Modelling the Effects of Internal and External Noise of Perceptual Systems.

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The brain has an impressive ability to process sensory information, despite the fact that both the external input to the system is often impoverished (external noise) and the neuronal circuitry that processes the sensory information is subject to its own inherent sources of internal noise. Recent research suggests that the brain has the remarkable ability to adaptatively compensate for at least some of these sources of noise, but computational principles underlying this phenomenon are currently unknown. A thorough understanding of how sensory systems are able to operate robustly and mitigate the deleterious effects of noise is essential for our understanding of information processing in neural circuits and in the longer term may impact upon the development of algorithms, for robotics and artificial sensory systems, that are resilient to noise and perturbations. The aim of this project is to explore these important issues with the objective of understanding how the visual system dynamically adapts to the presence of noise and other factors (e.g. neural loss) that could ultimately impair function. The project will employ a mixed methodology including psychophysical testing of human vision and computational modeling of data. This field of research aligns broadly to EPSRC's mathematical Science theme and in particular research in the area of biological informatics.