When sensory system receives an ambiguous signal, human perception often switches between two or more possible interpretations. This perceptual phenomenon is called multistable perception and considered important since this phenomenon links the relationship between the stimulus and sensation of the brain. This perceptional switch is considered important to understand brain functions thus extensively studied, however the complete mechanism of multistable perception is still elusive. To investigate the various feature of multistable perception, we perform psychophysics experiment and simulate the computational model to explore the mechanism. This study will provide an insight into how brain makes a perceptual interpretation based on the external stimulus.
Memory System simulation
Factors (e.g. Excitability, Oscillation) that affects memory; Efficient storage of information in memory system.
Computational Data Analysis Analysis & Simulation with experimental data; Neural activity pattern analysis.
Integrated Ph.D course
visual system modeling, computational neuroscience, simple/complex cells
Woo Yeon Shin
Integrated Ph.D course
Neuromodulatory properties of Mesencephalic locomotion region during decision-making in mice
At different internal state, animals adjust their walking speed and their behavior decisions accordingly. Mesencephalic locomotor region(MLR), a brain region which initiates and controls locomotion, also sends ascending projections to modulate brain state. My research aims to reveal the interaction between locomotion speed controlling neurons and neuromodulatory signals in MLR and explain its behavior role during perceptual decision-making. To investigate this, we developed a task that mice to perform perceptual decision-making on a head-fixed treadmill during an electrophysiological recording with a multi-channel recording of MLR. (Collaborate with KIST 김정진박사님 연구실)
Long-range horizontal connections are distinctive connectivity structure observed in V1 layer 2/3 in higher mammals. However, why visual cortex requires such connections, what is the functional role of them remains elusive. Here, we investigated visual functions of the structure by simulating model network.
Group-level benefit of unconditional imitation
Human, as well as other intelligent animals, are capable of imitating other subjects. The imitating behavior is often unconditional, which means that the subject behavior of imitation does not necessarily have to be profitable to the imitator. Yet, the fact that the unconditional imitation is observed in these animals suggests that it has some benefits to it. In this research, the unconditional imitation is thought to benefit a whole group or society of imitators, for instance, by increasing the overall lifespan or efficiency of the group. The underlying mechanism of this effect is studied using a multi-agent simulation.
Perception of visual symmetry
While symmetry has had a special status in vision research for more than a hundred years, the neural basis of symmetry perception is still unknown. My research interest is in finding the neural mechanism of symmetry perception and its effect on object recognition.
Visual perception of magnitude
A given visual scene may contain several types of magnitude information, such as the number of objects, their overall size and density. Despite what their apparent qualitative differences might suggest, experimental evidence points to the possibility that these distinct categories of magnitude are represented by a shared neural architecture and that a neuron might be tuned to a particular combination of magnitudes across the said categories. Using Convolutional Neural Network (CNN) model as a structural and functional abstraction of the real brain for visual processing, I'm investigating candidate computational mechanisms underlying these phenomena in a hope to elucidate how the brain might have evolved to code distinct, but relevant, information efficiently.
YangBoonSoon Building (E16-1) #408,
Department of Brain and Cognitive Sciences, Department of Bio and Brain Engineering, KAIST,
291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
Tel: +82-42-350-6513,6573, Fax: +82-42-350-6510