Shigeyoshi Fujisawa

The paper introduces a spiking neural network model that learns grid and place cell patterns through biologically realistic dynamics, reproducing key temporal coding features observed in the hippocampal-entorhinal circuit.

The paper investigates how time cells in the hippocampus, striatum, and orbitofrontal cortex process temporal information through synchronized activity and theta oscillations during a temporal bisection task.

The paper investigates how VTA dopamine and GABA neurons encode short-term memory during a T-maze task in mice, finding that firing rate differences are linked to memory-dependent decisions.

This paper estimates animal position from grid cell activity using persistent cohomology, showing it outperforms traditional methods in high nonlinearity scenarios.

This study identifies hippocampal neuron subpopulations that encode delay and value information during delay-discounting decisions in mice.