报告摘要：
报告题目： 随机自适应采样：受体神经元的自适应感知机制与多尺度模型
Sensory Adaptation by Stochastic Adaptive Sampling in Receptor Neurones ---- A Multi-scale Modelling Approach
报告人: Zhuoyi Song博士（英国University of Sheffield）
时间: 2017年6月7日（周三）上午10:00
报告摘要：
地点: 延长校区电机楼201会议室
报告摘要：
A traditional challenge in neuroscience is to understand a sensory neuron’s input-output relationship, which constantly adapts to environmental changes. I currently study this adaptation process in sensory receptors, whereas the common engineering challenge is to achieve a large dynamic range, i.e. effectively encode vastly varying naturalistic stimuli within their limited response range.
I will talk about stochastic adaptive sampling rules to achieve this challenge for sensory encoding, which requires only four general parameters. I will show that with these rules, a fly photoreceptor can encode stimulus from starlight to sunlight, and similarly a mechanoreceptor can generate complex temporal adaptation dynamics to simple stretch stimuli. I will also talk about my multi-scale computational models that simulate adaptation from the molecular to the systems level.
More in detail, Drosophila R1-R6 photoreceptor integrates light information from its ~30,000 microvilli, each of which can generate quantum bumps (QBs) from single photon absorptions. I will show how the cell’s voltage responses and their adaptation can be much explained by dynamic changes in four quantal sampling parameters/factors: (1) the number of photon sampling units in the cell structure (microvilli); (2) sample size (QB waveform); (3) latency distribution (time delay between photon arrival to emergence of a QB), and (4) refractory period distribution (time for a microvillus to recover after a QB). Stories include but not limited to: how refractory sampling attenuates contrast changes, how stochasticity contributes to anti-aliasing, how a global gain control forms adaptive quantal sampling, etc.
I will show that these stochastic adaptive sampling rules accurately predict information sampling across a range of fly species with different visual ecologies, supporting their general role in encoding sensory information. I will also discuss why and how these rules may work with other sensory modalities.
报告摘要：
报告人简介:
Dr. Zhuoyi Song is currently a research fellow of computational neuroscience at the University of Sheffield (UoS), UK. Her current work focuses on developing multi-scale modeling and inference framework for understanding signal transduction mechanisms in sensory receptor neurons. She received combined training in both Engineering and Biomedical Science disciplines. She obtained both of her undergraduate and master degree in Electrical Engineering and Control Theory. She then got a Ph.D. in the interdisciplinary area of computational neuroscience at UoS in 2011. She continued her postdoctoral training in Prof. Mikko Juusola’s lab, biomedical Science department, UoS. In 2013, she received a prestigious 2020 Science research fellowship in UCL, London in computational life science. Dr. Zhuoyi Song has published over 10 high impact journal papers, including Current Biology (IF>10) and Journal of Neuroscience (IF>7). She has given many talks at renowned research institutions, e.g. University of Oxford, UCL, etc. She has also received many international awards (>20) for training and research internships.
