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Bin Yang

Bin Yang   CV

Education & Training
  2013 - present
      Research Assistant
      Mentor: Mingsha Zhang
      National Key Lab of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P.R. China
  2009 - 2013
      B.Eng. in Software Engineering
      College of Software, Nankai University, Tianjin, P.R. China

Research Interests

One topic I currently focus on is about microsaccades. Microsaccades are small fixational eye movements which have been reported to be involved in preventing visual fading, supporting detailed visual sampling etc. Although microsaccades direction was found to be attention-modulated, the detailed modulation remains unclear due to the fact that the spatiotemporal dynamics of microsaccades direction have not been carefully studied. In this project, two monkeys were trained to perform a spatial cue instructed saccade task, and the spatiotemporal dynamics of microsaccades direction were elaborately studied. We found that microsaccades direction rotated continuously from about 100 ms after spatial cue onset to fixation off, and the rotation speed was greater when monkeys chose against their intrinsic directional bias, which was well explained by a conceptual model associating rotations with transitions between different brain states. Further, the idiosyncrasies of microsaccades direction implicate previously unknown complexities of involvements of microsaccades in visuo-oculomotor processes. The main results have already been presented as a poster in the 6th FAONS Congress and 11th Biennial Conference of CNS last year and were also orally presented by myself in the 1st Beijing Vision Science Conference in 2016. The manuscript on this topic has received comments from Prof. Michael Goldberg (Columbia Univ.) and Prof. Ning Qian (Columbia Univ.) and is now in the last stage of preparation.

Another project I have been conducting is about neuronal mechanisms underlying automatic allocentric spatial encoding. It is well-accepted that neurons in various brain areas represent egocentric locations of objects, especially in parieto-frontal cortex. However, despite crucial roles of allocentric spatial coordinates in our daily life, there is a heated debate about whether there are “real” allocentric spatial representations in human brains. In 2015, an MRI study showed the existence of an automatic allocentric encoding signal in human right precuneus. To find out the neuronal mechanisms underlying this automatic allocentric encoding, I have been doing single neuron recordings in right precuneus of rhesus monkey to verify my neuronal model which explains how egocentric encoding neurons integrate to form allocentric representations.

I also take part in a visual psychophysics project about a retrospective Bayesian perceptual decoding model, in collaboration with Prof. Qian. Like encoding for sensory input, decoding for perception and memory retrieval is usually thought to be carried out with information representations processed in a sequence from low- to high- levels. However, an unpublished study by Prof. Ning Qian (Columbia Univ.) and Prof. Misha Tsodyks (Weizmann Inst.; Columbia Univ.) found a repulsion phenomenon of the relative orientation judgement of two lines presented sequentially, which could not be explained by a low-to-high decoding protocol but could be well fitted by a retrospective Bayesian perceptual decoding model which decoded from high- to lower- levels. I have been involved in an ongoing project in collaboration with Prof. Qian, the goal of which is trying to verify, through psychophysical experiments, a prediction of this theoretical model: the repulsion will increase as the memory of two lines gets worse. I have contributed remarkably to experimental designs and technical issues and have been closely following the experimental progress.

I am very interested in visual perception. Modern computers are tremendously outperformed by human brains in tasks involving complex visual information such as image segmentation, face recognition, etc, although people have been trying hard to make computers more intelligent. Then how to build computers intelligent enough for complicated image processing tasks? I think the answer lies in human brains: imagine that one day we know every detail about how the visual system of human brains works, we would then be able to build a highly efficient machine capable of visual processing.

I am also very interested in the area of Visual working/short-term memory (VWM) capacity. People have been arguing about whether VWM is discrete slots based or continuous resource based; however, giving that data structure plays a vital role in data processing for a computer program, it is reasonable to hypothesize that the structure of all information held in memory, rather than simple allocations based on discrete slots or continuous resource, matters most. To test this hypothesis, both neurophysiology and psychophysics studies on human beings and electrophysiology studies on non-human primates would be needed.

Ultimately, I hope to see the day when the mystery of human brains is uncovered and intelligent computers can be built. After introducing Turing Machine, Alan Mathison Turing asked the question whether a computing machinery was able to think as human beings. In the book "The Computer and the Brain", John von Neumann also asked this question. As a matter of fact, people in computer science have long been dreaming of and trying hard for the realization of intelligent machines, and some of them begin to look for the answer in human brains -- I am exactly one of them!

Publications & Abstracts

The spatiotemporal characteristics of microsaccade reflect the interaction between saccadic choice and intrinsic directional bias.
Bin Yang, Xiaofeng Xu, Jing Guang, Yang Zhou, Mingsha Zhang.
Manuscript under preparation. pdf

Rotated direction of microsaccade represents motor choice in the spatial choice tasks.
Bin Yang, Jing Guang, Yang Zhou, Mingsha Zhang.
Oral presentation in the 1st Beijing Vision Science Conference, 2016.

Rotation of microsaccades direction reflects spatial choice and individual bias.
Bin Yang, Jing Guang, Yang Zhou, Mingsha Zhang.
Poster in the 6th FAONS Congress and 11th Biennial Conference of CNS, 2015. pdf

Reversibility of general 1D linear cellular automata over the binary field Z_2 under null boundary conditions.
Bin Yang, Chao Wang, Aiyun Xiang.
Information Sciences (2014 IF: 4.038), 2015. (Citations: 2) pdf