Artikel
Learning of image encoding
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Veröffentlicht: | 30. November 2009 |
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Gliederung
Text
Purpose: Simulation study of a novel retina implant with perceptual feedback, oculomotor feedback for simulated miniature eye movements (SM), and with optional feedback from spontaneous nerve impulses (NI) of the stimulated regions.
Methods: Simulations were performed with a novel retina encoder (RE-3) with a filter module (FM) consisting of an input array of 20x20 pixels for presentation of a pattern P1 and 100 spatio-temporal (ST) filters for generation of selective stimulation signals for 100 electrodes at the retinal output, a novel inverter module (IM-3) to mimic parts of the central visual system and to map the FMRef-output onto a simulated percept P2, and a dialog module (DM). DM simulated the perceptual feedback from a human user. SMs were generated on demand for movements of P1 by one pixel in a given direction to mimic typical eye movements during fixation. Spontaneous NI could be considered to avoid stimulus pattern disturbances: a) stimulation signals with NI-suppression capability and b) neural feedback from neural tissue to RE-3 via bi-directional electrode arrays for stimulation and recording.
Results: (a) FM was specified as a regular distribution of three ST filter types to mimic receptive field properties of primate retinal ganglion cells. (b) Both P1 and FM filter array were described as multi-dimensional vector matrices, which had several advantages, including easy changes of pixel numbers or filter numbers as well as FM-output calculation as matrix products. (c) The software for IM-3 was designed to process the FM-output matrices with an efficient algorithm and to invert the partly ambiguous FM mapping with the help of SMs. (d) Due to the matrix structure, inversion of the FM-output by IM-3 could be processed in a single matrix run through all coefficients.
Conclusions: Since ‘Gestalt’ perception in humans requires active vision as a continuous interaction between sensory and oculomotor processes, a combination of neural-, oculomotor, and perceptual feedback may be important to optimize the function of bionic visual prostheses.
This lecture is available as video recording (Attachment 1 [Attach. 1]).