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

大脑具有令人印象深刻的处理感官信息的能力，尽管系统的外部输入通常是贫乏的（外部噪声），并且处理感官信息的神经元回路受到其自身固有的内部噪声源的影响。最近的研究表明，大脑具有自适应补偿至少部分噪声源的非凡能力，但这种现象背后的计算原理目前尚不清楚。深入了解感觉系统如何能够稳健地运行并减轻噪声的有害影响，对于我们理解神经回路中的信息处理至关重要，并且从长远来看，可能会影响机器人和人工感觉系统算法的发展，这些算法对噪声和扰动具有弹性。该项目的目的是探索这些重要问题，目的是了解视觉系统如何动态适应噪声和其他可能最终损害功能的因素（例如神经损失）。该项目将采用混合方法，包括人类视觉的心理物理测试和数据的计算建模。该研究领域与EPSRC的数学科学主题，特别是生物信息学领域的研究广泛一致。

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.