Biology subjects

Igarashi, J.

Publications and source records attributed to Igarashi, J..

3 recordsLinked to original sources

bioRxiv2026

Recurrent Spiking Neural Networks for Continuous Motor Control: A Biologically Plausible Model of Motor Cortex

Motor cortex acquires skilled movements through recurrent spiking dynamics and experience-dependent synaptic plasticity, yet how such circuits learn continuous motor control using biologically local signals remains unclear. Here, we develop a recurrent spiking neural network model of motor cortex that addresses both the computational and biological requirements of motor learning. We first analytically derived the recurrent firing-rate state directly from synaptic input and trained the network by implicit differentiation, thereby avoiding backpropagation through long spike trajectories. We then introduced State-Carry Initialization, which enables closed-loop motor control by continuously carrying the ongoing recurrent state across successive actions. Building on this formulation, we devised a learning rule termed Feedback-Spike Nudging (FSN), in which teaching signals are delivered as spikes through a feedback pathway. Synaptic changes depend only on presynaptic activity and the feedback-evoked change in postsynaptic firing, consistent with biological mechanisms of synaptic plasticity in motor cortex. The resulting networks learned continuous motor control through both supervised and reinforcement learning, which could be mapped respectively onto interactions of motor cortex with the cerebellum and basal ganglia. Moreover, the FSN learning rule was consistent with experimental observations of structural spine plasticity in mouse motor cortex. Thus, our study lays a foundation for recurrent spiking models of motor cortex that are biologically grounded at both the functional and anatomical levels.

neuroscience↗

bioRxiv2024

Synergistic reinforcement learning by cooperation of the cerebellum and basal ganglia

The cerebral cortex, cerebellum and basal ganglia are essential for flexible learning in mammals. Although traditionally thought to operate under different learning rules, recent evidence suggests that both the basal ganglia and the cerebellum may employ reinforcement learning mechanisms. This raises the question of how these structures coordinate when a common reward prediction error mechanism is active. To address this issue, we first examined output signals from the basal ganglia and cerebellum following the activity of the cerebral cortex. We recorded single-neuron activity from the output regions of the cerebellum and basal ganglia - the cerebellar nuclei (CN) and substantia nigra pars reticulata (SNr) - in both male and female ChR2 transgenic rats. Neurons in the CN and SNr exhibited distinct temporal response patterns; notably, the fast excitatory response in the CN, driven by mossy fiber input, was synchronized with the inhibitory response in the SNr, mediated via the direct pathway. Using these experimental findings together with connectome data, we developed both a semi-realistic spiking network model and a reservoir-based reinforcement learning model. In the latter model, successful learning depended on synaptic plasticity in both the cerebellum and basal ganglia with a temporal precision on the order of 10 ms. Furthermore, cortical {beta}-oscillations enhanced learning and optimal reinforcement learning occurred when the output of cerebellar and basal ganglia signal phase-locked at the frequency of cortical oscillation. Taken together, our results suggest that the coordinated output of the cerebellum and basal ganglia, driven by tightly tuned cortical input, underlies brain-wide synergistic reinforcement learning. Significance StatementThe cerebral cortex, cerebellum, and basal ganglia support learning. Recent research suggests that both the basal ganglia and cerebellum use a similar learning process called reinforcement learning, which involves predicting rewards. To understand how these brain regions work together, we recorded brain activity in rats while photo-stimulating the cerebral cortex. We found that two types of responses in the cerebellum and basal ganglia were synchronized, which might help activate the cerebral cortex. A computer model showed that precise timing of signals from both the cerebellum and basal ganglia is important for learning. This timing was important only when the cerebral cortex worked in a specific frequency range. Our findings suggest that coordinated brain activity enhances learning.

neuroscience↗

bioRxiv2022

Quantitative measures of topographic and divergent/convergent connectivity in diffusion MRI of the human cerebral cortex

Spatial features of connections, such as topography and divergence/convergence, reflect the information-processing mechanisms crucial for understanding and modeling the brain. However, this has not yet been comprehensively investigated. Using diffusion Magnetic Resonance Imaging (dMRI) data, we developed a topographic factor (TF) and divergence/convergence factor (DC) to quantitatively explore the spatial connectivity patterns of one entire hemisphere of the human cerebral cortex. In the analysis, the early sensory areas, which are located far away from the center of the cerebral cortex, exhibited high topographic connectivity. In contrast, the limbic system, which is located proximal to the center, showed high divergence/convergence in two types of connectivity: one region to another region at the region-to-region level, and one region to all other regions at the global level. Topography had anti-correlation with divergence/convergence over the cerebral cortex, and the two types of divergence/convergence displayed positive correlation with each other. These results suggest that topographic and divergent/convergent connectivity are spatially organized with respect to cytoarchitecture over the cerebral cortex to optimize energy efficiency and information transfer performance in the cerebral cortex.

neuroscience↗