Abstract
The function of the low-level image processing that takes place in the biological retina is to compress only the relevant visual information to a manageable size. The behavior of the layers and different channels of the neuromorphic retina has been successfully modeled by cellular neural/nonlinear networks (CNNs). In this paper, we present an extended, application-specific emulated-digital CNN-universal machine (UM) architecture to compute the complex dynamic of this mammalian retina in video real time. The proposed emulated-digital implementation of multichannel retina model is compared to the previously developed models from three key aspects, which are processing speed, number of physical cells, and accuracy. Our primary aim was to build up a simple, real-time test environment with camera input and display output in order to mimic the behavior of retina model implementation on emulated digital CNN by using low-cost, moderate-sized field-programmable gate array (FPGA) architectures.
Highlights
The most important sensory organ for both humans and mammals is the retina, which is a sophisticated visual preprocessor system
Definition of the neuromorphic model elements was based on retinal anatomy and electrophysiology measurements. The complexity of this channel-based model is moderate enough, it can be adapted to different hardware implementations using the cellular neural/nonlinear networks (CNNs) computational paradigm. These cellular neural/nonlinear network- (CNN-)based retina models can be realized in different forms, such as software simulator, analog VLSI
The basic building blocks of the biological retina model are the abstract neurons which are organized into two-dimensional layers [2]
Summary
The most important sensory organ for both humans and mammals is the retina, which is a sophisticated visual preprocessor system This well-known part of the eye sends visual information to the higher brain center (visual cortex) across several parallel stacked channels (visual pathway). The detailed framework of mammalian retinal modeling via two-dimensional and multilayer CNN was published in [5], which provides us suitable model background to the real-time implementation. Definition of the neuromorphic model elements was based on retinal anatomy and electrophysiology measurements The complexity of this channel-based model is moderate enough, it can be adapted to different hardware implementations using the CNN computational paradigm. These CNN-based retina models can be realized in different forms, such as software simulator, analog VLSI.
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