Hananel Hazan

This paper introduces evidence-gated plasticity (EGP) for multi-goal learning in spiking neural networks, showing improved reward and target selectivity with target-context EGP.

The paper presents a computational framework to identify interventions that compensate for ion channel mutations by modulating other conductances, demonstrating feasibility through simulations and a differentiable Hodgkin-Huxley model.

The paper introduces a platform called the Cell Trainer for automated training of mammalian cells using timed drug pulses and fluorescence imaging, demonstrating sensitization-like dynamics and feedback control in single-cell responses.

The paper introduces a spiking neuronal network model that uses spatial representations, value-guided plasticity, and contextual modulation to enable flexible navigation under sparse reward conditions.

This paper identifies key proteins in CLL using statistical thermodynamics and systems biology, advocating for multi-target inhibition strategies to improve therapy design.

This paper trains spiking neuronal network models to play a virtual racket-ball game using reinforcement learning and evaluates different circuit architectures and learning rules.

This study compares spike-timing-dependent reinforcement learning and evolutionary strategy for training spiking neuronal networks to solve a reinforcement learning control task.

The paper experimentally validates the Hodgkin-Huxley phase diagram using dynamic clamp and Kv1.3 channels in Xenopus oocytes, revealing activity-dependent and hysteretic dynamics.