Implementation of an elaborated neuromorphic model of a biological photoreceptor |
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Authors: | Eng-Leng Mah Russell S A Brinkworth David C O’Carroll |
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Institution: | (1) Discipline of Physiology, School of Molecular and Biomedical Science and the Centre for Biomedical Engineering, The University of Adelaide, Adelaide, SA, 5005, Australia |
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Abstract: | We describe here an elaborated neuromorphic model based on the photoreceptors of flies and realised in both software simulation
and hardware using discrete circuit components. The design of the model is based on optimisations and further elaborations
to the mathematical model initially developed by van Hateren and Snippe that has been shown to accurately simulate biological
responses in simulations under both steady-state and limited dynamic conditions. The model includes an adaptive time constant,
nonlinear adaptive gain control, logarithmic saturation and a nonlinear adaptive frequency response mechanism. It consists
of a linear phototransduction stage, a dynamic filter stage, two divisive feedback loops and a static nonlinearity. In order
to test the biological accuracy of the model, impulses and step responses were used to test and evaluate the steady-state
characteristics of both the biological (fly) and artificial (new neuromorphic model) photoreceptors. These tests showed that
the model has faithfully captured most of the essential characteristics of the insect photoreceptor cells. The model showed
a decreasing response to impulsive stimuli when the background intensity was increased, indicating that the circuit adapted
to background luminance in order to improve the overall operating range and better encode the contrast of the stimulus rather
than luminance. The model also showed the same change in its frequency response characteristics as the biological photoreceptors
over a luminance range of 70,000 cd/m2, with the corner frequency of the circuit ranging from 10 to 90 Hz depending on the current state of adaptation. Complex
naturalistic experiments have also further proven the robustness of the model to perform in real-world scenario. The model
showed great correlation to the biological photoreceptors with an r
2 value exceeding 0.83. Our model could act as an excellent platform for future experiments that could be carried out in scenarios
where in vivo intracellular recording from biological photoreceptors would be impractical or impossible, or as a front-end
for an artificial imaging system. |
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Keywords: | Insect vision Visual system Adaptive photoreceptor Neuromorphic Bio-inspired Artificial vision |
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