Yuri Imaizumi

The paper shows that fast-spiking neurons in monkey orbitofrontal cortex compute economic value more strongly than regular-spiking neurons due to their wider dynamic range.

The paper shows that visual information propagates as a heterogeneous mixture of stochastic neural population signals across brain-wide regions in monkeys.

This study compares neural population dynamics for continuous and categorical task parameters using state-space analysis and optimal-stimulus paradigms in visual recognition tasks.

This study shows that single-neuron activity in monkeys' reward circuitry reflects prospect theory parameters for risky gambles, with distributed coding across brain regions.

This paper combines prospect theory and reinforcement learning to model gambling behavior in monkeys, finding that probability weighting changes dynamically with reward prediction error.

The paper investigates neural dynamics in reward-related brain regions during expected value computation, finding distinct temporal patterns in orbitofrontal cortex and ventral striatum.