Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. However, adaptation also entails computational costs: adaptive code is intrinsically ambiguous, because output symbols cannot be trivially mapped back to the stimuli without the knowledge of the adaptive state of the encoding neuron. It is thus important to learn which statistical changes in the input do, and which do not, invoke adaptive responses, and ask about the reasons for potential limits to adaptation. We measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying two-dimensional linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the retinal ganglion cells adapt to contrast, but exhibit remarkably invariant behavior to changes in higher-order statistics. Finally, by theoretically analyzing optimal coding in LN-type models, we showed that the neural code can maintain a high information rate without dynamic adaptation despite changes in stimulus skew and kurtosis.
The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream
Institute of Cognitive Science, University of Osnabrück（奥斯纳布吕克大学认知科学研究所）；Department of Medicine and Surgery, Neuroscience Unit, University of Parma（帕尔马大学医学与外科系神经科学单元）；Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW)（荷兰神经科学研究所（荷兰皇家科学院）视觉与认知系）
Eye dominance and testing order effects in the circularly-oriented macular pigment optical density measurements that rely on the perception of structured light-based stimuli
Centre for Eye and Vision Research（眼与视觉研究中心）；Entoptica Limited（Entoptica有限公司）；Kazakh Eye Research Institute（哈萨克斯坦眼科研究所）；Institute for Quantum Computing, University of Waterloo（滑铁卢大学量子计算研究所）；Department of Physics, University of Waterloo（滑铁卢大学物理系）；Incoherent Vision Inc.（Incoherent Vision公司）；Department of Physics, University at Buffalo, State University of New York（纽约州立大学布法罗分校物理系）；Department of Chemistry, University of Waterloo（滑铁卢大学化学系）；School of Optometry and Vision Science, University of Waterloo（滑铁卢大学验光与视科学学院）
Higher-Order Convolution Improves Neural Predictivity in the Retina
Institut de la Vision & Laboratoire des Systèmes Perceptifs Sorbonne Université, CNRS, INSERM, & École Normale Supérieure（视觉研究所及感知系统实验室，索邦大学，CNRS，INSERM，以及巴黎高等师范学院）；Institut de la Vision Sorbonne Université, CNRS, INSERM（视觉研究所，索邦大学，CNRS，INSERM）；Sensory Processing and Computation Unit & Laboratoire des Systèmes Perceptifs Italian Institute of Technology & École Normale Supérieure（感知与计算单元及感知系统实验室，意大利理工学院及巴黎高等师范学院）
