnourishment of photoreceptors. Wet AMD occurs when a growth of blood vessels from the choroid expands to the center of the visual field. The macula is normally isolated from blood vessels in order to allow for maximum light to penetrate the retina. Macular degeneration patients demonstrate increased performance in visual tasks when subjects undertake a subretinal implantation of RPE cells. The procedure involves deriving RPE cells from Human Embryonic Stem Cells, (hESC). This approach is remarkable since it allows for the selection of young, healthy RPEs which would otherwise be difficult to obtain from a donor. The present delivery system for implantation in clinical trials involves injection of RPE cells with the use of rigid materials including a stiff cannula. Due to the physical properties of RPE cells, this method leads to clustering and damage of the implanted tissue, as well as risks of retinal injury [1]. Novel technologies for subretinal implants are being trialed in order to reduce hazards involved with the treatment.

Bibliography
1. Yuntao Hu et al. A Novel Approach for Subretinal Implantation of Ultrathin Substrates Containing Stem Cell-Derived Retinal Pigment Epithelium Monolayer. Opthalmic Research 48:186–191 (2012)
2. Steven D Schwartz et al. Embryonic stem cell trials for macular degeneration: a preliminary report. Lancet 12; 379: 713–20 (2012)


Retinal Coding

main article: Retinal Coding
author: Herun Tarun

Bionic Eye
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source: http://arya-dragonqueen.deviantart.com/art/Bionic-Eye-148158145

Retinal coding is a subsection of population coding, the process of how stimulus information is represented in the neuronal level, specifically how visual information (color, shape, depth etc) is encoded through ganglionic cells[1]. It's particularly important in the field of vision science and prosthetics since modern prosthetics can allow one to perceive spots of light and sharply contrasted edges, focusing on resolution, they do not allow perception of images[2]. Even still, there is a limit to improving resolution, and retinal coding allows visual prosthetics to pass beyond this inherent barrier. Understanding of the retinal code allows one to perceive images as a whole as opposed to images of contrast. Research in this area is focused on identifying the measure of neural activity in ganglionic retinal cells, analyzing it’s response to given visual stimuli, understanding how precise the response is, and specifying the degree of plasticity in stimulus-response relationships[1].

Bibliography
1. Meister, M., & Berry II, M. J. (1999). The Neural Code of the Retina. Neuron, 22(3), 435-450.
2. Nirenberg, S., & Pandarinath, C. (2012). Retinal prosthetic strategy with the capacity to restore normal vision. Proceedings of the National Academy of Sciences of the United States of America, 109(37), 15012–15017


Retinal Implants

main article: Retinal Implants
author: Kenny Ma
Retinal degenerative disorders such as Retinitis Pigmentosa, and especially Macular Degeneration are common nowadays. With a lack of natural drugs or treatments available, other alternative vision restoration methods are needed. Retinal implants are now becoming a focus of research as the idea of bypassing the damage instead of trying to repair it has great potential. Retinal implants nowadays are mostly divided into the epiretinal and subretinal groups, but newer technologies are being investigated as well . The Artifical Silicon Retina and the Argus 2 system are one of the more successful implants presently [6]. Other emerging implants like optogenetics are currently stepping up to be another viable treatment to degenerative retinal diseases.

Bibliography
1. Soest, V.S., et al. Retinitis Pigmentosa: Defined From a Molecular Point of View. Survey of Ophthalmology. 43(4), 321-334 (1991).
2. Paulus, T.V.M., and de Jong, M.D. Age-Related Macular Degeneration. The New England Journal of Medicine. 355, 1474-1485 (2006).
3. Helmut, G., and Gabel, V. Retinal replacement- the development of retinal microelectronic retinal prostheses- experience with subretinal implants and new aspects. Arch Clin Exp Ophthamol. 242, 717-723 (2004).
4. Chow, M.D., et al. The Artificial silicon Retinal Microchip for the Treatment of Vision Loss From Retinitis Pigmentosa. Arch Ophthamol. 122, 460-469 (2004).
5. Harada, C., Harada, T., and Mitamura, Y. The role of cytokines and tropic factors in epiretinal membranes: Involvement of signal transduction in glial cells. Progress in Retinal and Eye Research. 25, 149-165 (2006).
6. Margalit, M.D., et al. Retinal Prosthesis for the Blind. Survey of Ophthalmology. 47(4), 335-356 (2002).
7. Hamayun, M.S., et al. Preliminary 6 Month Results from the Argus (TM) II Epiretinal Prosthesis Feasibility Study. Investigative Ophthalmology and Visual Scienc. 53(9), 5095-5101 (2012).
8. Ahuja, A.K., et al. blind subjects implanted with Argus 2 retinal prosthesis are able to improve performance in a spatial-motor task. Br J Ophthamol. 95, 539-543 (2011).
9. Weiland, J.D., Faraji, B., Greenberg, R.J., Humayun, M.S., and shellock, F.G. Assesment of MRI issues for the Argus 2 Retinal prosthesis. Magnetic Resonance Imaging. 20(3), 382-389 (2012).
10. Busskamp, V., and Botond, R. Optogenetic Approaches to restoring visual function in Retinitis Pigmentosa. Current opinion in Neurobiology. 21, 942-946 (2011).
11. Nirenber, S., and Pandarinath, C. Retinal Prosthetic Strategy with the capacity to restore normal vision. PNAS. 109 (37), 15012-15017, DOI: 10.1073/pnas.1207035109 (2012).


Subcortical Visual Prosthetics

main article: Subcortical Visual Prosthetics
author: Ileea Larente

Ventrolateral Thalamic Nuclei
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The lateral geniculate nucleus extends from ventral posterior tip of the
thalamus, inferior to the pulvinar and lateral to the medial geniculate nucleus.
Image source: http://en.wikipedia.org/wiki/Ventral_lateral_nucleus.

The lateral geniculate nucleus (LGN) of the thalamus has recently received attention as a possible site of visual prosthesis. The LGN is a subcortical structure of early visual processing that is retinotopically organized, easily accessed surgically, and structurally flat in humans, making it an appealing alternative to cortical, retinal, and subretinal implantation. [1] The possibility of LGN prosthesis stems from the ability of electrical stimulation to produce phosphenes, which are percepts of light in the absence of retinal stimulation. [2] If video signals were converted into electrical signals and delivered to the LGN by microelectrodes, then such stimulation could reconstruct a pixelated representation of the visual world. [1] Studies of simulated vision suggest that microelectrode stimulation to the LGN could allow the identification of faces and text; [3] however, the neural signals contained in the LGN are complex with numerous interactions, posing a formidable challenge to the development of an appropriate model of stimulation. [4]

Bibliography
1. Pezaris, J. S., & Reid, R. C. (2009). Simulations of Electrode Placement for a Thalamic Visual Prosthesis. Ieee Transactions on Biomedical Engineering, 56(1), 172–178. doi:10.1109/TBME.2008.2005973
2. Pezaris, J. S., & Reid, R. C. (2007). Demonstration of artificial visual percepts generated through thalamic microstimulation. Proceedings of the National Academy of Sciences of the United States of America, 104(18), 7670–7675. doi:10.1073/pnas.0608563104
3. Chen, S. C., Suaning, G. J., Morley, J. W., & Lovell, N. H. (2009). Simulating prosthetic vision: I. Visual models of phosphenes. Vision Research, 49(12), 1493–1506. doi:10.1016/j.visres.2009.02.003
4. Millard, D. C., Wang, Q., & Stanley, G. B. (2011). Nonlinear System Identification of the Thalamocortical Circuit in Response to Thalamic Microstimulation. In 2011 5th International Ieee/Embs Conference on Neural Engineering (ner) (pp. 1–4). New York: Ieee.


Visual perception related to object recognition in higher stage (V4, IT) in ventral pathway

main article: Visual perception related to object recognition in higher stage (V4, IT) in ventral pathway
author: Dehi Joung

How does your brain perceive object?
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A pathway of visual object perception. Source: http://cogprints.org/2798/1/Lehar-Velmans_Commentary(cogprints).htm

Visual information from thalamus is projected to the primary visual area at occipital lobe in order to interpret the environment around us[2]. This interpretation, called visual perception, is associated with not only sensory inputs but also memories[2]. There are two visual processing: bottom-up sensory information arises from retina, and top-down neuronal signals are associated with imagery that influences the bottom-up processing[2]. In addition, visual perception is affected by the region such as Frontal eye Field in prefrontal cortex[3]. The visual information processing in the first stage of ventral pathway (V1) has been well studied. Many studies showed that visual inputs from V1 is projected to Inferior Temporal(IT) cortex along the ventral pathway[2],known as what pathway; however, this processing in higher stages(such as V4, IT) is not fully elucidated1. Recent studies suggested that the last step of the ventral visual pathway occurs in the IT cortex, and it plays important roles in visual object recognition, which is the ability to identify particular object by labelling and categorizing regardless of various appearance of visual target[1]. Also, based on computational models, recent studies have focused on algorithms for uncovering circuits in visual object recognition that may provide opportunities to reveal and treat entire circuit in brain lesions or diseases related to visual perception, such as agnosia and blindness[1].

Bibliography
1. DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition?. Neuron, 73(3), 415 – 434.
2. Albright, T. D. (2012). On the perception of probable things: neural substrates of associative memory, imagery, and perception. Neuron, 74(2), 227-245.
3. Libedinsky, C., & Livingstone, M. (2011). Role of prefrontal cortex in conscious visual perception. J. neuroscience , 31(1), 64 – 69.
4. Thompson, K.G.,& Schall, J.D. (1999). The detection of visual signals by macaque frontal eye field during masking. Nat Neurosci, 2, 283–288.
5. Schall, J.D. (2002). The neural selection and control of saccades by the frontal eye field. Biol Sci, 357, 1073–1082.
6. Stanton, G.B., Bruce, C.J.,& Goldberg, M.E. (1995). Topography of projections to posterior cortical areas from macaque frontal eye fields. J Comp Neurol, 353. 291–305.
7. Edward, H.F., de, H.,& Cowey, A. (2011). On the usefulness of ‘what’ and ‘where’ pathways in vision. Trends in Cognitive Sciences, 15 (10), 460-466.
8. Ungerleider, L.G., & Mishkin, M. (1982). Two cortical visual systems. In Analysis of Visual Behavior, 549–586.
9. Milner, A.D.,& Goodale, M.A. (1995). The visual brain in action.
10. Kravitz, D.J., Saleem, K.S., Baker, C.I., & Mishkin, M. (2011). A new neural framework for visuospatial processing. Nat Rev Neurosci, 12, 217–230.
11. Grol, M.J., Majdandz̆ić, J., Stephan , K. E., Verhagen , L., Dijkerman , H.C., Bekkering , H., Verstraten, A. J., & Toni, I. (2007). Parieto-frontal connectivity during visually guided grasping. J Neurosci, 27, 11877–11887.
12. Sincich, L.C., Park, K.F., Wohlgemuth, M.J., & Horton, C. (2004).Bypassing V1: a direct geniculate input to area MT. Nat Neurosci,7, 1123–1128.
13. Battelli, L. Walsh, L.V., Leone, A.P.,&Cavanagh,P. (2008).The ‘when’ parietal pathway explored by lesion studies. Curr Opin Neurobiol, 18, 120–126.
14. Logothetis, N.K.,& Sheinberg, D.L. (1996). Visual object recognition. Annu Rev Neurosci,19, 577–621.
15. Rousselet, G.A., Fabre-Thorpe, M., & Thorpe, S.J. (2002). Parallel processing in high-level categorization of natural images. Nat Neurosci, 5, 629–630.
16. DiCarlo, J.J., & Cox, D.D. (2007). Untangling invariant object recognition. Trends Cogn Sci, 11, 333–341.
17. Rust, N.C., and DiCarlo, J.J. (2010). Selectivity and tolerance (‘‘invariance’’) both increase as visual information propagates from cortical area V4 to IT. J Neurosci, 30, 12978–12995.
18. Roelfsema, P.R., and Houtkamp, R. (2011). Incremental grouping of image elements in vision. Atten Percept Psychophys,73, 2542–2572.



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