Continuous Attractor Neural Networks
Continuous Attractor Neural Networks (CANNs) are models aiming to explain and describe dynamics of neural systems able to support continuous attractors. I have jointly studied CANNs on its dynamics and implications, since 2006. Here I will give a brief introduction to those studies.
Analysis on Experimental Data
Data analysis plays an important role in scientific discovery. After joining Tomoki Fukai's Lab, I have engaged in data analysis in different experiments. Here I will briefly describe my progress so far.
Spiking-neuron Model for Slow Oscillations
Slow oscillations are believed to be important for memory consolidation. We have recently proposed intra-network conditions for its occurrence. Also, based on the modeling study, we found a relation between neural ensemble variability and oscillatory cycles, which may help us to clarify the role and function of the phenomenon.