A bioinspired image coder with temporal scalability We present a novel bioinspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with timedelays. The second stage is a timedependent analogtodigital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.

Nonlinear retinal response modeling for future neuromorphic instrumentation The development of a bioinspired 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 1xe2x80x934. 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 highspeed motion detection at lowpower requirements 6xe2x80x939. 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 frontend array of photoreceptor cells, and pass through a system of nonlinear integral equations derived from experimental voltage and currentclamp 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.