Implementation of an elaborated neuromorphic model of a biological photoreceptor

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/m(2), 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.