Hardware architecture of a neural net based retina implant for patients suffering from retinitis pigmentosa

Schwarz, Markus; Hosticka, B.J.; Hauschild, R.; Mokwa, W.; Scholles, M.; Trieu, Hoc Khiem

doi:10.1109/icnn.1996.548973

Cited by 13 publications

(4 citation statements)

References 3 publications

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“…The RE will consist of about 100-1.000 tunable RF filters [9,10,13]. Each RF filter simulates the spatial and temporal RF proper ties of typical primate retinal ganglion cells as Learning Retina Implants with Epiretinal Contacts depicted in figure 2.…”

Section: Concept Of Learning Resmentioning

confidence: 99%

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Learning Retina Implants with Epiretinal Contacts

1997

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Retina implants are currently being developed by several interdisciplinary research consortia worldwide for blind humans with various retinal degenerative diseases. It is the aim of our retina implant project to develop a novel type of visual prosthesis to regain a moderate amount of vision such as perception of location and shape of large objects in the first stage and to approach reading quality in a subsequent stage. In our planned retina implant, a retina encoder (RE) outside the eye has to replace the information processing of the retina. A retina stimulator (RS), implanted adjacently to the retinal ganglion cell layer, has to contact a sufficient number of retinal ganglion cells/fibers for electrical elicitation of spikes. A wireless signal and energy transmission system has to provide the communication between the RE and RS. This paper outlines the retina implant project of our consortium of 14 expert groups and describes first results of the learning RE. The RE approximates the typical receptive field (RF) properties of primate retinal ganglion cells by means of individually tunable spatiotemporal RF filters. The RE as a cluster of RF filters maps visual patterns onto spike trains for a number of contacted ganglion cells. A concept is presented to train the individual RF filters in an unsupervised learning process, which employs neural networks in a dialog with the individual human subject. The desired aim of this dialog is an optimization of the visual perception by matching the various RF filter properties with those ‘expected’ by the central visual system for each contacted ganglion cell.

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Section: Concept Of Learning Resmentioning

confidence: 99%

“…Several groups in the USA [5][6][7] and Ger many [8][9][10] arc currently developing compo nents for such retina implants. It is hoped to complete the development of entire retina im plant prototypes and experimental tests in an imals within the next 3-5 years and to apply this technology to humans within the next 10 years.…”

Section: Introductionmentioning

confidence: 99%

Learning Retina Implants with Epiretinal Contacts

1997

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“…9,35,36 It is also a motivation for building MNN hardware implementations. 16,18,30,35,37 However, simulating what we now know about the biological "circuits" may well be beyond the capability of the current hardware technology. For example, when part of the biological retina was simulated by a new artificial retina implant system, 37 the resultant hardware architecture goes "beyond the current decade's complexity of bioelectronic systems".…”

Section: Biological Motivationsmentioning

confidence: 99%

Modular Neural Networks: A Survey

Auda

Kamel

1999

Int. J. Neur. Syst.

129

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Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.

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“…Two CMOS image sensors have been developed using this pixel principle. In order to realize a circularly symmetric receptive field spatial filter kernel (RF-spatial filter kernels can be described by a difference of 2D-gaussian functions [5]) the pixel elements of the first 128x 128 pixel CMOS sensor have been arranged on a hexagonal grid structure which yields a reduction of required samples of 13.5% in comparison to rectangular sampling used for the second 400x300 pixel sensor [8]. This is a benefit for lower resolution images, but on the other hand it requires about 5% more pixel area for metal wiring.…”

Section: Retina Enc0de:r Cmos Image Sensormentioning

confidence: 99%

Single chip CMOS image sensors for a retina implant system

Schwarz

Hauschild²,

Hosticka³

et al.

ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187)

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This work describes the architecture and realization of microelectronic components for a retina implant system that will provide visual sensations to patients with photoreceptor degeneration by applying electrostimulation of the intact retinal ganglion cell layer. Special circuitry has been developed for a fast single-chip CMOS image sensor system which provides high dynamic range of more than seven decades (without mechanical shutter) corresponding to the performance of the human eye. This image sensor system is directly attachable to a digital filter and a signal processor that compute the so-called receptive-field function for generation of the stimulation data. These external components are wireless linked to an implanted flexible silicon multielectrode stimulator which generates electrical signals for electrostimulation of the intact ganglion cells. All components, including additional hardware for digital signal processing and wireless data and power transmission have been developed for fabrication using our in-house standard CMOStechnology.

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Hardware architecture of a neural net based retina implant for patients suffering from retinitis pigmentosa

Cited by 13 publications

References 3 publications

Learning Retina Implants with Epiretinal Contacts

Learning Retina Implants with Epiretinal Contacts

Modular Neural Networks: A Survey

Single chip CMOS image sensors for a retina implant system

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