Yoshikazu Isomura

This paper develops a biologically plausible recurrent spiking neural network model of motor cortex that learns continuous motor control through synaptic plasticity and feedback mechanisms.

The paper investigates how thalamocortical pathways from the basal ganglia and cerebellum route credit signals to cortical circuits, revealing anatomical segregation and plasticity mechanisms that enhance learning.

This study investigates how the fronto-parietal network switches between independent and coherent temporal coding modes during timing tasks in mice.

The paper identifies a core area in the parietal cortex for ground-tilt representation and shows tilt information is encoded in a condition-invariant low-dimensional manifold across the dorsal cortex.

This study shows that hippocampal sharp-wave ripples are suppressed by reward delivery and minor movements in head-fixed rats, indicating motor engagement inhibits ripples regardless of behavioral state.

This study shows that hippocampal sharp-wave ripples (SWRs) decrease during physical actions and consummatory behavior in immobile rats, challenging the idea that SWRs are specific to consummation.

This paper investigates how the cerebellum and basal ganglia coordinate reinforcement learning through synchronized neural responses and cortical oscillations in rats.

The paper investigates neural activity patterns in the superior colliculus (SC) and finds that neurons in the deep layers of the anterolateral SC dynamically integrate cortical and local inputs, while others simply relay activity.

The paper shows that cerebellar climbing fibers encode multiple behavioral variables and are organized into functional modules in mice.

The paper compares the timing of action and reward event representation in hippocampal CA1 and lateral entorhinal cortex layers during operant learning in rats.

The paper investigates how dopamine systems in the basal ganglia process reward prediction based on working memory versus random rewards in rats and mice.

This paper introduces Multi-Linc analysis, an automated method for parallel spike collision tests to identify neuronal projections, demonstrating its application in rat cortical neurons.

The paper shows inhibitory neurons have central positions and stronger control in cortical microconnectomes, maintaining brain activity balance.