Nonlinear retinal response modeling for future neuromorphic instrumentation

The development of a bio-inspired image sensor that can match the functionality of the vertebrate retina in terms of image resolution, power dissipation and dynamic resolution demands an extremely challenging standard in terms of efficiency and performance [1]–[4]. At present, functional biological image processing possesses extremely high temporal resolution by asynchronously optimizing the sampling rate of a scene but suffers from low spatial resolution, noise, and an inability to process low frequency content [5]. Regardless, the use of dynamic vision sensors that emulate biological retina behaviors are important in performing classification tasks using high-speed motion detection at low-power requirements [6]–[9]. The difficulty in pushing image sensor performance to meet the specifications of the retina lies in the biological complexities of the underlying neural networks. Therefore, a prerequisite for hardware mapping of biological vision systems is understanding the bioprocessing between various retina cells. Here, we present a biologically plausible cellular network retina simulator, where light stimuli are accepted at the front-end array of photoreceptor cells, and pass through a system of nonlinear integral equations derived from experimental voltage- and current-clamp data used to calculate the response of each sequential retina cell. It was shown in [10], [11] that integrating the system improves performance when compared with conventional numerical solvers. We expect that by placing our simulator in the hands of interdisciplinary researchers, especially computational neuroscientists, mathematical scientists and machine learning practitioners, it will foster the development of more efficient representations of visual inputs in deep learning applications.