Yoshimasa Kubo

Biologically Inspired Neural Networks
Development of neural network models inspired by brain mechanisms, including dendritic computation, recurrent dynamics, and sleep-related learning processes. Focus on designing learning algorithms that are both effective and biologically plausible.

Equilibrium Propagation (EP) and Alternatives to Backpropagation
Advancing energy-based learning frameworks such as Equilibrium Propagation and holomorphic EP. Research includes recurrent EP models, stability improvements, and integrating EP into supervised, sequential, and reinforcement learning settings.

Continual Learning and Sleep Replay Consolidation (SRC)
Designing sleep-inspired mechanisms—such as replay consolidation and awake rehearsal—to mitigate catastrophic forgetting. Creating biologically grounded continual learning algorithms that remain stable over long task sequences.

Recurrent Neural Networks and Dynamical Models
Developing biologically plausible recurrent neural architectures (MRNN-EP, Wave-RNN, dendritic RNNs) for sequential data, time series, and sensory processing tasks. Investigating recurrent dynamics and activity patterns that resemble cortical computation.

Computational Neuroscience of Learning and Memory
Studying neural mechanisms of memory consolidation, replay, and cortical plasticity. Using computational models to explore how biological systems stabilize learning across changing environments.

Reinforcement Learning with Biologically Plausible Updates
Integrating EP-based learning into actor–critic frameworks and reinforcement learning environments, bridging the gap between biological plausibility and practical control tasks.