Research Interest

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.

Ph.D. course

(Lab Manager)

Research Interest

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.

Research Interest

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.

Research Interest

Benefit of random behavior in individuals for system

Various systems from artificial to social systems are Multi-agent systems, system where autonomous entities interact while sharing common environment. In multi-agent systems, each agent might want to optimize their behavior to achieve their goals. What will happen if all agents are identical and wish the same goal? They might gather at the same place and system will paralyzed. Then what should we do to prevent this kind of tragedy? We think that randomness in individual behavior can solve the problem. From multi-agent simulation and ecological data, we will discover the power of randomness.

Research Interest

Neural network dynamics underlying natural and machine intelligence

The biological brain solves many complex tasks in its environment. Artificial neural networks, inspired by the brain, have established themselves as powerful tools for solving nonlinear problems today. However, the principles underlying neural dynamics are not yet fully understood. Through a computational neuroscience approach, we aim to uncover the neural dynamics underlying biological intelligence. Simultaneously, we seek to achieve a breakthrough in engineering mechanical intelligence, drawing insights from natural intelligence.

Research Interest

Neural basis of reversal learning

An ability to flexibly change behavior according to changing environment is crucial for animals’ survival. Reversal learning paradigm tests this ability by tasking subjects to make decisions based on their evaluation on the changing environment. My research proposes the underlying synaptic mechanism of reversal learning behavior and model behavior with a reinforcement learning algorithm.

Research Interest

Functional advantage of innate early visual circuitry

In mammalian brains, the function or structure of neural circuits shows experience-dependent plasticity, constantly adapting to dynamic environments. On the other hand, the early visual circuitry remains largely unchanged despite lifelong visual experiences. Yet, the specific functional advantage of the fixed early visual circuitry remains elusive. In the research, we investigated the functional benefit of the fixed early layer under dynamic environments utilizing a visual deep neural network (DNN) as a model for the human visual system.

Research Interest

Continual learning of humans and machine intelligence

Throughout life, humans encounter a vast array of sensory experiences. Some of these experiences become ingrained in our memory, playing a key role in how we interpret new stimuli, while others gradually fade away—a concept known as continual learning. My primary research interest lies in understanding the neural computations that underlie continual learning and leveraging these insights to construct AI systems capable of continual learning. In particular, I aim to address questions such as: How does the brain encode sensory stimuli as knowledge? How does it adaptively employ prior knowledge to interpret novel stimuli? And finally, how can we replicate these features in AI?